Sync from GitHub via hub-sync

.dockerignore

@@ -0,0 +1,13 @@
+.git
+.pytest_cache
+**/__pycache__
+**/*.pyc
+backend/.venv
+backend/venv
+backend/last_trained_model_checkpoint.pth
+backend/artifacts/*
+!backend/artifacts/.gitkeep
+frontend/node_modules
+frontend/build
+node_modules
+

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+LEGACY/Machine[[:space:]]Learning-AOL.pdf filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -0,0 +1,27 @@
+FROM node:20-slim AS frontend-build
+
+WORKDIR /app/frontend
+COPY frontend/package*.json ./
+RUN npm ci
+COPY frontend/ ./
+RUN npm run build
+
+FROM python:3.11-slim AS runtime
+
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+ENV PORT=7860
+ENV QUOTIFY_ENABLE_CROSS_ENCODER=false
+
+WORKDIR /app/backend
+
+COPY backend/requirements.txt ./
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY backend/ ./
+COPY --from=frontend-build /app/frontend/build /app/frontend/build
+
+EXPOSE 7860
+
+CMD ["gunicorn", "--bind", "0.0.0.0:7860", "--workers", "1", "--threads", "4", "--timeout", "240", "app:app"]
+

LEGACY/Machine Learning-AOL.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7b0c988426bd58f3efb94b5527d9e12361cf5b4b510392eff3cdc24d9aa0944d
+size 2041143

LEGACY/NaiveBayes_ML_AOL_3_0.ipynb

@@ -0,0 +1,1483 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9BJOHx4uLmQM"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "from statistics import mode, multimode\n",
+        "import string\n",
+        "\n",
+        "from nltk.corpus import stopwords, wordnet\n",
+        "from nltk.tokenize import word_tokenize\n",
+        "from nltk.tag import pos_tag\n",
+        "from nltk.stem import SnowballStemmer, WordNetLemmatizer\n",
+        "from nltk.probability import FreqDist\n",
+        "from nltk.classify import NaiveBayesClassifier, accuracy\n",
+        "\n",
+        "from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score\n",
+        "import random\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt\n",
+        "import json"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G7E9ljABLmQO"
+      },
+      "source": [
+        "## Load the Preprocessed Data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Mhw00AHpLmQR",
+        "outputId": "5db382de-17e8-4632-a1c0-6078ab566ebd"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Comment</th>\n",
+              "      <th>Emotion</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>i seriously hate one subject to death but now ...</td>\n",
+              "      <td>fear</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>im so full of life i feel appalled</td>\n",
+              "      <td>anger</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>i sit here to write i start to dig out my feel...</td>\n",
+              "      <td>fear</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>ive been really angry with r and i feel like a...</td>\n",
+              "      <td>joy</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>i feel suspicious if there is no one outside l...</td>\n",
+              "      <td>fear</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5929</th>\n",
+              "      <td>i begun to feel distressed for you</td>\n",
+              "      <td>fear</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5930</th>\n",
+              "      <td>i left feeling annoyed and angry thinking that...</td>\n",
+              "      <td>anger</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5931</th>\n",
+              "      <td>i were to ever get married i d have everything...</td>\n",
+              "      <td>joy</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5932</th>\n",
+              "      <td>i feel reluctant in applying there because i w...</td>\n",
+              "      <td>fear</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5933</th>\n",
+              "      <td>i just wanted to apologize to you because i fe...</td>\n",
+              "      <td>anger</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5934 rows × 2 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                                                Comment Emotion\n",
+              "0     i seriously hate one subject to death but now ...    fear\n",
+              "1                    im so full of life i feel appalled   anger\n",
+              "2     i sit here to write i start to dig out my feel...    fear\n",
+              "3     ive been really angry with r and i feel like a...     joy\n",
+              "4     i feel suspicious if there is no one outside l...    fear\n",
+              "...                                                 ...     ...\n",
+              "5929                 i begun to feel distressed for you    fear\n",
+              "5930  i left feeling annoyed and angry thinking that...   anger\n",
+              "5931  i were to ever get married i d have everything...     joy\n",
+              "5932  i feel reluctant in applying there because i w...    fear\n",
+              "5933  i just wanted to apologize to you because i fe...   anger\n",
+              "\n",
+              "[5934 rows x 2 columns]"
+            ]
+          },
+          "execution_count": 29,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "dataset = pd.read_csv('../Dataset/(Preprocessed)Emotion_classify_Data(Labeled).csv')\n",
+        "dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5yZznhfqLmQT"
+      },
+      "outputs": [],
+      "source": [
+        "from thePreprocessingII import preProcess\n",
+        "\n",
+        "stopsList = stopwords.words('english')\n",
+        "punctList = string.punctuation\n",
+        "stemmer = SnowballStemmer('english')\n",
+        "lemmatizer = WordNetLemmatizer()\n",
+        "\n",
+        "def getLabel(tag):\n",
+        "    if tag == 'jj': return 'a'\n",
+        "    elif tag in ['vb', 'nn', 'rb']: return tag[0]\n",
+        "    else: return None\n",
+        "\n",
+        "def preProcess(wordList):\n",
+        "    wordList = [w for w in wordList if w not in stopsList]\n",
+        "    wordList = [w for w in wordList if w not in punctList]\n",
+        "    wordList = [w for w in wordList if w.isalpha()]\n",
+        "    wordList = [stemmer.stem(w) for w in wordList]\n",
+        "\n",
+        "    lemmaList = []\n",
+        "    for word, tag in pos_tag(wordList):\n",
+        "        label = getLabel(tag)\n",
+        "        if label != None: lemmaList.append(lemmatizer.lemmatize(word, label))\n",
+        "        else: lemmaList.append(lemmatizer.lemmatize(word))\n",
+        "\n",
+        "    return lemmaList"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "etBgq9hWLmQU"
+      },
+      "outputs": [],
+      "source": [
+        "commentList = dataset['Comment'].to_list()\n",
+        "emotionList = dataset['Emotion'].to_list()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "WjH0Qj3mLmQV",
+        "outputId": "f26ac20d-c925-48ce-c66d-9c891cb25e93"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['serious',\n",
+              " 'hate',\n",
+              " 'one',\n",
+              " 'subject',\n",
+              " 'death',\n",
+              " 'feel',\n",
+              " 'reluct',\n",
+              " 'drop',\n",
+              " 'im',\n",
+              " 'full',\n",
+              " 'life',\n",
+              " 'feel',\n",
+              " 'appal',\n",
+              " 'sit',\n",
+              " 'write',\n",
+              " 'start',\n",
+              " 'dig',\n",
+              " 'feel',\n",
+              " 'think',\n",
+              " 'afraid',\n",
+              " 'accept',\n",
+              " 'possibl',\n",
+              " 'might',\n",
+              " 'make',\n",
+              " 'ive',\n",
+              " 'realli',\n",
+              " 'angri',\n",
+              " 'r',\n",
+              " 'feel',\n",
+              " 'like',\n",
+              " 'idiot',\n",
+              " 'trust',\n",
+              " 'first',\n",
+              " 'place',\n",
+              " 'feel',\n",
+              " 'suspici',\n",
+              " 'one',\n",
+              " 'outsid',\n",
+              " 'like',\n",
+              " 'raptur',\n",
+              " 'happen',\n",
+              " 'someth',\n",
+              " 'feel',\n",
+              " 'jealous',\n",
+              " 'becasu',\n",
+              " 'want',\n",
+              " 'kind',\n",
+              " 'love',\n",
+              " 'true',\n",
+              " 'connect',\n",
+              " 'two',\n",
+              " 'soul',\n",
+              " 'want',\n",
+              " 'friend',\n",
+              " 'mine',\n",
+              " 'keep',\n",
+              " 'tell',\n",
+              " 'morbid',\n",
+              " 'thing',\n",
+              " 'happen',\n",
+              " 'dog',\n",
+              " 'final',\n",
+              " 'fell',\n",
+              " 'asleep',\n",
+              " 'feel',\n",
+              " 'angri',\n",
+              " 'useless',\n",
+              " 'still',\n",
+              " 'full',\n",
+              " 'anxieti',\n",
+              " 'feel',\n",
+              " 'bit',\n",
+              " 'annoy',\n",
+              " 'antsi',\n",
+              " 'good',\n",
+              " 'way',\n",
+              " 'feel',\n",
+              " 'like',\n",
+              " 'regain',\n",
+              " 'anoth',\n",
+              " 'vital',\n",
+              " 'part',\n",
+              " 'life',\n",
+              " 'live',\n",
+              " 'feel',\n",
+              " 'bit',\n",
+              " 'like',\n",
+              " 'franz',\n",
+              " 'liebkind',\n",
+              " 'produc',\n",
+              " 'mani',\n",
+              " 'peopl',\n",
+              " 'know',\n",
+              " 'fuhrer',\n",
+              " 'terrif',\n",
+              " 'dancer',\n",
+              " 'feel',\n",
+              " 'start',\n",
+              " 'didnt',\n",
+              " 'want',\n",
+              " 'move',\n",
+              " 'much',\n",
+              " 'realli',\n",
+              " 'glad',\n",
+              " 'experi',\n",
+              " 'glimps',\n",
+              " 'sort',\n",
+              " 'vibrant',\n",
+              " 'energi',\n",
+              " 'gain',\n",
+              " 'year',\n",
+              " 'bitten',\n",
+              " 'dog',\n",
+              " 'alway',\n",
+              " 'find',\n",
+              " 'feel',\n",
+              " 'thank',\n",
+              " 'year',\n",
+              " 'gather',\n",
+              " 'trick',\n",
+              " 'allow',\n",
+              " 'feel',\n",
+              " 'grate',\n",
+              " 'face',\n",
+              " 'moment',\n",
+              " 'last',\n",
+              " 'thing',\n",
+              " 'want',\n",
+              " 'say',\n",
+              " 'thank',\n",
+              " 'tri',\n",
+              " 'talk',\n",
+              " 'anyon',\n",
+              " 'feel',\n",
+              " 'irrit',\n",
+              " 'like',\n",
+              " 'feel',\n",
+              " 'like',\n",
+              " 'hate',\n",
+              " 'sinc',\n",
+              " 'dont',\n",
+              " 'know',\n",
+              " 'wrote',\n",
+              " 'follow',\n",
+              " 'littl',\n",
+              " 'note',\n",
+              " 'feel',\n",
+              " 'today',\n",
+              " 'u',\n",
+              " 'r',\n",
+              " 'offend',\n",
+              " 'follow',\n",
+              " 'post',\n",
+              " 'obvious',\n",
+              " 'live',\n",
+              " 'long',\n",
+              " 'enough',\n",
+              " 'compromis',\n",
+              " 'act',\n",
+              " 'believ',\n",
+              " 'feel',\n",
+              " 'offend',\n",
+              " 'choos',\n",
+              " 'tell',\n",
+              " 'guy',\n",
+              " 'feel',\n",
+              " 'treat',\n",
+              " 'guy',\n",
+              " 'friend',\n",
+              " 'would',\n",
+              " 'want',\n",
+              " 'put',\n",
+              " 'stop',\n",
+              " 'nonsens',\n",
+              " 'rememb',\n",
+              " 'feel',\n",
+              " 'belong',\n",
+              " 'smart',\n",
+              " 'enough',\n",
+              " 'cool',\n",
+              " 'enough',\n",
+              " 'even',\n",
+              " 'young',\n",
+              " 'enough',\n",
+              " 'feel',\n",
+              " 'like',\n",
+              " 'redeem',\n",
+              " 'even',\n",
+              " 'though',\n",
+              " 'think',\n",
+              " 'realiz',\n",
+              " 'distraught',\n",
+              " 'ok',\n",
+              " 'hope',\n",
+              " 'next',\n",
+              " 'quot',\n",
+              " 'abl',\n",
+              " 'let',\n",
+              " 'special',\n",
+              " 'someon',\n",
+              " 'know',\n",
+              " 'im',\n",
+              " 'feel',\n",
+              " 'insecur',\n",
+              " 'understand',\n",
+              " 'matter',\n",
+              " 'much',\n",
+              " 'trust',\n",
+              " 'feel',\n",
+              " 'much',\n",
+              " 'better',\n",
+              " 'without',\n",
+              " 'help',\n",
+              " 'ice',\n",
+              " 'sit',\n",
+              " 'type',\n",
+              " 'wonder',\n",
+              " 'belong',\n",
+              " 'feel',\n",
+              " 'distract',\n",
+              " 'feel',\n",
+              " 'comfort',\n",
+              " 'feel',\n",
+              " 'misunderstood',\n",
+              " 'hurt',\n",
+              " 'still',\n",
+              " 'feel',\n",
+              " 'littl',\n",
+              " 'shaki',\n",
+              " 'time',\n",
+              " 'move',\n",
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+              " 'odd',\n",
+              " 'jade',\n",
+              " 'hair',\n",
+              " 'particular',\n",
+              " 'seem',\n",
+              " 'prone',\n",
+              " 'general',\n",
+              " 'work',\n",
+              " 'well',\n",
+              " 'spencer',\n",
+              " 'write',\n",
+              " 'strop',\n",
+              " 'bit',\n",
+              " 'feel',\n",
+              " 'grumpi',\n",
+              " 'miss',\n",
+              " 'feel',\n",
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+              " 'get',\n",
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+              " 'result',\n",
+              " 'field',\n",
+              " 'academ',\n",
+              " 'athlet',\n",
+              " 'got',\n",
+              " 'feel',\n",
+              " 'brig',\n",
+              " 'sincer',\n",
+              " 'strong',\n",
+              " 'desir',\n",
+              " 'help',\n",
+              " 'other',\n",
+              " 'becom',\n",
+              " 'success',\n",
+              " 'financi',\n",
+              " 'also',\n",
+              " 'build',\n",
+              " 'strengthen',\n",
+              " 'relationship',\n",
+              " 'christian',\n",
+              " 'im',\n",
+              " 'feel',\n",
+              " 'deepli',\n",
+              " 'overwhelm',\n",
+              " 'ordinari',\n",
+              " 'task',\n",
+              " 'feel',\n",
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+              " 'sick',\n",
+              " 'feel',\n",
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+              " 'midnight',\n",
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+              " 'nervous',\n",
+              " 'made',\n",
+              " 'way',\n",
+              " 'event',\n",
+              " 'venu',\n",
+              " 'realli',\n",
+              " 'worri',\n",
+              " 'would',\n",
+              " 'feel',\n",
+              " 'intimid',\n",
+              " 'monica',\n",
+              " 'met',\n",
+              " 'morn',\n",
+              " 'incred',\n",
+              " 'welcom',\n",
+              " 'made',\n",
+              " 'feel',\n",
+              " 'relax',\n",
+              " 'straight',\n",
+              " 'away',\n",
+              " 'sick',\n",
+              " 'cold',\n",
+              " 'amp',\n",
+              " 'feel',\n",
+              " 'well',\n",
+              " 'wonder',\n",
+              " 'would',\n",
+              " 'even',\n",
+              " 'abl',\n",
+              " 'patienc',\n",
+              " 'go',\n",
+              " 'whitley',\n",
+              " 'month',\n",
+              " 'photo',\n",
+              " 'shoot',\n",
+              " 'feel',\n",
+              " 'like',\n",
+              " 'bit',\n",
+              " 'obnoxi',\n",
+              " 'pictur',\n",
+              " 'post',\n",
+              " 'feel',\n",
+              " 'moral',\n",
+              " 'outrag',\n",
+              " 'furious',\n",
+              " 'often',\n",
+              " 'like',\n",
+              " 'feel',\n",
+              " 'piss',\n",
+              " 'friend',\n",
+              " 'didnt',\n",
+              " 'offer',\n",
+              " 'soda',\n",
+              " 'feel',\n",
+              " 'heart',\n",
+              " 'tread',\n",
+              " 'danger',\n",
+              " 'territori',\n",
+              " 'feel',\n",
+              " 'frighten',\n",
+              " 'know',\n",
+              " 'pleasant',\n",
+              " 'feel',\n",
+              " 'selfish',\n",
+              " 'say',\n",
+              " 'think',\n",
+              " 'would',\n",
+              " 'fallen',\n",
+              " 'apart',\n",
+              " 'feel',\n",
+              " 'shaft',\n",
+              " 'greedi',\n",
+              " 'crappi',\n",
+              " 'week',\n",
+              " 'still',\n",
+              " 'feel',\n",
+              " 'agit',\n",
+              " 'like',\n",
+              " 'day',\n",
+              " 'want',\n",
+              " 'feel',\n",
+              " 'rebelli',\n",
+              " 'particular',\n",
+              " 'like',\n",
+              " 'watch',\n",
+              " 'romcom',\n",
+              " 'get',\n",
+              " 'feel',\n",
+              " 'may',\n",
+              " 'pretti',\n",
+              " 'good',\n",
+              " 'write',\n",
+              " 'feel',\n",
+              " 'reluct',\n",
+              " 'leav',\n",
+              " 'alon',\n",
+              " 'like',\n",
+              " 'without',\n",
+              " 'help',\n",
+              " 'enough',\n",
+              " 'repay',\n",
+              " 'good',\n",
+              " 'asham',\n",
+              " 'feel',\n",
+              " 'like',\n",
+              " 'moment',\n",
+              " 'see',\n",
+              " 'terrifi',\n",
+              " 'cri',\n",
+              " 'child',\n",
+              " 'dead',\n",
+              " 'one',\n",
+              " 'wake',\n",
+              " 'morn',\n",
+              " 'voic',\n",
+              " 'throat',\n",
+              " 'feel',\n",
+              " 'ok',\n",
+              " 'afternoon',\n",
+              " 'scratchi',\n",
+              " 'sound',\n",
+              " 'like',\n",
+              " 'marg',\n",
+              " 'simpson',\n",
+              " 'night',\n",
+              " 'bad',\n",
+              " 'throat',\n",
+              " 'sore',\n",
+              " 'whisper',\n",
+              " 'ive',\n",
+              " 'alway',\n",
+              " 'nervous',\n",
+              " 'someth',\n",
+              " 'like',\n",
+              " 'feel',\n",
+              " 'like',\n",
+              " 'realli',\n",
+              " 'talent',\n",
+              " 'enter',\n",
+              " 'someth',\n",
+              " 'offici',\n",
+              " 'contest',\n",
+              " 'feel',\n",
+              " 'bit',\n",
+              " 'piss',\n",
+              " 'went',\n",
+              " 'first',\n",
+              " 'feel',\n",
+              " 'satisfi',\n",
+              " 'know',\n",
+              " 'dirt',\n",
+              " 'hair',\n",
+              " 'longer',\n",
+              " 'car',\n",
+              " 'hous',\n",
+              " 'realli',\n",
+              " 'like',\n",
+              " 'special',\n",
+              " 'edit',\n",
+              " 'realli',\n",
+              " 'feel',\n",
+              " 'special',\n",
+              " 'song',\n",
+              " 'would',\n",
+              " 'accept',\n",
+              " 'gift',\n",
+              " 'without',\n",
+              " 'feel',\n",
+              " 'mad',\n",
+              " 'feel',\n",
+              " 'hesit',\n",
+              " 'sinc',\n",
+              " 'experi',\n",
+              " 'program',\n",
+              " 'feel',\n",
+              " 'particular',\n",
+              " 'bitchi',\n",
+              " 'dont',\n",
+              " 'think',\n",
+              " 'adequ',\n",
+              " 'express',\n",
+              " 'appreci',\n",
+              " 'feel',\n",
+              " 'confus',\n",
+              " 'im',\n",
+              " 'professor',\n",
+              " 'look',\n",
+              " 'like',\n",
+              " 'spitbal',\n",
+              " 'friend',\n",
+              " 'die',\n",
+              " 'cri',\n",
+              " 'pillow',\n",
+              " 'night',\n",
+              " 'feel',\n",
+              " 'danger',\n",
+              " 'tickl',\n",
+              " 'jealousi',\n",
+              " 'lure',\n",
+              " 'lair',\n",
+              " 'feel',\n",
+              " 'rush',\n",
+              " 'finish',\n",
+              " 'million',\n",
+              " 'thing',\n",
+              " 'abl',\n",
+              " 'focus',\n",
+              " 'task',\n",
+              " 'separ',\n",
+              " 'feel',\n",
+              " 'tortur',\n",
+              " 'dunno',\n",
+              " 'feel',\n",
+              " 'complet',\n",
+              " 'happi',\n",
+              " 'real',\n",
+              " 'world',\n",
+              " 'taught',\n",
+              " 'struggl',\n",
+              " 'go',\n",
+              " 'thru',\n",
+              " 'noth',\n",
+              " 'close',\n",
+              " 'struggl',\n",
+              " 'feel',\n",
+              " 'like',\n",
+              " 'taken',\n",
+              " 'role',\n",
+              " 'grandmoth',\n",
+              " 'sinc',\n",
+              " 'belov',\n",
+              " 'grandma',\n",
+              " 'longer',\n",
+              " 'fear',\n",
+              " 'peopl',\n",
+              " 'ask',\n",
+              " 'feel',\n",
+              " 'reluct',\n",
+              " 'talk',\n",
+              " 'thing',\n",
+              " 'would',\n",
+              " 'smile',\n",
+              " 'except',\n",
+              " 'start',\n",
+              " 'feel',\n",
+              " 'like',\n",
+              " 'uptight',\n",
+              " 'comment',\n",
+              " 'jaw',\n",
+              " 'would',\n",
+              " 'fall',\n",
+              " 'right',\n",
+              " 'head',\n",
+              " 'feel',\n",
+              " 'comfort',\n",
+              " 'around',\n",
+              " 'feel',\n",
+              " 'complet',\n",
+              " 'unsur',\n",
+              " 'boundari',\n",
+              " 'normalci',\n",
+              " 'think',\n",
+              " 'feel',\n",
+              " 'gratitud',\n",
+              " 'think',\n",
+              " 'kind',\n",
+              " 'gracious',\n",
+              " 'heaven',\n",
+              " 'father',\n",
+              " 'im',\n",
+              " 'feel',\n",
+              " 'realli',\n",
+              " 'realli',\n",
+              " 'left',\n",
+              " 'somewhat',\n",
+              " 'dissatisfi',\n",
+              " 'everyth',\n",
+              " 'simpli',\n",
+              " 'said',\n",
+              " 'sorri',\n",
+              " 'got',\n",
+              " 'car',\n",
+              " 'got',\n",
+              " 'hous',\n",
+              " 'feel',\n",
+              " 'restless',\n",
+              " 'feel',\n",
+              " 'weird',\n",
+              " 'feel',\n",
+              " 'wan',\n",
+              " 'na',\n",
+              " 'know',\n",
+              " 'incred',\n",
+              " 'feel',\n",
+              " 'frantic',\n",
+              " 'despair',\n",
+              " 'feel',\n",
+              " 'reluct',\n",
+              " 'sell',\n",
+              " 'hey',\n",
+              " 'take',\n",
+              " 'long',\n",
+              " 'sip',\n",
+              " 'feel',\n",
+              " 'cold',\n",
+              " 'sensat',\n",
+              " 'ice',\n",
+              " 'capp',\n",
+              " 'feel',\n",
+              " 'pressur',\n",
+              " 'even',\n",
+              " 'longest',\n",
+              " 'race',\n",
+              " 'one',\n",
+              " 'expect',\n",
+              " 'also',\n",
+              " 'hope',\n",
+              " 'understand',\n",
+              " 'feel',\n",
+              " 'angri',\n",
+              " 'dont',\n",
+              " 'support',\n",
+              " 'hat',\n",
+              " 'rule',\n",
+              " 'turn',\n",
+              " 'school',\n",
+              " 'event',\n",
+              " 'san',\n",
+              " 'hat',\n",
+              " 'rememb',\n",
+              " 'feel',\n",
+              " 'terrifi',\n",
+              " 'ever',\n",
+              " 'felt',\n",
+              " 'entir',\n",
+              " 'life',\n",
+              " 'still',\n",
+              " 'affect',\n",
+              " 'ive',\n",
+              " 'never',\n",
+              " 'thought',\n",
+              " 'account',\n",
+              " 'trauma',\n",
+              " 'confess',\n",
+              " 'feel',\n",
+              " 'still',\n",
+              " 'couldnt',\n",
+              " 'bring',\n",
+              " 'scare',\n",
+              " 'lose',\n",
+              " 'im',\n",
+              " 'clear',\n",
+              " 'influenc',\n",
+              " 'dash',\n",
+              " 'happi',\n",
+              " 'emili',\n",
+              " 'dickinson',\n",
+              " 'exampl',\n",
+              " 'use',\n",
+              " 'dash',\n",
+              " 'instead',\n",
+              " 'colon',\n",
+              " 'semi',\n",
+              " 'colon',\n",
+              " 'enhanc',\n",
+              " 'feel',\n",
+              " 'rush',\n",
+              " 'enjamb',\n",
+              " 'sonnet',\n",
+              " 'pray',\n",
+              " 'eye',\n",
+              " 'read',\n",
+              " 'mind',\n",
+              " 'comprehend',\n",
+              " 'heart',\n",
+              " 'feel',\n",
+              " 'offend',\n",
+              " 'im',\n",
+              " 'afraid',\n",
+              " 'go',\n",
+              " 'feel',\n",
+              " 'like',\n",
+              " 'lot',\n",
+              " 'peopl',\n",
+              " 'group',\n",
+              " 'part',\n",
+              " 'feel',\n",
+              " 'like',\n",
+              " 'would',\n",
+              " 'cool',\n",
+              " 'small',\n",
+              " 'group',\n",
+              " 'hang',\n",
+              " 'possibl',\n",
+              " 'fail',\n",
+              " 'examin',\n",
+              " 'feel',\n",
+              " 'thank',\n",
+              " 'part',\n",
+              " 'countri',\n",
+              " 'train',\n",
+              " 'outdoor',\n",
+              " 'late',\n",
+              " 'year',\n",
+              " 'bundl',\n",
+              " 'wear',\n",
+              " 'sever',\n",
+              " 'layer',\n",
+              " 'feel',\n",
+              " 'rather',\n",
+              " 'grouchi',\n",
+              " 'morn',\n",
+              " 'sinc',\n",
+              " 'didnt',\n",
+              " 'sleep',\n",
+              " 'last',\n",
+              " 'night',\n",
+              " 'purpos',\n",
+              " 'feel',\n",
+              " 'like',\n",
+              " 'earthquak',\n",
+              " 'also',\n",
+              " 'shaken',\n",
+              " 'foundat',\n",
+              " 'life',\n",
+              " 'work',\n",
+              " 'might',\n",
+              " 'abl',\n",
+              " 'recreat',\n",
+              " 'feel',\n",
+              " 'get',\n",
+              " 'back',\n",
+              " 'cold',\n",
+              " 'fog',\n",
+              " 'await',\n",
+              " 'tomorrow',\n",
+              " 'night',\n",
+              " 'woke',\n",
+              " 'feel',\n",
+              " 'posit',\n",
+              " 'total',\n",
+              " 'mood',\n",
+              " 'even',\n",
+              " 'feel',\n",
+              " 'banana',\n",
+              " 'shake',\n",
+              " 'breakfast',\n",
+              " 'chocol',\n",
+              " 'shake',\n",
+              " 'dinner',\n",
+              " 'sunday',\n",
+              " 'roast',\n",
+              " 'tea',\n",
+              " 'would',\n",
+              " 'go',\n",
+              " 'straight',\n",
+              " 'point',\n",
+              " 'rather',\n",
+              " 'defin',\n",
+              " 'romanc',\n",
+              " 'feel',\n",
+              " 'anger',\n",
+              " 'feel',\n",
+              " 'suspici',\n",
+              " 'feel',\n",
+              " 'feel',\n",
+              " 'bitchi',\n",
+              " 'mean',\n",
+              " 'terribl',\n",
+              " 'feel',\n",
+              " 'envious',\n",
+              " 'keep',\n",
+              " 'post',\n",
+              " 'regular',\n",
+              " 'interest',\n",
+              " 'wish',\n",
+              " 'could',\n",
+              " 'feel',\n",
+              " 'way',\n",
+              " 'feel',\n",
+              " 'like',\n",
+              " 'tri',\n",
+              " 'stay',\n",
+              " 'faith',\n",
+              " 'possibl',\n",
+              " 'perceiv',\n",
+              " 'real',\n",
+              " 'event',\n",
+              " 'happen',\n",
+              " 'mountain',\n",
+              " 'feel',\n",
+              " 'assault',\n",
+              " 'new',\n",
+              " 'kid',\n",
+              " 'whine',\n",
+              " 'ive',\n",
+              " 'long',\n",
+              " 'road',\n",
+              " 'initi',\n",
+              " 'feel',\n",
+              " 'like',\n",
+              " 'rude',\n",
+              " 'turn',\n",
+              " 'food',\n",
+              " 'made',\n",
+              " 'brought',\n",
+              " 'sometim',\n",
+              " 'eat',\n",
+              " 'stuff',\n",
+              " 'gluten',\n",
+              " 'free',\n",
+              " 'look',\n",
+              " 'delici',\n",
+              " 'even',\n",
+              " 'mayb',\n",
+              " 'wasnt',\n",
+              " 'felt',\n",
+              " 'good',\n",
+              " 'eat',\n",
+              " 'realli',\n",
+              " 'mediocr',\n",
+              " 'wed',\n",
+              " 'cake',\n",
+              " 'exampl',\n",
+              " 'want',\n",
+              " 'make',\n",
+              " 'sure',\n",
+              " 'didnt',\n",
+              " 'feel',\n",
+              " 'rush',\n",
+              " 'get',\n",
+              " 'centuri',\n",
+              " 'colleg',\n",
+              " 'friday',\n",
+              " 'afternoon',\n",
+              " 'id',\n",
+              " 'kick',\n",
+              " 'gear',\n",
+              " 'feel',\n",
+              " 'irrit',\n",
+              " 'motiv',\n",
+              " 'ever',\n",
+              " 'feel',\n",
+              " 'much',\n",
+              " 'product',\n",
+              " 'colleg',\n",
+              " 'keep',\n",
+              " 'product',\n",
+              " 'full',\n",
+              " 'gear',\n",
+              " 'ill',\n",
+              " 'chalk',\n",
+              " 'idea',\n",
+              " 'art',\n",
+              " 'project',\n",
+              " 'summer',\n",
+              " 'train',\n",
+              " 'armi',\n",
+              " 'attack',\n",
+              " 'pigeon',\n",
+              " 'take',\n",
+              " 'tini',\n",
+              " ...]"
+            ]
+          },
+          "execution_count": 33,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Load list from file\n",
+        "with open('../Dataset/(PreprocessedII)sentList.json', 'r') as f:\n",
+        "    sentList = json.load(f)\n",
+        "\n",
+        "wordlister = []\n",
+        "for sentence in sentList:\n",
+        "    words = sentence.split(' ')\n",
+        "    for word in words:\n",
+        "        wordlister.append(word)\n",
+        "\n",
+        "wordlister"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "x8UAzcH_LmQW",
+        "outputId": "f64e999d-034a-4078-f802-f4b6e9a82a67"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "FreqDist({'feel': 6230, 'like': 1012, 'im': 942, 'get': 402, 'go': 361, 'time': 360, 'want': 358, 'know': 349, 'make': 343, 'littl': 326, ...})"
+            ]
+          },
+          "execution_count": 34,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "wordList = FreqDist(wordlister)\n",
+        "wordList"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "UdiG5splLmQY"
+      },
+      "outputs": [],
+      "source": [
+        "labeledData = list(zip(commentList, emotionList))\n",
+        "\n",
+        "featureList = []\n",
+        "for sentence, label in labeledData:\n",
+        "    checklist = word_tokenize(sentence.lower())\n",
+        "    checklist = preProcess(checklist)\n",
+        "\n",
+        "    features = {}\n",
+        "    for word in wordList:\n",
+        "        features[word] = (word in checklist)\n",
+        "\n",
+        "    featureList.append((features, label))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Gy3PxRmBLmQZ"
+      },
+      "outputs": [],
+      "source": [
+        "# featureList"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0G1vFvJxLmQa"
+      },
+      "source": [
+        "## Naive Bayes Part"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jwM5tDvMLmQb",
+        "outputId": "4d979351-b483-4e9a-b674-b89197c9248f"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Most Informative Features\n",
+            "                   scare = True             fear : joy    =     41.1 : 1.0\n",
+            "                 terrifi = True             fear : joy    =     39.0 : 1.0\n",
+            "                   unsur = True             fear : anger  =     34.7 : 1.0\n",
+            "                  vulner = True             fear : joy    =     33.6 : 1.0\n",
+            "                paranoid = True             fear : anger  =     32.0 : 1.0\n",
+            "Acc: 0.8812131423757371\n"
+          ]
+        }
+      ],
+      "source": [
+        "random.shuffle(featureList)\n",
+        "idxData = int(len(featureList)*0.8)\n",
+        "trainData = featureList[:idxData]\n",
+        "testData = featureList[idxData:]\n",
+        "\n",
+        "classifier = NaiveBayesClassifier.train(trainData)\n",
+        "\n",
+        "# Compute Accuracy\n",
+        "acc = accuracy(classifier, testData)\n",
+        "\n",
+        "classifier.show_most_informative_features(n=5)\n",
+        "print(f'Accuracy: {acc}')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-EcW0R15LmQc",
+        "outputId": "c2e1ca0b-bea4-4867-dc73-ae743ba76e06"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Confusion Matrix:\n",
+            "[[357  26  11]\n",
+            " [ 23 343  12]\n",
+            " [ 28  41 346]]\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Make predictions on the test data\n",
+        "predicted_labels = [classifier.classify(features) for features, label in testData]\n",
+        "true_labels = [label for features, label in testData]\n",
+        "\n",
+        "# Compute confusion matrix\n",
+        "conf_matrix = confusion_matrix(true_labels, predicted_labels)\n",
+        "print(\"Confusion Matrix:\")\n",
+        "print(conf_matrix)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qVHGABEQLmQd",
+        "outputId": "c9ef6638-1a84-4b92-c22e-782dbf645002"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x600 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Plot confusion matrix as heatmap\n",
+        "plt.figure(figsize=(8, 6))\n",
+        "sns.set(font_scale=1.2)\n",
+        "sns.heatmap(conf_matrix, annot=True, cmap='magma', fmt='g', xticklabels=True, yticklabels=True)\n",
+        "plt.xlabel('Predicted Label')\n",
+        "plt.ylabel('True Label')\n",
+        "plt.title('CM Naive Bayes')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "RV74Sye4LmQe",
+        "outputId": "054b8930-0969-4921-c6d8-2ffd7e80372c"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Accuracy: 0.8812131423757371\n",
+            "Precision: 0.8846773880544198\n",
+            "Recall: 0.8812131423757371\n",
+            "F1 Score: 0.8813312267889719\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Compute precision, recall, and F1 score\n",
+        "precision = precision_score(true_labels, predicted_labels, average='weighted')\n",
+        "recall = recall_score(true_labels, predicted_labels, average='weighted')\n",
+        "f1 = f1_score(true_labels, predicted_labels, average='weighted')\n",
+        "\n",
+        "# Print evaluation metrics\n",
+        "print(f'Accuracy: {acc}')\n",
+        "print(f'Precision: {precision}')\n",
+        "print(f'Recall: {recall}')\n",
+        "print(f'F1 Score: {f1}')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cT9nIIdWLmQf",
+        "outputId": "38736f92-1cec-43a2-ed50-69f4df29569f"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Sentence: It was so frightening\n",
+            "Emotion: fear\n"
+          ]
+        }
+      ],
+      "source": [
+        "testDrive = \"It was so frightening\"\n",
+        "print(f'Sentence: {testDrive}')\n",
+        "print(f'Emotion: {classifier.classify(FreqDist(preProcess(word_tokenize(testDrive))))}')"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "base",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.13"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

LEGACY/QuotesSelector.ipynb

@@ -0,0 +1,851 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Fjb2MSfGZxXW"
+      },
+      "outputs": [],
+      "source": [
+        "import torch\n",
+        "from transformers import BertTokenizer, BertForSequenceClassification\n",
+        "from torch.utils.data import DataLoader, TensorDataset\n",
+        "import pandas as pd\n",
+        "from sklearn.preprocessing import LabelEncoder\n",
+        "\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "import random\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "import pandas as pd\n",
+        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+        "from sklearn.metrics.pairwise import cosine_similarity\n",
+        "import numpy as np"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4daEm8HLZxXK"
+      },
+      "source": [
+        "## LOADUP Trained Model BERT"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "u0hvSKq4ZxXO"
+      },
+      "outputs": [],
+      "source": [
+        "dataset = pd.read_csv('../Dataset/(Preprocessed)Emotion_classify_Data(Labeled).csv')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ALC9OXraZxXQ"
+      },
+      "outputs": [],
+      "source": [
+        "# Shuffle the dataset\n",
+        "dataset = dataset.sample(frac=1, random_state=42).reset_index(drop=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "FDYeA9lnZxXR",
+        "outputId": "2960b5d0-f2aa-46d6-d8a7-1ec91f5c57c3"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
+            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Initialize BERT tokenizer\n",
+        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
+        "\n",
+        "# Load pre-trained BERT model for sequence classification\n",
+        "model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=dataset['Emotion'].nunique())\n",
+        "\n",
+        "# Fine-tune BERT on your dataset\n",
+        "learning_rate = 2e-5\n",
+        "optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)\n",
+        "loss_fn = torch.nn.CrossEntropyLoss()\n",
+        "\n",
+        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "oqFQb1sDZxXT",
+        "outputId": "3a44cbf6-b96c-48ea-879b-d79e6d940ae0"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-1 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-1 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-1 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-1 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LabelEncoder()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LabelEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.LabelEncoder.html\">?<span>Documentation for LabelEncoder</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LabelEncoder()</pre></div> </div></div></div></div>"
+            ],
+            "text/plain": [
+              "LabelEncoder()"
+            ]
+          },
+          "execution_count": 13,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "emotion_labels = dataset['Emotion']\n",
+        "\n",
+        "# Instantiate the label encoder\n",
+        "label_encoder = LabelEncoder()\n",
+        "\n",
+        "# Fit the label encoder to the encoded emotion labels\n",
+        "label_encoder.fit(emotion_labels)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2sNgKjaTZxXT"
+      },
+      "outputs": [],
+      "source": [
+        "  # Load the checkpoint\n",
+        "  checkpoint = torch.load('last_trained_model_checkpoint.pth' , map_location=torch.device('cpu'))\n",
+        "\n",
+        "  # Load model and optimizer states\n",
+        "  model.load_state_dict(checkpoint['model_state_dict'])\n",
+        "  optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n",
+        "\n",
+        "  # Update other necessary variables\n",
+        "  epoch = checkpoint['epoch']\n",
+        "  best_val_loss = checkpoint['best_val_loss']\n",
+        "  early_stop_count = checkpoint['early_stop_count']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "IKBkgBMvZxXU",
+        "outputId": "19a64462-3f9d-46e7-8b18-230f3da49987"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Predicted Emotion: joy\n",
+            "Predicted Label: tensor([[-2.6676, -2.7545,  6.4300]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "def predict_emotion(text, label_encoder):\n",
+        "    # Tokenize the text using the same tokenizer used during training\n",
+        "    inputs = tokenizer.encode_plus(text, add_special_tokens=True, max_length=128, padding='max_length', truncation=True, return_tensors='pt')\n",
+        "    input_ids = inputs['input_ids'].to(device)  # Move input to the same device as the model\n",
+        "    attention_mask = inputs['attention_mask'].to(device)  # Move attention mask to the same device as the model\n",
+        "\n",
+        "    # Make prediction\n",
+        "    with torch.no_grad():\n",
+        "        model.eval()  # Set model to evaluation mode\n",
+        "        outputs = model(input_ids, attention_mask=attention_mask)\n",
+        "    logits = outputs.logits\n",
+        "\n",
+        "    # Get predicted label\n",
+        "    predicted_label = torch.argmax(logits, dim=1).item()\n",
+        "\n",
+        "    # Decode the predicted label using label_encoder\n",
+        "    predicted_emotion = label_encoder.inverse_transform([predicted_label])[0]\n",
+        "\n",
+        "    return predicted_emotion, logits\n",
+        "\n",
+        "# Example usage\n",
+        "text = \"Super happy today\"\n",
+        "predicted_emotion, x = predict_emotion(text, label_encoder)\n",
+        "print(f'Predicted Emotion: {predicted_emotion}')\n",
+        "print(f'Predicted Label: {x}')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "P0nhzIDyZxXV"
+      },
+      "source": [
+        "## Get the Quotes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "MDIaQThjZxXX",
+        "outputId": "9ad0c687-af99-447c-ea1d-c12aa2db310e"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Quote</th>\n",
+              "      <th>Author</th>\n",
+              "      <th>emotion</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Don't cry because it's over, smile because it ...</td>\n",
+              "      <td>Dr. Seuss</td>\n",
+              "      <td>joy</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>I'm selfish, impatient and a little insecure. ...</td>\n",
+              "      <td>Marilyn Monroe</td>\n",
+              "      <td>anger</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Be yourself; everyone else is already taken.</td>\n",
+              "      <td>Oscar Wilde</td>\n",
+              "      <td>anger</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Two things are infinite: the universe and huma...</td>\n",
+              "      <td>Albert Einstein</td>\n",
+              "      <td>joy</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Be who you are and say what you feel, because ...</td>\n",
+              "      <td>Bernard M. Baruch</td>\n",
+              "      <td>joy</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>36932</th>\n",
+              "      <td>In Buddhism, they say attachment to anything o...</td>\n",
+              "      <td>Jason Mraz</td>\n",
+              "      <td>joy</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>36933</th>\n",
+              "      <td>I love British humor. It's just so - surreal.</td>\n",
+              "      <td>Beck</td>\n",
+              "      <td>fear</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>36934</th>\n",
+              "      <td>I've got a sense of humor. I'm a funny guy.</td>\n",
+              "      <td>Daryl Hall</td>\n",
+              "      <td>joy</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>36935</th>\n",
+              "      <td>Humor is such a wonderful thing, helping you r...</td>\n",
+              "      <td>Lynda Barry</td>\n",
+              "      <td>joy</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>36936</th>\n",
+              "      <td>Life Is Full of Obstacles, Stumble Upon !!</td>\n",
+              "      <td>Pratik Shelar</td>\n",
+              "      <td>fear</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>36937 rows × 3 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                                                   Quote             Author  \\\n",
+              "0      Don't cry because it's over, smile because it ...          Dr. Seuss   \n",
+              "1      I'm selfish, impatient and a little insecure. ...     Marilyn Monroe   \n",
+              "2           Be yourself; everyone else is already taken.        Oscar Wilde   \n",
+              "3      Two things are infinite: the universe and huma...    Albert Einstein   \n",
+              "4      Be who you are and say what you feel, because ...  Bernard M. Baruch   \n",
+              "...                                                  ...                ...   \n",
+              "36932  In Buddhism, they say attachment to anything o...         Jason Mraz   \n",
+              "36933      I love British humor. It's just so - surreal.               Beck   \n",
+              "36934        I've got a sense of humor. I'm a funny guy.         Daryl Hall   \n",
+              "36935  Humor is such a wonderful thing, helping you r...        Lynda Barry   \n",
+              "36936         Life Is Full of Obstacles, Stumble Upon !!      Pratik Shelar   \n",
+              "\n",
+              "      emotion  \n",
+              "0         joy  \n",
+              "1       anger  \n",
+              "2       anger  \n",
+              "3         joy  \n",
+              "4         joy  \n",
+              "...       ...  \n",
+              "36932     joy  \n",
+              "36933    fear  \n",
+              "36934     joy  \n",
+              "36935     joy  \n",
+              "36936    fear  \n",
+              "\n",
+              "[36937 rows x 3 columns]"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "quotesDF = pd.read_csv('../Dataset/(Preprocessed)quotes.csv')\n",
+        "quotesDF"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0_Z5tCRWZxXX"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+        "from sklearn.metrics.pairwise import cosine_similarity\n",
+        "\n",
+        "def find_most_similar_quote(input_sentence, quotes_df, input_emotion, emotion_weight=1.1):\n",
+        "    # Combine all quotes into a list\n",
+        "    quotes = quotes_df['Quote'].tolist()\n",
+        "    authors = quotes_df['Author'].tolist()\n",
+        "    emotions = quotes_df['emotion'].tolist()\n",
+        "\n",
+        "    # Initialize the vectorizer and fit it on all quotes\n",
+        "    vectorizer = TfidfVectorizer()\n",
+        "    tfidf_matrix = vectorizer.fit_transform(quotes)\n",
+        "\n",
+        "    # Vectorize the input sentence\n",
+        "    input_vec = vectorizer.transform([input_sentence])\n",
+        "\n",
+        "    # Calculate cosine similarity between the input sentence and all quotes\n",
+        "    cosine_similarities = cosine_similarity(input_vec, tfidf_matrix).flatten()\n",
+        "\n",
+        "    # Apply emotion-based weighting\n",
+        "    emotion_weights = np.array([emotion_weight if emotion == input_emotion else 1.0 for emotion in emotions])\n",
+        "    weighted_similarities = cosine_similarities * emotion_weights\n",
+        "\n",
+        "     # Get the indices of the top 5 quotes with the highest similarity scores\n",
+        "    top_10_indices = np.argsort(weighted_similarities)[-5:]\n",
+        "\n",
+        "    # Randomly select one quote from the top\n",
+        "    selected_index = random.choice(top_10_indices)\n",
+        "\n",
+        "    # Return the most similar quote and its author\n",
+        "    return quotes[selected_index], authors[selected_index], emotions[selected_index]\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "uOC5p6SXZxXY",
+        "outputId": "7fd15a8b-fa1c-4db9-d976-eeff41fa89f6"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Input sentence:  What should I do with my life?\n",
+            "Detected emotion: anger\n",
+            "Emotion score: tensor([[ 1.2938,  0.3162, -1.3738]])\n",
+            "Most similar quote: Before I can tell my life what I want to do with it, I must listen to my life telling me who I am.\n",
+            "Author:  Parker J. Palmer,  Let Your Life Speak: Listening for the Voice of Vocation\n",
+            "Quotes Emotion:  joy\n"
+          ]
+        }
+      ],
+      "source": [
+        "input_sentence = \"What should I do with my life?\"\n",
+        "detected_emotion, emotion_score = predict_emotion(input_sentence, label_encoder)\n",
+        "most_similar_quote, author, emotionQuotes = find_most_similar_quote(input_sentence, quotesDF, detected_emotion)\n",
+        "print('Input sentence: ', input_sentence)\n",
+        "print(\"Detected emotion:\", detected_emotion)\n",
+        "print(\"Emotion score:\", emotion_score)\n",
+        "print(\"Most similar quote:\", most_similar_quote)\n",
+        "print(\"Author: \", author)\n",
+        "print(\"Quotes Emotion: \", emotionQuotes)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "base",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.13"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 Michael Dimas Chrispradipta
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

Makefile

@@ -0,0 +1,23 @@
+.PHONY: dev-backend dev-frontend test build-index validate-models docker-build docker-run
+
+dev-backend:
+	cd backend && python app.py
+
+dev-frontend:
+	cd frontend && npm start
+
+test:
+	cd backend && pytest
+
+build-index:
+	cd backend && python scripts/build_sbert_index.py
+
+validate-models:
+	cd backend && python scripts/validate_models.py
+
+docker-build:
+	docker build -t quotify .
+
+docker-run:
+	docker run --rm -p 7860:7860 --env QUOTIFY_ENABLE_CROSS_ENCODER=false quotify
+

README.md

@@ -1,10 +1,284 @@
 ---
-title: Quotify Advance
-emoji: 📚
+title: Quotify
+emoji: 💬
 colorFrom: green
-colorTo: red
+colorTo: blue
 sdk: docker
+app_port: 7860
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Quotify
+
+Quotify is an emotion-aware quote recommendation prototype. A user writes how they feel, the backend detects the likely emotion, and the app recommends quotes using one of three selectable NLP retrieval strategies.
+
+The original coursework method is preserved as the baseline, and the upgraded version adds modern dense retrieval plus an optional retrieve-and-rerank pipeline suitable for an AI/NLP portfolio demo.
+
+## What Changed
+
+- Refactored the Flask backend into modular strategy classes.
+- Added `/health`, `/strategies`, `/get_quote`, and `/compare`.
+- Preserved the legacy BERT + TF-IDF method as a selectable baseline.
+- Added SentenceTransformer dense semantic search.
+- Added optional Bi-Encoder retrieval + Cross-Encoder reranking.
+- Added model/artifact fallback behavior so missing checkpoints do not crash the app.
+- Updated the React UI with strategy switching, compare mode, score cards, loading states, and error states.
+- Added Docker deployment for Hugging Face Spaces on port `7860`.
+
+## Recommendation Strategies
+
+| Strategy ID | UI Label | Method | Best Use |
+| --- | --- | --- | --- |
+| `legacy_tfidf_bert` | Legacy BERT + TF-IDF | Fine-tuned BERT emotion classifier, TF-IDF cosine similarity, emotion boost | Preserves the original project method |
+| `sbert_dense` | Dense Semantic Search | `sentence-transformers/all-MiniLM-L6-v2` quote embeddings, cosine similarity, emotion boost | Better meaning-based retrieval on CPU |
+| `cross_encoder_rerank` | AI Reranker | Dense retrieval top K, `cross-encoder/ms-marco-MiniLM-L6-v2` reranking, emotion-aware final score | Higher quality reranking when CPU/memory allows |
+
+## Architecture
+
+```text
+React UI
+  |-- strategy selector
+  |-- quote result cards
+  `-- compare-all mode
+        |
+        v
+Flask API
+  |-- GET /health
+  |-- GET /strategies
+  |-- POST /get_quote
+  `-- POST /compare
+        |
+        v
+Recommendation registry
+  |-- legacy_tfidf_bert
+  |-- sbert_dense
+  `-- cross_encoder_rerank
+        |
+        v
+Data and models
+  |-- fine-tuned BERT checkpoint, optional
+  |-- preprocessed emotion dataset
+  |-- preprocessed quote dataset
+  `-- generated SBERT artifacts under backend/artifacts/
+```
+
+## Tech Stack
+
+- Backend: Flask, gunicorn, PyTorch, Transformers, SentenceTransformers, scikit-learn, pandas, NumPy
+- Frontend: React 18, Create React App, CSS, React Icons
+- Deployment: Docker, Hugging Face Spaces
+- Research assets: Jupyter notebooks and official PDF report in `LEGACY/`
+
+## Local Setup
+
+### Backend
+
+```bash
+cd backend
+python3 -m venv .venv
+source .venv/bin/activate
+pip install -r requirements.txt
+python app.py
+```
+
+The backend runs on:
+
+```text
+http://127.0.0.1:7860
+```
+
+For a lighter local run that avoids loading dense models:
+
+```bash
+QUOTIFY_ENABLE_SBERT=false QUOTIFY_ENABLE_CROSS_ENCODER=false python app.py
+```
+
+### Frontend
+
+```bash
+cd frontend
+npm install
+npm start
+```
+
+The frontend runs on:
+
+```text
+http://localhost:3000
+```
+
+Create React App proxies API calls to the backend through `frontend/package.json`.
+
+## Model Checkpoint
+
+The original fine-tuned BERT checkpoint is intentionally not committed. Download it from:
+
+```text
+https://drive.google.com/file/d/1UCsaBpSQEWzD8kkK2Etcdw5r63pmj2yO/view?usp=drive_web
+```
+
+Place it at:
+
+```text
+backend/last_trained_model_checkpoint.pth
+```
+
+If the checkpoint is missing, Quotify still starts and uses a clearly marked lightweight keyword fallback for emotion detection. `/health` reports whether the real checkpoint is loaded.
+
+## Artifact Generation
+
+The dense semantic strategy builds quote embeddings on first startup when `sentence-transformers` is available. To build them manually:
+
+```bash
+make build-index
+```
+
+Generated files are written to `backend/artifacts/`:
+
+```text
+quote_embeddings.npy
+quote_metadata.csv
+artifact_manifest.json
+```
+
+These artifacts can be large, so they are ignored by Git by default.
+
+## Docker
+
+Build and run locally:
+
+```bash
+make docker-build
+make docker-run
+```
+
+Or:
+
+```bash
+docker compose up --build
+```
+
+The container serves the React build and Flask API from one process on:
+
+```text
+http://localhost:7860
+```
+
+## Hugging Face Spaces Deployment
+
+1. Create a new Hugging Face Space.
+2. Choose Docker as the SDK.
+3. Push this repository to the Space.
+4. Keep the default app port as `7860`.
+5. Optional: add repository secrets or Space variables:
+   - `QUOTIFY_DEFAULT_STRATEGY=sbert_dense`
+   - `QUOTIFY_ENABLE_CROSS_ENCODER=false`
+   - `QUOTIFY_EMOTION_BOOST=0.08`
+   - `QUOTIFY_TOP_K_RETRIEVE=50`
+   - `QUOTIFY_MODEL_CHECKPOINT_PATH=last_trained_model_checkpoint.pth`
+6. If you do not include the BERT checkpoint, the app still runs with fallback emotion detection.
+
+For free CPU Spaces, keep `QUOTIFY_ENABLE_CROSS_ENCODER=false` unless startup time and memory are acceptable.
+
+## API Examples
+
+### Health
+
+```bash
+curl http://127.0.0.1:7860/health
+```
+
+### Strategies
+
+```bash
+curl http://127.0.0.1:7860/strategies
+```
+
+### Get One Quote
+
+```bash
+curl -X POST http://127.0.0.1:7860/get_quote \
+  -H "Content-Type: application/json" \
+  -d '{"inputText":"I feel nervous about tomorrow","strategy":"sbert_dense","topK":5}'
+```
+
+Example response:
+
+```json
+{
+  "quote": "Returned quote text",
+  "author": "Quote author",
+  "emotion": "fear",
+  "quote_emotion": "fear",
+  "strategy": "sbert_dense",
+  "strategy_label": "Dense Semantic Search",
+  "scores": {
+    "semantic_score": 0.721,
+    "cross_encoder_score": null,
+    "emotion_boost": 0.08,
+    "final_score": 0.801
+  },
+  "alternatives": []
+}
+```
+
+### Compare Methods
+
+```bash
+curl -X POST http://127.0.0.1:7860/compare \
+  -H "Content-Type: application/json" \
+  -d '{"inputText":"I feel nervous about tomorrow","topK":5}'
+```
+
+## Developer Commands
+
+```bash
+make dev-backend
+make dev-frontend
+make test
+make build-index
+make validate-models
+make docker-build
+make docker-run
+```
+
+## Environment Variables
+
+| Variable | Default | Purpose |
+| --- | --- | --- |
+| `PORT` | `7860` | Flask/gunicorn port |
+| `QUOTIFY_DEFAULT_STRATEGY` | `sbert_dense` | Preferred recommendation strategy |
+| `QUOTIFY_ENABLE_SBERT` | `true` | Enable dense retrieval loading |
+| `QUOTIFY_ENABLE_CROSS_ENCODER` | `true` locally, `false` in Docker | Enable optional reranker |
+| `QUOTIFY_EMOTION_BOOST` | `0.08` | Score boost for emotion-aligned quotes |
+| `QUOTIFY_TOP_K_RETRIEVE` | `50` | Dense candidates passed to reranker |
+| `QUOTIFY_MODEL_CHECKPOINT_PATH` | `last_trained_model_checkpoint.pth` | Fine-tuned BERT checkpoint path |
+
+## Limitations
+
+- Emotion detection is limited to `anger`, `fear`, and `joy`.
+- The quote emotion labels are generated by the project model, so they may contain prediction errors.
+- Dense retrieval may need to download open-source model files from Hugging Face on first run.
+- Cross-Encoder reranking is slower and may be disabled on constrained CPU deployments.
+- The fallback emotion classifier is only for local/demo resilience, not a replacement for the fine-tuned model.
+
+## Ethical Note
+
+Quotify recommends quotes for reflection and inspiration. It is not a mental health diagnosis tool, therapy tool, crisis intervention system, or substitute for professional support.
+
+## Legacy Project Reference
+
+The official report and original research notebooks remain under `LEGACY/`:
+
+```text
+LEGACY/Machine Learning-AOL.pdf
+LEGACY/EDAandPreprocess.ipynb
+LEGACY/NaiveBayes_ML_AOL_3_0.ipynb
+LEGACY/LogisticRegression_ML_AOL_2_0.ipynb
+LEGACY/BERT_ML_AOL_5_0.ipynb
+LEGACY/QuotesSelector.ipynb
+```
+
+## License
+
+See [`LICENSE`](LICENSE).

backend/.python-version

@@ -0,0 +1,2 @@
+3.11
+

backend/app.py

@@ -0,0 +1,140 @@
+from pathlib import Path
+
+from flask import Flask, jsonify, request, send_from_directory
+from flask_cors import CORS
+
+from src.config import AppConfig, config
+from src.data_loader import load_data
+from src.emotion_classifier import EmotionClassifier
+from src.health import artifact_status
+from src.recommenders import CrossEncoderRerankRecommender, LegacyTfidfRecommender, SbertDenseRecommender
+
+
+def create_app(app_config: AppConfig = config, quotes_override=None) -> Flask:
+    static_folder = str(app_config.frontend_build_dir) if app_config.frontend_build_dir.exists() else None
+    app = Flask(__name__, static_folder=static_folder, static_url_path="")
+
+    if app_config.cors_origins:
+        CORS(app, origins=[origin.strip() for origin in app_config.cors_origins.split(",") if origin.strip()])
+    else:
+        CORS(app, origins=["http://localhost:3000", "http://127.0.0.1:3000"])
+
+    loaded_data = load_data(app_config, quotes_override=quotes_override)
+    emotion_classifier = EmotionClassifier(app_config, loaded_data.emotion_data["Emotion"].unique())
+    legacy = LegacyTfidfRecommender(app_config, loaded_data.quotes, emotion_classifier)
+    dense = SbertDenseRecommender(app_config, loaded_data.quotes, emotion_classifier)
+    reranker = CrossEncoderRerankRecommender(app_config, loaded_data.quotes, emotion_classifier, dense)
+    recommenders = {
+        legacy.strategy_id: legacy,
+        dense.strategy_id: dense,
+        reranker.strategy_id: reranker,
+    }
+
+    app.extensions["quotify"] = {
+        "config": app_config,
+        "data": loaded_data,
+        "emotion_classifier": emotion_classifier,
+        "recommenders": recommenders,
+    }
+
+    def _available_recommenders():
+        return {key: value for key, value in recommenders.items() if value.available}
+
+    def _resolve_strategy(strategy_id):
+        requested = strategy_id or app_config.default_strategy
+        if requested in recommenders and recommenders[requested].available:
+            return recommenders[requested]
+        available = _available_recommenders()
+        if available:
+            return next(iter(available.values()))
+        raise RuntimeError("No recommendation strategies are available.")
+
+    @app.get("/health")
+    def health():
+        available = _available_recommenders()
+        return jsonify(
+            {
+                "status": "ok" if available else "degraded",
+                "app": app_config.app_name,
+                "version": app_config.version,
+                "available_strategies": list(available.keys()),
+                "strategies": [strategy.info().to_dict() for strategy in recommenders.values()],
+                "artifacts": artifact_status(app_config, dense),
+                "models": {
+                    "emotion_classifier": emotion_classifier.status.to_dict(),
+                    "sentence_transformer": {
+                        "available": dense.available,
+                        "model": app_config.sbert_model_name,
+                        "reason": dense.unavailable_reason or None,
+                    },
+                    "cross_encoder": {
+                        "available": reranker.available,
+                        "enabled": app_config.enable_cross_encoder,
+                        "model": app_config.cross_encoder_model_name,
+                        "reason": reranker.unavailable_reason or None,
+                    },
+                },
+            }
+        )
+
+    @app.get("/strategies")
+    def strategies():
+        return jsonify([strategy.info().to_dict() for strategy in recommenders.values()])
+
+    @app.post("/get_quote")
+    def get_quote():
+        payload = request.get_json(silent=True) or {}
+        input_text = str(payload.get("inputText", "")).strip()
+        if not input_text:
+            return jsonify({"error": "inputText is required."}), 400
+
+        top_k = int(payload.get("topK") or 5)
+        strategy = _resolve_strategy(payload.get("strategy"))
+        try:
+            return jsonify(strategy.recommend(input_text, top_k=top_k).to_dict())
+        except Exception as exc:
+            return jsonify({"error": str(exc), "strategy": strategy.strategy_id}), 503
+
+    @app.post("/compare")
+    def compare():
+        payload = request.get_json(silent=True) or {}
+        input_text = str(payload.get("inputText", "")).strip()
+        if not input_text:
+            return jsonify({"error": "inputText is required."}), 400
+
+        top_k = int(payload.get("topK") or 5)
+        results = []
+        errors = []
+        for strategy in recommenders.values():
+            if not strategy.available:
+                errors.append({"strategy": strategy.strategy_id, "error": strategy.unavailable_reason})
+                continue
+            try:
+                results.append(strategy.recommend(input_text, top_k=top_k).to_dict())
+            except Exception as exc:
+                errors.append({"strategy": strategy.strategy_id, "error": str(exc)})
+        return jsonify({"inputText": input_text, "results": results, "errors": errors})
+
+    @app.get("/")
+    def index():
+        if app.static_folder:
+            return send_from_directory(app.static_folder, "index.html")
+        return jsonify({"app": app_config.app_name, "message": "Frontend build is not available."})
+
+    @app.get("/<path:path>")
+    def static_proxy(path):
+        if app.static_folder:
+            candidate = Path(app.static_folder) / path
+            if candidate.exists():
+                return send_from_directory(app.static_folder, path)
+            return send_from_directory(app.static_folder, "index.html")
+        return jsonify({"error": "Not found"}), 404
+
+    return app
+
+
+app = create_app()
+
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=config.port, debug=False)

backend/artifacts/.gitkeep

@@ -0,0 +1 @@
+

backend/model.ipynb

@@ -0,0 +1,439 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from transformers import BertTokenizer, BertForSequenceClassification, AdamW\n",
+    "from torch.utils.data import DataLoader, TensorDataset\n",
+    "import pandas as pd\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "import random\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import pandas as pd\n",
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "from sklearn.metrics.pairwise import cosine_similarity\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### LOADUP Trained Model BERT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias']\n",
+      "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+      "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
+      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset = pd.read_csv('./(Preprocessed)Emotion_classify_Data(Labeled).csv')\n",
+    "\n",
+    "# Shuffle the dataset\n",
+    "dataset = dataset.sample(frac=1, random_state=42).reset_index(drop=True)\n",
+    "\n",
+    "# Initialize BERT tokenizer\n",
+    "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
+    "\n",
+    "# Load pre-trained BERT model for sequence classification\n",
+    "model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=dataset['Emotion'].nunique())\n",
+    "\n",
+    "# Fine-tune BERT on your dataset\n",
+    "learning_rate = 2e-5\n",
+    "optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)\n",
+    "loss_fn = torch.nn.CrossEntropyLoss()\n",
+    "\n",
+    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LabelEncoder()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LabelEncoder</label><div class=\"sk-toggleable__content\"><pre>LabelEncoder()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LabelEncoder()"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "emotion_labels = dataset['Emotion']\n",
+    "\n",
+    "# Instantiate the label encoder\n",
+    "label_encoder = LabelEncoder()\n",
+    "\n",
+    "# Fit the label encoder to the encoded emotion labels\n",
+    "label_encoder.fit(emotion_labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias']\n",
+      "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+      "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
+      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model and optimizer states loaded successfully.\n",
+      "Epoch: 7, Best Validation Loss: 0.09719424509431089, Early Stop Count: 5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "d:\\App\\Programming\\python\\Anaconda\\lib\\site-packages\\transformers\\optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load the checkpoint\n",
+    "checkpoint = torch.load('last_trained_model_checkpoint.pth', map_location=torch.device('cpu'))\n",
+    "\n",
+    "# Initialize the model and optimizer\n",
+    "model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=3)\n",
+    "optimizer = AdamW(model.parameters(), lr=5e-5)\n",
+    "\n",
+    "# Load model and optimizer states with strict=False to ignore missing keys\n",
+    "model.load_state_dict(checkpoint['model_state_dict'], strict=False)\n",
+    "optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n",
+    "\n",
+    "# Update other necessary variables\n",
+    "epoch = checkpoint['epoch']\n",
+    "best_val_loss = checkpoint['best_val_loss']\n",
+    "early_stop_count = checkpoint['early_stop_count']\n",
+    "\n",
+    "print(f\"Model and optimizer states loaded successfully.\")\n",
+    "print(f\"Epoch: {epoch}, Best Validation Loss: {best_val_loss}, Early Stop Count: {early_stop_count}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted Emotion: joy\n",
+      "Predicted Label: tensor([[-2.6676, -2.7545,  6.4300]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "def predict_emotion(text, label_encoder):\n",
+    "    # Tokenize the text using the same tokenizer used during training\n",
+    "    inputs = tokenizer.encode_plus(text, add_special_tokens=True, max_length=128, padding='max_length', truncation=True, return_tensors='pt')\n",
+    "    input_ids = inputs['input_ids'].to(device)  # Move input to the same device as the model\n",
+    "    attention_mask = inputs['attention_mask'].to(device)  # Move attention mask to the same device as the model\n",
+    "\n",
+    "    # Make prediction\n",
+    "    with torch.no_grad():\n",
+    "        model.eval()  # Set model to evaluation mode\n",
+    "        outputs = model(input_ids, attention_mask=attention_mask)\n",
+    "    logits = outputs.logits\n",
+    "\n",
+    "    # Get predicted label\n",
+    "    predicted_label = torch.argmax(logits, dim=1).item()\n",
+    "\n",
+    "    # Decode the predicted label using label_encoder\n",
+    "    predicted_emotion = label_encoder.inverse_transform([predicted_label])[0]\n",
+    "\n",
+    "    return predicted_emotion, logits\n",
+    "\n",
+    "# Example usage\n",
+    "text = \"Super happy today\"\n",
+    "predicted_emotion, x = predict_emotion(text, label_encoder)\n",
+    "print(f'Predicted Emotion: {predicted_emotion}')\n",
+    "print(f'Predicted Label: {x}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Read the Quote"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Quote</th>\n",
+       "      <th>Author</th>\n",
+       "      <th>emotion</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Don't cry because it's over, smile because it ...</td>\n",
+       "      <td>Dr. Seuss</td>\n",
+       "      <td>joy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>I'm selfish, impatient and a little insecure. ...</td>\n",
+       "      <td>Marilyn Monroe</td>\n",
+       "      <td>anger</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Be yourself; everyone else is already taken.</td>\n",
+       "      <td>Oscar Wilde</td>\n",
+       "      <td>anger</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Two things are infinite: the universe and huma...</td>\n",
+       "      <td>Albert Einstein</td>\n",
+       "      <td>joy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Be who you are and say what you feel, because ...</td>\n",
+       "      <td>Bernard M. Baruch</td>\n",
+       "      <td>joy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36932</th>\n",
+       "      <td>In Buddhism, they say attachment to anything o...</td>\n",
+       "      <td>Jason Mraz</td>\n",
+       "      <td>joy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36933</th>\n",
+       "      <td>I love British humor. It's just so - surreal.</td>\n",
+       "      <td>Beck</td>\n",
+       "      <td>fear</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36934</th>\n",
+       "      <td>I've got a sense of humor. I'm a funny guy.</td>\n",
+       "      <td>Daryl Hall</td>\n",
+       "      <td>joy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36935</th>\n",
+       "      <td>Humor is such a wonderful thing, helping you r...</td>\n",
+       "      <td>Lynda Barry</td>\n",
+       "      <td>joy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36936</th>\n",
+       "      <td>Life Is Full of Obstacles, Stumble Upon !!</td>\n",
+       "      <td>Pratik Shelar</td>\n",
+       "      <td>fear</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>36937 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                   Quote             Author  \\\n",
+       "0      Don't cry because it's over, smile because it ...          Dr. Seuss   \n",
+       "1      I'm selfish, impatient and a little insecure. ...     Marilyn Monroe   \n",
+       "2           Be yourself; everyone else is already taken.        Oscar Wilde   \n",
+       "3      Two things are infinite: the universe and huma...    Albert Einstein   \n",
+       "4      Be who you are and say what you feel, because ...  Bernard M. Baruch   \n",
+       "...                                                  ...                ...   \n",
+       "36932  In Buddhism, they say attachment to anything o...         Jason Mraz   \n",
+       "36933      I love British humor. It's just so - surreal.               Beck   \n",
+       "36934        I've got a sense of humor. I'm a funny guy.         Daryl Hall   \n",
+       "36935  Humor is such a wonderful thing, helping you r...        Lynda Barry   \n",
+       "36936         Life Is Full of Obstacles, Stumble Upon !!      Pratik Shelar   \n",
+       "\n",
+       "      emotion  \n",
+       "0         joy  \n",
+       "1       anger  \n",
+       "2       anger  \n",
+       "3         joy  \n",
+       "4         joy  \n",
+       "...       ...  \n",
+       "36932     joy  \n",
+       "36933    fear  \n",
+       "36934     joy  \n",
+       "36935     joy  \n",
+       "36936    fear  \n",
+       "\n",
+       "[36937 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "quotesDF = pd.read_csv('./(Preprocessed)quotes.csv')\n",
+    "quotesDF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "from sklearn.metrics.pairwise import cosine_similarity\n",
+    "\n",
+    "def find_most_similar_quote(input_sentence, quotes_df, input_emotion, emotion_weight=1.1):\n",
+    "    # Combine all quotes into a list\n",
+    "    quotes = quotes_df['Quote'].tolist()\n",
+    "    authors = quotes_df['Author'].tolist()\n",
+    "    emotions = quotes_df['emotion'].tolist()\n",
+    "\n",
+    "    # Initialize the vectorizer and fit it on all quotes\n",
+    "    vectorizer = TfidfVectorizer()\n",
+    "    tfidf_matrix = vectorizer.fit_transform(quotes)\n",
+    "\n",
+    "    # Vectorize the input sentence\n",
+    "    input_vec = vectorizer.transform([input_sentence])\n",
+    "\n",
+    "    # Calculate cosine similarity between the input sentence and all quotes\n",
+    "    cosine_similarities = cosine_similarity(input_vec, tfidf_matrix).flatten()\n",
+    "\n",
+    "    # Apply emotion-based weighting\n",
+    "    emotion_weights = np.array([emotion_weight if emotion == input_emotion else 1.0 for emotion in emotions])\n",
+    "    weighted_similarities = cosine_similarities * emotion_weights\n",
+    "\n",
+    "     # Get the indices of the top 10 quotes with the highest similarity scores\n",
+    "    top_10_indices = np.argsort(weighted_similarities)[-5:]\n",
+    "\n",
+    "    # Randomly select one quote from the top 10\n",
+    "    selected_index = random.choice(top_10_indices)\n",
+    "\n",
+    "    # Return the most similar quote and its author\n",
+    "    return quotes[selected_index], authors[selected_index], emotions[selected_index]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Input sentence:  What should I do with my life?\n",
+      "Detected emotion: anger\n",
+      "Emotion score: tensor([[ 1.2938,  0.3162, -1.3738]])\n",
+      "Most similar quote: Don't let what you cannot do interfere with what you can do.\n",
+      "Author:  John Wooden\n",
+      "Quotes Emotion:  anger\n"
+     ]
+    }
+   ],
+   "source": [
+    "input_sentence = \"What should I do with my life?\"\n",
+    "detected_emotion, emotion_score = predict_emotion(input_sentence, label_encoder)\n",
+    "most_similar_quote, author, emotionQuotes = find_most_similar_quote(input_sentence, quotesDF, detected_emotion)\n",
+    "print('Input sentence: ', input_sentence)\n",
+    "print(\"Detected emotion:\", detected_emotion)\n",
+    "print(\"Emotion score:\", emotion_score)\n",
+    "print(\"Most similar quote:\", most_similar_quote)\n",
+    "print(\"Author: \", author)\n",
+    "print(\"Quotes Emotion: \", emotionQuotes)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

backend/requirements.txt

@@ -0,0 +1,11 @@
+Flask>=3.0,<4.0
+flask-cors>=4.0,<6.0
+gunicorn>=21.2,<23.0
+torch>=2.2,<3.0
+transformers>=4.40,<5.0
+sentence-transformers>=3.0,<4.0
+scikit-learn>=1.4,<2.0
+pandas>=2.0,<3.0
+numpy>=1.26,<3.0
+pytest>=8.0,<9.0
+

backend/runtime.txt

@@ -0,0 +1,2 @@
+python-3.11
+

backend/scripts/build_sbert_index.py

@@ -0,0 +1,30 @@
+#!/usr/bin/env python3
+"""Build and cache SentenceTransformer quote embeddings."""
+
+from pathlib import Path
+import sys
+
+BACKEND_DIR = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(BACKEND_DIR))
+
+from src.config import config
+from src.data_loader import load_data
+from src.emotion_classifier import EmotionClassifier
+from src.recommenders.sbert_dense import SbertDenseRecommender
+
+
+def main() -> int:
+    loaded = load_data(config)
+    classifier = EmotionClassifier(config, loaded.emotion_data["Emotion"].unique())
+    recommender = SbertDenseRecommender(config, loaded.quotes, classifier)
+    if not recommender.available:
+        print(f"Could not build dense index: {recommender.unavailable_reason}")
+        return 1
+    print(f"Built dense index for {len(loaded.quotes)} quotes.")
+    print(f"Artifacts directory: {config.artifacts_dir}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
+

backend/scripts/validate_models.py

@@ -0,0 +1,29 @@
+#!/usr/bin/env python3
+"""Validate model and strategy availability without starting a server."""
+
+from pathlib import Path
+import sys
+
+BACKEND_DIR = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(BACKEND_DIR))
+
+from app import create_app
+from src.config import config
+
+
+def main() -> int:
+    app = create_app(config)
+    state = app.extensions["quotify"]
+    print(f"{config.app_name} {config.version}")
+    print("Strategies:")
+    for strategy in state["recommenders"].values():
+        status = "available" if strategy.available else f"unavailable - {strategy.unavailable_reason}"
+        print(f"- {strategy.strategy_id}: {status}")
+    print("Emotion classifier:")
+    print(state["emotion_classifier"].status.to_dict())
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
+

backend/src/__init__.py

@@ -0,0 +1,2 @@
+"""Quotify backend package."""
+

backend/src/config.py

@@ -0,0 +1,57 @@
+import os
+from dataclasses import dataclass
+from pathlib import Path
+
+
+def _env_bool(name: str, default: bool) -> bool:
+    value = os.getenv(name)
+    if value is None:
+        return default
+    return value.strip().lower() in {"1", "true", "yes", "on"}
+
+
+@dataclass(frozen=True)
+class AppConfig:
+    app_name: str = "Quotify"
+    version: str = "2.0.0"
+    root_dir: Path = Path(__file__).resolve().parents[2]
+    backend_dir: Path = Path(__file__).resolve().parents[1]
+    default_strategy: str = os.getenv("QUOTIFY_DEFAULT_STRATEGY", "sbert_dense")
+    enable_sbert: bool = _env_bool("QUOTIFY_ENABLE_SBERT", True)
+    enable_cross_encoder: bool = _env_bool("QUOTIFY_ENABLE_CROSS_ENCODER", True)
+    emotion_boost: float = float(os.getenv("QUOTIFY_EMOTION_BOOST", "0.08"))
+    top_k_retrieve: int = int(os.getenv("QUOTIFY_TOP_K_RETRIEVE", "50"))
+    model_checkpoint_path: Path = Path(
+        os.getenv("QUOTIFY_MODEL_CHECKPOINT_PATH", "last_trained_model_checkpoint.pth")
+    )
+    sbert_model_name: str = os.getenv("QUOTIFY_SBERT_MODEL", "sentence-transformers/all-MiniLM-L6-v2")
+    cross_encoder_model_name: str = os.getenv(
+        "QUOTIFY_CROSS_ENCODER_MODEL", "cross-encoder/ms-marco-MiniLM-L6-v2"
+    )
+    port: int = int(os.getenv("PORT", "7860"))
+    cors_origins: str = os.getenv("QUOTIFY_CORS_ORIGINS", "")
+
+    @property
+    def emotion_dataset_path(self) -> Path:
+        return self.backend_dir / "(Preprocessed)Emotion_classify_Data(Labeled).csv"
+
+    @property
+    def quotes_dataset_path(self) -> Path:
+        return self.backend_dir / "(Preprocessed)quotes.csv"
+
+    @property
+    def artifacts_dir(self) -> Path:
+        return self.backend_dir / "artifacts"
+
+    @property
+    def frontend_build_dir(self) -> Path:
+        return self.root_dir / "frontend" / "build"
+
+    @property
+    def resolved_checkpoint_path(self) -> Path:
+        if self.model_checkpoint_path.is_absolute():
+            return self.model_checkpoint_path
+        return self.backend_dir / self.model_checkpoint_path
+
+
+config = AppConfig()

backend/src/data_loader.py

@@ -0,0 +1,39 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Optional
+
+import pandas as pd
+
+from .config import AppConfig
+from .utils import normalize_emotion
+
+
+@dataclass
+class LoadedData:
+    emotion_data: pd.DataFrame
+    quotes: pd.DataFrame
+
+
+def _read_csv(path: Path) -> pd.DataFrame:
+    if not path.exists():
+        raise FileNotFoundError(f"Required dataset is missing: {path}")
+    return pd.read_csv(path)
+
+
+def load_data(config: AppConfig, quotes_override: Optional[pd.DataFrame] = None) -> LoadedData:
+    emotion_data = _read_csv(config.emotion_dataset_path)
+    emotion_data = emotion_data.dropna(subset=["Comment", "Emotion"]).drop_duplicates(subset="Comment")
+    emotion_data["Emotion"] = emotion_data["Emotion"].map(normalize_emotion)
+
+    if quotes_override is None:
+        quotes = _read_csv(config.quotes_dataset_path)
+    else:
+        quotes = quotes_override.copy()
+
+    quotes = quotes.dropna(subset=["Quote", "Author", "emotion"]).drop_duplicates(subset="Quote")
+    quotes["emotion"] = quotes["emotion"].map(normalize_emotion)
+    quotes = quotes.reset_index(drop=True)
+    return LoadedData(emotion_data=emotion_data, quotes=quotes)
+

backend/src/emotion_classifier.py

@@ -0,0 +1,165 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Dict, Iterable, Optional
+
+from sklearn.preprocessing import LabelEncoder
+
+from .config import AppConfig
+from .utils import normalize_emotion
+
+
+@dataclass
+class EmotionClassifierStatus:
+    available: bool
+    backend: str
+    checkpoint_loaded: bool
+    fallback: bool
+    reason: Optional[str] = None
+
+    def to_dict(self) -> Dict[str, object]:
+        return {
+            "available": self.available,
+            "backend": self.backend,
+            "checkpoint_loaded": self.checkpoint_loaded,
+            "fallback": self.fallback,
+            "reason": self.reason,
+        }
+
+
+class EmotionClassifier:
+    def __init__(self, config: AppConfig, labels: Iterable[str]):
+        self.config = config
+        self.label_encoder = LabelEncoder()
+        self.label_encoder.fit([normalize_emotion(label) for label in labels])
+        self.device = None
+        self.tokenizer = None
+        self.model = None
+        self._status = EmotionClassifierStatus(
+            available=True,
+            backend="keyword_fallback",
+            checkpoint_loaded=False,
+            fallback=True,
+            reason="Fine-tuned BERT checkpoint was not loaded; using keyword fallback.",
+        )
+        self._load_bert_if_possible()
+
+    @property
+    def status(self) -> EmotionClassifierStatus:
+        return self._status
+
+    def _load_bert_if_possible(self) -> None:
+        checkpoint_path = self.config.resolved_checkpoint_path
+        if not checkpoint_path.exists():
+            return
+
+        try:
+            import torch
+            from transformers import BertForSequenceClassification, BertTokenizer
+        except Exception as exc:  # pragma: no cover - depends on optional runtime packages
+            self._status = EmotionClassifierStatus(
+                available=True,
+                backend="keyword_fallback",
+                checkpoint_loaded=False,
+                fallback=True,
+                reason=f"BERT dependencies unavailable: {exc}",
+            )
+            return
+
+        try:  # pragma: no cover - exercised only when checkpoint/model deps exist
+            self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+            self.tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+            self.model = BertForSequenceClassification.from_pretrained(
+                "bert-base-uncased", num_labels=len(self.label_encoder.classes_)
+            )
+            checkpoint = torch.load(checkpoint_path, map_location=self.device)
+            self.model.load_state_dict(checkpoint["model_state_dict"], strict=False)
+            self.model.to(self.device)
+            self.model.eval()
+            self._status = EmotionClassifierStatus(
+                available=True,
+                backend="fine_tuned_bert",
+                checkpoint_loaded=True,
+                fallback=False,
+            )
+        except Exception as exc:
+            self.tokenizer = None
+            self.model = None
+            self._status = EmotionClassifierStatus(
+                available=True,
+                backend="keyword_fallback",
+                checkpoint_loaded=False,
+                fallback=True,
+                reason=f"Failed to load BERT checkpoint: {exc}",
+            )
+
+    def predict(self, text: str) -> str:
+        if self.model is not None and self.tokenizer is not None:
+            return self._predict_with_bert(text)
+        return self._predict_with_keywords(text)
+
+    def _predict_with_bert(self, text: str) -> str:  # pragma: no cover - requires external checkpoint
+        import torch
+
+        inputs = self.tokenizer.encode_plus(
+            text,
+            add_special_tokens=True,
+            max_length=128,
+            padding="max_length",
+            truncation=True,
+            return_tensors="pt",
+        )
+        input_ids = inputs["input_ids"].to(self.device)
+        attention_mask = inputs["attention_mask"].to(self.device)
+
+        with torch.no_grad():
+            outputs = self.model(input_ids, attention_mask=attention_mask)
+
+        predicted_label = torch.argmax(outputs.logits, dim=1).item()
+        return normalize_emotion(self.label_encoder.inverse_transform([predicted_label])[0])
+
+    def _predict_with_keywords(self, text: str) -> str:
+        lowered = text.lower()
+        keyword_map = {
+            "anger": {
+                "angry",
+                "mad",
+                "hate",
+                "annoyed",
+                "furious",
+                "irritated",
+                "frustrated",
+                "upset",
+            },
+            "fear": {
+                "afraid",
+                "fear",
+                "scared",
+                "nervous",
+                "worried",
+                "anxious",
+                "panic",
+                "terrified",
+            },
+            "joy": {
+                "happy",
+                "joy",
+                "excited",
+                "grateful",
+                "hopeful",
+                "great",
+                "love",
+                "calm",
+            },
+        }
+        scores = {
+            emotion: sum(1 for keyword in keywords if keyword in lowered)
+            for emotion, keywords in keyword_map.items()
+        }
+        best_emotion, best_score = max(scores.items(), key=lambda item: item[1])
+        if best_score > 0:
+            return best_emotion
+        if "fear" in self.label_encoder.classes_:
+            return "fear"
+        return normalize_emotion(self.label_encoder.classes_[0])
+

backend/src/health.py

@@ -0,0 +1,19 @@
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Dict
+
+from .config import AppConfig
+
+
+def artifact_status(config: AppConfig, dense_recommender=None) -> Dict[str, object]:
+    status = {
+        "artifacts_dir": str(config.artifacts_dir),
+        "quote_embeddings.npy": (config.artifacts_dir / "quote_embeddings.npy").exists(),
+        "quote_metadata.csv": (config.artifacts_dir / "quote_metadata.csv").exists(),
+        "artifact_manifest.json": (config.artifacts_dir / "artifact_manifest.json").exists(),
+    }
+    if dense_recommender is not None and hasattr(dense_recommender, "artifact_status"):
+        status.update(dense_recommender.artifact_status())
+    return status
+

backend/src/recommenders/__init__.py

@@ -0,0 +1,10 @@
+from .cross_encoder_rerank import CrossEncoderRerankRecommender
+from .legacy_tfidf import LegacyTfidfRecommender
+from .sbert_dense import SbertDenseRecommender
+
+__all__ = [
+    "CrossEncoderRerankRecommender",
+    "LegacyTfidfRecommender",
+    "SbertDenseRecommender",
+]
+

backend/src/recommenders/base.py

@@ -0,0 +1,49 @@
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from typing import List
+
+import pandas as pd
+
+from ..config import AppConfig
+from ..emotion_classifier import EmotionClassifier
+from ..schemas import RecommendationResult, StrategyInfo
+
+
+class BaseRecommender(ABC):
+    strategy_id: str
+    label: str
+    description: str
+
+    def __init__(self, config: AppConfig, quotes: pd.DataFrame, emotion_classifier: EmotionClassifier):
+        self.config = config
+        self.quotes = quotes.reset_index(drop=True)
+        self.emotion_classifier = emotion_classifier
+        self.unavailable_reason = ""
+
+    @property
+    def available(self) -> bool:
+        return not self.unavailable_reason
+
+    def info(self) -> StrategyInfo:
+        return StrategyInfo(
+            id=self.strategy_id,
+            label=self.label,
+            description=self.description,
+            available=self.available,
+            reason=self.unavailable_reason or None,
+        )
+
+    @abstractmethod
+    def recommend(self, input_text: str, top_k: int = 5) -> RecommendationResult:
+        raise NotImplementedError
+
+    def _require_available(self) -> None:
+        if not self.available:
+            raise RuntimeError(f"{self.label} is unavailable: {self.unavailable_reason}")
+
+    @staticmethod
+    def _top_indices(scores, limit: int) -> List[int]:
+        limit = max(1, min(int(limit), len(scores)))
+        return list(scores.argsort()[-limit:][::-1])
+

backend/src/recommenders/cross_encoder_rerank.py

@@ -0,0 +1,102 @@
+from __future__ import annotations
+
+import numpy as np
+
+from .base import BaseRecommender
+from .sbert_dense import SbertDenseRecommender
+from ..schemas import Alternative, RecommendationResult, ScoreBreakdown
+from ..utils import normalize_vector, score_with_emotion_boost
+
+
+class CrossEncoderRerankRecommender(BaseRecommender):
+    strategy_id = "cross_encoder_rerank"
+    label = "AI Reranker"
+    description = (
+        "Retrieves candidates with dense embeddings, then reranks them with a Cross-Encoder "
+        "and emotion-aware scoring."
+    )
+
+    def __init__(self, config, quotes, emotion_classifier, dense_recommender: SbertDenseRecommender):
+        super().__init__(config, quotes, emotion_classifier)
+        self.dense_recommender = dense_recommender
+        self.cross_encoder = None
+        if not self.config.enable_cross_encoder:
+            self.unavailable_reason = "Cross-Encoder mode is disabled by QUOTIFY_ENABLE_CROSS_ENCODER."
+            return
+        if not dense_recommender.available:
+            self.unavailable_reason = "Dense retriever is unavailable, so reranking cannot run."
+            return
+        self._load_cross_encoder()
+
+    def _load_cross_encoder(self) -> None:
+        try:
+            from sentence_transformers import CrossEncoder
+        except Exception as exc:
+            self.unavailable_reason = f"CrossEncoder dependency unavailable: {exc}"
+            return
+        try:
+            self.cross_encoder = CrossEncoder(self.config.cross_encoder_model_name)
+        except Exception as exc:
+            self.unavailable_reason = f"Failed to load Cross-Encoder model: {exc}"
+
+    def recommend(self, input_text: str, top_k: int = 5) -> RecommendationResult:
+        self._require_available()
+        user_emotion = self.emotion_classifier.predict(input_text)
+        query = self.dense_recommender.model.encode(
+            [input_text], convert_to_numpy=True, normalize_embeddings=True
+        )[0].astype("float32")
+        query = normalize_vector(query)
+        semantic_scores = np.dot(self.dense_recommender.embeddings, query)
+        retrieve_count = max(top_k, min(self.config.top_k_retrieve, len(self.quotes)))
+        candidate_indices = self._top_indices(semantic_scores, retrieve_count)
+
+        pairs = [(input_text, str(self.quotes.iloc[idx]["Quote"])) for idx in candidate_indices]
+        cross_scores = np.array(self.cross_encoder.predict(pairs), dtype="float32")
+        if cross_scores.size:
+            min_score = float(cross_scores.min())
+            max_score = float(cross_scores.max())
+            if max_score > min_score:
+                normalized_cross = (cross_scores - min_score) / (max_score - min_score)
+            else:
+                normalized_cross = np.ones_like(cross_scores)
+        else:
+            normalized_cross = cross_scores
+
+        ranked = []
+        for position, idx in enumerate(candidate_indices):
+            semantic = float(semantic_scores[idx])
+            cross = float(normalized_cross[position])
+            blended = (0.35 * semantic) + (0.65 * cross)
+            final, boost = score_with_emotion_boost(
+                blended, self.quotes.iloc[idx]["emotion"], user_emotion, self.config.emotion_boost
+            )
+            ranked.append((idx, semantic, cross, boost, final))
+
+        ranked.sort(key=lambda item: item[-1], reverse=True)
+        selected_idx, semantic, cross, boost, final = ranked[0]
+        selected = self.quotes.iloc[selected_idx]
+        alternatives = [
+            Alternative(
+                quote=str(self.quotes.iloc[idx]["Quote"]),
+                author=str(self.quotes.iloc[idx]["Author"]),
+                quote_emotion=str(self.quotes.iloc[idx]["emotion"]),
+                final_score=round(float(item_final), 4),
+            )
+            for idx, _, _, _, item_final in ranked[1:top_k]
+        ]
+        return RecommendationResult(
+            quote=str(selected["Quote"]),
+            author=str(selected["Author"]),
+            emotion=user_emotion,
+            quote_emotion=str(selected["emotion"]),
+            strategy=self.strategy_id,
+            strategy_label=self.label,
+            scores=ScoreBreakdown(
+                semantic_score=round(float(semantic), 4),
+                cross_encoder_score=round(float(cross), 4),
+                emotion_boost=round(float(boost), 4),
+                final_score=round(float(final), 4),
+            ),
+            alternatives=alternatives,
+        )
+

backend/src/recommenders/legacy_tfidf.py

@@ -0,0 +1,72 @@
+from __future__ import annotations
+
+import numpy as np
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+
+from .base import BaseRecommender
+from ..schemas import Alternative, RecommendationResult, ScoreBreakdown
+from ..utils import score_with_emotion_boost
+
+
+class LegacyTfidfRecommender(BaseRecommender):
+    strategy_id = "legacy_tfidf_bert"
+    label = "Legacy BERT + TF-IDF"
+    description = (
+        "Original project method using fine-tuned BERT emotion classification, "
+        "TF-IDF cosine similarity, and emotion weighting."
+    )
+
+    def __init__(self, config, quotes, emotion_classifier):
+        super().__init__(config, quotes, emotion_classifier)
+        try:
+            self.vectorizer = TfidfVectorizer()
+            self.matrix = self.vectorizer.fit_transform(self.quotes["Quote"].tolist())
+        except Exception as exc:
+            self.unavailable_reason = f"Failed to build TF-IDF matrix: {exc}"
+
+    def recommend(self, input_text: str, top_k: int = 5) -> RecommendationResult:
+        self._require_available()
+        user_emotion = self.emotion_classifier.predict(input_text)
+        input_vec = self.vectorizer.transform([input_text])
+        semantic_scores = cosine_similarity(input_vec, self.matrix).flatten()
+
+        final_scores = []
+        boosts = []
+        for score, quote_emotion in zip(semantic_scores, self.quotes["emotion"]):
+            final, boost = score_with_emotion_boost(score, quote_emotion, user_emotion, self.config.emotion_boost)
+            final_scores.append(final)
+            boosts.append(boost)
+
+        final_scores = np.array(final_scores)
+        boosts = np.array(boosts)
+        indices = self._top_indices(final_scores, max(top_k, 1))
+        selected_idx = indices[0]
+        selected = self.quotes.iloc[selected_idx]
+
+        alternatives = [
+            Alternative(
+                quote=str(self.quotes.iloc[idx]["Quote"]),
+                author=str(self.quotes.iloc[idx]["Author"]),
+                quote_emotion=str(self.quotes.iloc[idx]["emotion"]),
+                final_score=round(float(final_scores[idx]), 4),
+            )
+            for idx in indices[1:top_k]
+        ]
+
+        return RecommendationResult(
+            quote=str(selected["Quote"]),
+            author=str(selected["Author"]),
+            emotion=user_emotion,
+            quote_emotion=str(selected["emotion"]),
+            strategy=self.strategy_id,
+            strategy_label=self.label,
+            scores=ScoreBreakdown(
+                semantic_score=round(float(semantic_scores[selected_idx]), 4),
+                cross_encoder_score=None,
+                emotion_boost=round(float(boosts[selected_idx]), 4),
+                final_score=round(float(final_scores[selected_idx]), 4),
+            ),
+            alternatives=alternatives,
+        )
+

backend/src/recommenders/sbert_dense.py

@@ -0,0 +1,130 @@
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import numpy as np
+
+from .base import BaseRecommender
+from ..schemas import Alternative, RecommendationResult, ScoreBreakdown
+from ..utils import normalize_rows, normalize_vector, score_with_emotion_boost, write_json
+
+
+class SbertDenseRecommender(BaseRecommender):
+    strategy_id = "sbert_dense"
+    label = "Dense Semantic Search"
+    description = "Uses SentenceTransformer embeddings for meaning-based quote retrieval with emotion weighting."
+
+    def __init__(self, config, quotes, emotion_classifier):
+        super().__init__(config, quotes, emotion_classifier)
+        self.model = None
+        self.embeddings = None
+        self.embedding_path = self.config.artifacts_dir / "quote_embeddings.npy"
+        self.metadata_path = self.config.artifacts_dir / "quote_metadata.csv"
+        self.manifest_path = self.config.artifacts_dir / "artifact_manifest.json"
+        self._load_model_and_artifacts()
+
+    def _load_model_and_artifacts(self) -> None:
+        if not self.config.enable_sbert:
+            self.unavailable_reason = "Dense retrieval is disabled by QUOTIFY_ENABLE_SBERT."
+            return
+        try:
+            from sentence_transformers import SentenceTransformer
+        except Exception as exc:
+            self.unavailable_reason = f"sentence-transformers is unavailable: {exc}"
+            return
+
+        try:
+            self.model = SentenceTransformer(self.config.sbert_model_name)
+            self.config.artifacts_dir.mkdir(parents=True, exist_ok=True)
+            if self.embedding_path.exists():
+                embeddings = np.load(self.embedding_path)
+                if embeddings.shape[0] == len(self.quotes):
+                    self.embeddings = normalize_rows(embeddings.astype("float32"))
+                else:
+                    self.embeddings = self._build_embeddings()
+            else:
+                self.embeddings = self._build_embeddings()
+        except Exception as exc:
+            self.unavailable_reason = f"Failed to load dense retrieval model/artifacts: {exc}"
+
+    def _build_embeddings(self) -> np.ndarray:
+        embeddings = self.model.encode(
+            self.quotes["Quote"].tolist(),
+            batch_size=64,
+            convert_to_numpy=True,
+            show_progress_bar=False,
+            normalize_embeddings=True,
+        ).astype("float32")
+        np.save(self.embedding_path, embeddings)
+        self.quotes.to_csv(self.metadata_path, index=False)
+        write_json(
+            self.manifest_path,
+            {
+                "model": self.config.sbert_model_name,
+                "quote_count": int(len(self.quotes)),
+                "embedding_file": self.embedding_path.name,
+                "metadata_file": self.metadata_path.name,
+            },
+        )
+        return embeddings
+
+    def recommend(self, input_text: str, top_k: int = 5) -> RecommendationResult:
+        self._require_available()
+        user_emotion = self.emotion_classifier.predict(input_text)
+        query = self.model.encode([input_text], convert_to_numpy=True, normalize_embeddings=True)[0].astype("float32")
+        query = normalize_vector(query)
+        semantic_scores = np.dot(self.embeddings, query)
+        return self._format_result(input_text, user_emotion, semantic_scores, top_k)
+
+    def _format_result(self, input_text: str, user_emotion: str, semantic_scores, top_k: int) -> RecommendationResult:
+        final_scores = []
+        boosts = []
+        for score, quote_emotion in zip(semantic_scores, self.quotes["emotion"]):
+            final, boost = score_with_emotion_boost(float(score), quote_emotion, user_emotion, self.config.emotion_boost)
+            final_scores.append(final)
+            boosts.append(boost)
+
+        final_scores = np.array(final_scores)
+        boosts = np.array(boosts)
+        indices = self._top_indices(final_scores, max(top_k, 1))
+        selected_idx = indices[0]
+        selected = self.quotes.iloc[selected_idx]
+        alternatives = [
+            Alternative(
+                quote=str(self.quotes.iloc[idx]["Quote"]),
+                author=str(self.quotes.iloc[idx]["Author"]),
+                quote_emotion=str(self.quotes.iloc[idx]["emotion"]),
+                final_score=round(float(final_scores[idx]), 4),
+            )
+            for idx in indices[1:top_k]
+        ]
+        return RecommendationResult(
+            quote=str(selected["Quote"]),
+            author=str(selected["Author"]),
+            emotion=user_emotion,
+            quote_emotion=str(selected["emotion"]),
+            strategy=self.strategy_id,
+            strategy_label=self.label,
+            scores=ScoreBreakdown(
+                semantic_score=round(float(semantic_scores[selected_idx]), 4),
+                cross_encoder_score=None,
+                emotion_boost=round(float(boosts[selected_idx]), 4),
+                final_score=round(float(final_scores[selected_idx]), 4),
+            ),
+            alternatives=alternatives,
+        )
+
+    def artifact_status(self) -> dict:
+        manifest = {}
+        if self.manifest_path.exists():
+            try:
+                manifest = json.loads(self.manifest_path.read_text(encoding="utf-8"))
+            except Exception:
+                manifest = {}
+        return {
+            "quote_embeddings.npy": self.embedding_path.exists(),
+            "quote_metadata.csv": self.metadata_path.exists(),
+            "artifact_manifest.json": self.manifest_path.exists(),
+            "manifest": manifest,
+        }

backend/src/schemas.py

@@ -0,0 +1,79 @@
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional
+
+
+@dataclass
+class ScoreBreakdown:
+    semantic_score: Optional[float]
+    cross_encoder_score: Optional[float]
+    emotion_boost: float
+    final_score: float
+
+    def to_dict(self) -> Dict[str, Optional[float]]:
+        return {
+            "semantic_score": self.semantic_score,
+            "cross_encoder_score": self.cross_encoder_score,
+            "emotion_boost": self.emotion_boost,
+            "final_score": self.final_score,
+        }
+
+
+@dataclass
+class Alternative:
+    quote: str
+    author: str
+    quote_emotion: str
+    final_score: float
+
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "quote": self.quote,
+            "author": self.author,
+            "quote_emotion": self.quote_emotion,
+            "final_score": self.final_score,
+        }
+
+
+@dataclass
+class RecommendationResult:
+    quote: str
+    author: str
+    emotion: str
+    quote_emotion: str
+    strategy: str
+    strategy_label: str
+    scores: ScoreBreakdown
+    alternatives: List[Alternative] = field(default_factory=list)
+
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "quote": self.quote,
+            "author": self.author,
+            "emotion": self.emotion,
+            "quote_emotion": self.quote_emotion,
+            "strategy": self.strategy,
+            "strategy_label": self.strategy_label,
+            "scores": self.scores.to_dict(),
+            "alternatives": [item.to_dict() for item in self.alternatives],
+        }
+
+
+@dataclass
+class StrategyInfo:
+    id: str
+    label: str
+    description: str
+    available: bool
+    reason: Optional[str] = None
+
+    def to_dict(self) -> Dict[str, Any]:
+        payload = {
+            "id": self.id,
+            "label": self.label,
+            "description": self.description,
+            "available": self.available,
+        }
+        if self.reason:
+            payload["reason"] = self.reason
+        return payload
+

backend/src/utils.py

@@ -0,0 +1,35 @@
+from __future__ import annotations
+
+import json
+from pathlib import Path
+from typing import Any, Dict
+
+import numpy as np
+
+
+def normalize_emotion(value: Any) -> str:
+    return str(value or "").strip().lower()
+
+
+def normalize_rows(scores: np.ndarray) -> np.ndarray:
+    norms = np.linalg.norm(scores, axis=1, keepdims=True)
+    norms[norms == 0] = 1.0
+    return scores / norms
+
+
+def normalize_vector(vector: np.ndarray) -> np.ndarray:
+    norm = np.linalg.norm(vector)
+    if norm == 0:
+        return vector
+    return vector / norm
+
+
+def score_with_emotion_boost(score: float, quote_emotion: str, user_emotion: str, boost: float) -> tuple[float, float]:
+    applied_boost = boost if normalize_emotion(quote_emotion) == normalize_emotion(user_emotion) else 0.0
+    return float(score + applied_boost), float(applied_boost)
+
+
+def write_json(path: Path, payload: Dict[str, Any]) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(json.dumps(payload, indent=2), encoding="utf-8")
+

backend/tests/test_api.py

@@ -0,0 +1,101 @@
+import os
+from dataclasses import replace
+
+import pandas as pd
+import pytest
+
+os.environ["QUOTIFY_ENABLE_SBERT"] = "false"
+os.environ["QUOTIFY_ENABLE_CROSS_ENCODER"] = "false"
+
+from app import create_app
+from src.config import config
+
+
+@pytest.fixture()
+def client():
+    quotes = pd.DataFrame(
+        [
+            {
+                "Quote": "Courage is resistance to fear, mastery of fear, not absence of fear.",
+                "Author": "Mark Twain",
+                "emotion": "fear",
+            },
+            {
+                "Quote": "For every minute you are angry you lose sixty seconds of happiness.",
+                "Author": "Ralph Waldo Emerson",
+                "emotion": "anger",
+            },
+            {
+                "Quote": "Joy is the simplest form of gratitude.",
+                "Author": "Karl Barth",
+                "emotion": "joy",
+            },
+        ]
+    )
+    test_config = replace(
+        config,
+        enable_sbert=False,
+        enable_cross_encoder=False,
+        default_strategy="legacy_tfidf_bert",
+    )
+    app = create_app(test_config, quotes_override=quotes)
+    app.config.update(TESTING=True)
+    return app.test_client()
+
+
+def test_health_returns_200(client):
+    response = client.get("/health")
+    assert response.status_code == 200
+    payload = response.get_json()
+    assert payload["app"] == "Quotify"
+    assert "legacy_tfidf_bert" in payload["available_strategies"]
+    assert payload["models"]["emotion_classifier"]["available"] is True
+
+
+def test_strategies_returns_all_strategy_ids(client):
+    response = client.get("/strategies")
+    assert response.status_code == 200
+    ids = {item["id"] for item in response.get_json()}
+    assert ids == {"legacy_tfidf_bert", "sbert_dense", "cross_encoder_rerank"}
+
+
+def test_get_quote_validates_missing_input(client):
+    response = client.post("/get_quote", json={})
+    assert response.status_code == 400
+    assert response.get_json()["error"] == "inputText is required."
+
+
+def test_each_available_strategy_returns_valid_quote(client):
+    strategies = client.get("/strategies").get_json()
+    for strategy in strategies:
+        if not strategy["available"]:
+            continue
+        response = client.post(
+            "/get_quote",
+            json={"inputText": "I am nervous about tomorrow", "strategy": strategy["id"], "topK": 2},
+        )
+        assert response.status_code == 200
+        payload = response.get_json()
+        assert payload["quote"]
+        assert payload["author"]
+        assert payload["strategy"] == strategy["id"]
+        assert "final_score" in payload["scores"]
+
+
+def test_compare_returns_available_strategy_results(client):
+    response = client.post("/compare", json={"inputText": "I am happy today", "topK": 2})
+    assert response.status_code == 200
+    payload = response.get_json()
+    assert payload["results"]
+    assert payload["results"][0]["strategy"] == "legacy_tfidf_bert"
+    assert isinstance(payload["errors"], list)
+
+
+def test_missing_checkpoint_does_not_crash_backend(client):
+    response = client.get("/health")
+    assert response.status_code == 200
+    classifier = response.get_json()["models"]["emotion_classifier"]
+    assert classifier["available"] is True
+    assert classifier["checkpoint_loaded"] is False
+    assert classifier["fallback"] is True
+

backend/tests/test_health.py

@@ -0,0 +1,3 @@
+def test_placeholder_health_test_module_exists():
+    assert True
+

backend/tests/test_recommenders.py

@@ -0,0 +1,25 @@
+from dataclasses import replace
+
+import pandas as pd
+
+from src.config import config
+from src.emotion_classifier import EmotionClassifier
+from src.recommenders.legacy_tfidf import LegacyTfidfRecommender
+
+
+def test_legacy_recommender_returns_quote_from_fixture():
+    quotes = pd.DataFrame(
+        [
+            {"Quote": "Be happy with this moment.", "Author": "Unknown", "emotion": "joy"},
+            {"Quote": "Fear can be a teacher.", "Author": "Unknown", "emotion": "fear"},
+        ]
+    )
+    classifier = EmotionClassifier(replace(config, enable_sbert=False), ["joy", "fear", "anger"])
+    recommender = LegacyTfidfRecommender(replace(config, emotion_boost=0.08), quotes, classifier)
+
+    result = recommender.recommend("I am happy and grateful", top_k=2)
+
+    assert result.quote
+    assert result.author
+    assert result.strategy == "legacy_tfidf_bert"
+    assert result.scores.final_score >= result.scores.semantic_score

docker-compose.yml

@@ -0,0 +1,12 @@
+services:
+  quotify:
+    build: .
+    ports:
+      - "7860:7860"
+    environment:
+      PORT: 7860
+      QUOTIFY_ENABLE_CROSS_ENCODER: "false"
+      QUOTIFY_DEFAULT_STRATEGY: legacy_tfidf_bert
+    volumes:
+      - ./backend/artifacts:/app/backend/artifacts
+

frontend/package.json

@@ -0,0 +1,48 @@
+{
+  "name": "quotify",
+  "version": "0.1.0",
+  "private": true,
+  "proxy": "http://127.0.0.1:7860",
+  "dependencies": {
+    "@testing-library/jest-dom": "^5.17.0",
+    "@testing-library/react": "^13.4.0",
+    "@testing-library/user-event": "^13.5.0",
+    "axios": "^1.7.2",
+    "react": "^18.3.1",
+    "react-dom": "^18.3.1",
+    "react-icons": "^5.2.1",
+    "react-router-dom": "^6.23.1",
+    "react-scripts": "5.0.1",
+    "web-vitals": "^2.1.4"
+  },
+  "scripts": {
+    "start": "react-scripts start",
+    "build": "react-scripts build",
+    "test": "react-scripts test",
+    "eject": "react-scripts eject"
+  },
+  "eslintConfig": {
+    "extends": [
+      "react-app",
+      "react-app/jest"
+    ]
+  },
+  "browserslist": {
+    "production": [
+      ">0.2%",
+      "not dead",
+      "not op_mini all"
+    ],
+    "development": [
+      "last 1 chrome version",
+      "last 1 firefox version",
+      "last 1 safari version"
+    ]
+  },
+  "devDependencies": {
+    "@babel/plugin-proposal-private-property-in-object": "^7.21.11",
+    "autoprefixer": "^10.4.19",
+    "postcss": "^8.4.38",
+    "tailwindcss": "^3.4.4"
+  }
+}

frontend/public/index.html

@@ -0,0 +1,46 @@
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+    <link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=Work+Sans:wght@400;500;600;700&display=swap">
+    <link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=Overpass:wght@400;600;700&display=swap">
+    <link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=Merriweather:wght@400;700&display=swap">
+    <meta charset="utf-8" />
+    <link rel="icon" href="./quotify.png" />
+    <meta name="viewport" content="width=device-width, initial-scale=1" />
+    <meta name="theme-color" content="#000000" />
+    <meta
+      name="description"
+      content="Web site created using create-react-app"
+    />
+    <link rel="apple-touch-icon" href="%PUBLIC_URL%/logo192.png" />
+    <!--
+      manifest.json provides metadata used when your web app is installed on a
+      user's mobile device or desktop. See https://developers.google.com/web/fundamentals/web-app-manifest/
+    -->
+    <link rel="manifest" href="%PUBLIC_URL%/manifest.json" />
+    <!--
+      Notice the use of %PUBLIC_URL% in the tags above.
+      It will be replaced with the URL of the `public` folder during the build.
+      Only files inside the `public` folder can be referenced from the HTML.
+
+      Unlike "/favicon.ico" or "favicon.ico", "%PUBLIC_URL%/favicon.ico" will
+      work correctly both with client-side routing and a non-root public URL.
+      Learn how to configure a non-root public URL by running `npm run build`.
+    -->
+    <title>Quotify</title>
+  </head>
+  <body>
+    <noscript>You need to enable JavaScript to run this app.</noscript>
+    <div id="root"></div>
+    <!--
+      This HTML file is a template.
+      If you open it directly in the browser, you will see an empty page.
+
+      You can add webfonts, meta tags, or analytics to this file.
+      The build step will place the bundled scripts into the <body> tag.
+
+      To begin the development, run `npm start` or `yarn start`.
+      To create a production bundle, use `npm run build` or `yarn build`.
+    -->
+  </body>
+</html>

frontend/public/manifest.json

@@ -0,0 +1,25 @@
+{
+  "short_name": "React App",
+  "name": "Create React App Sample",
+  "icons": [
+    {
+      "src": "favicon.ico",
+      "sizes": "64x64 32x32 24x24 16x16",
+      "type": "image/x-icon"
+    },
+    {
+      "src": "logo192.png",
+      "type": "image/png",
+      "sizes": "192x192"
+    },
+    {
+      "src": "logo512.png",
+      "type": "image/png",
+      "sizes": "512x512"
+    }
+  ],
+  "start_url": ".",
+  "display": "standalone",
+  "theme_color": "#000000",
+  "background_color": "#ffffff"
+}

frontend/public/robots.txt

@@ -0,0 +1,3 @@
+# https://www.robotstxt.org/robotstxt.html
+User-agent: *
+Disallow:

frontend/src/App.css

@@ -0,0 +1,33 @@
+.page {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: center;
+  height: 100vh;
+  background-color: #004d40;
+  color: #fff;
+}
+
+input {
+  padding: 10px;
+  margin-top: 20px;
+  border: none;
+  border-radius: 5px;
+}
+
+button {
+  margin-top: 20px;
+  padding: 10px 20px;
+  border: none;
+  border-radius: 5px;
+  background-color: #ff5722;
+  color: #fff;
+}
+
+.quote-box {
+  margin-top: 20px;
+  padding: 20px;
+  background-color: #ff5722;
+  border-radius: 5px;
+  text-align: center;
+}

frontend/src/App.js

@@ -0,0 +1,28 @@
+import React, { useState } from 'react';
+import Main from './components/MainPage';
+import Login from './components/Login';
+import './index.css';
+
+const App = () => {
+  const [page, setPage] = useState('login');
+  const [userName, setUserName] = useState('');
+
+  const handleLogin = (name) => {
+    setUserName(name);
+    setPage('main');
+  };
+
+  const handlePowerButtonClick = () => {
+    setPage('login');
+    setUserName('');
+  };
+
+  return (
+    <div className="app">
+      {page === 'login' && <Login onLogin={handleLogin} />}
+      {page === 'main' && <Main userName={userName} onPowerButtonClick={handlePowerButtonClick} />}
+    </div>
+  );
+};
+
+export default App;

frontend/src/App.test.js

@@ -0,0 +1,8 @@
+import { render, screen } from '@testing-library/react';
+import App from './App';
+
+test('renders learn react link', () => {
+  render(<App />);
+  const linkElement = screen.getByText(/learn react/i);
+  expect(linkElement).toBeInTheDocument();
+});

frontend/src/components/Login.css

@@ -0,0 +1,60 @@
+.login-container {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: center;
+  height: 100vh;
+  background-color: #014737;
+  color: white;
+}
+
+.login-header {
+  font-size: 2.5rem;
+  margin-bottom: 1rem;
+}
+
+.login-input {
+  padding: 0.5rem 1rem;
+  width: 24rem;
+  border-radius: 9999px;
+  color: black;
+  font-size: 1.25rem;
+  border: none;
+  outline: none;
+  margin-bottom: 1rem;
+}
+
+.login-button {
+  padding: 0.5rem 1rem;
+  font-size: 1rem;
+  border: none;
+  border-radius: 9999px;
+  background-color: #C7DFC5;
+  color: #014737;
+  cursor: pointer;
+  transition: transform 0.2s, color 0.2s;
+  font-weight: 700;
+}
+
+.login-input.error {
+  border: 2px solid red;
+}
+
+.error-text {
+  position: absolute;
+  color: rgb(255, 65, 65);
+  font-size: 0.875rem;
+  margin-top: -1.45rem;
+  margin-bottom: 1rem;
+  font-weight: 900;
+}
+
+.login-button:hover {
+  transform: scale(1.05);
+}
+
+.login-button:active {
+  transform: scale(0.95);
+  background-color: #C44900;
+  color: #ffffff;
+}

frontend/src/components/Login.js

@@ -0,0 +1,50 @@
+import React, { useState } from 'react';
+import './Login.css'; // Import the CSS file
+
+const Login = ({ onLogin }) => {
+  const [name, setName] = useState('');
+  const [error, setError] = useState('');
+
+  const handleLogin = () => {
+    if (name.trim()) {
+      onLogin(name);
+      setName(''); // Clear the input field
+    }
+  };
+
+  const handleKeyDown = (e) => {
+    if (e.key === 'Enter') {
+      handleLogin();
+    }
+  };
+
+  const handleChange = (e) => {
+    const value = e.target.value;
+    if (value.length <= 10) {
+      setName(value);
+      setError('');
+    } else {
+      setError('Nickname cannot be more than 10 characters.');
+    }
+  };
+
+  return (
+    <div className="login-container font-worksans">
+      <h1 className="login-header">Welcome to <strong className='font-merriweather'>Quotify</strong></h1>
+      <input 
+        type="text" 
+        className={`login-input ${error ? 'error' : ''}`} 
+        placeholder="Enter your nickname" 
+        value={name} 
+        onChange={handleChange} 
+        onKeyDown={handleKeyDown}
+      />
+      {error && <p className="error-text">{error}</p>}
+      <button className="login-button" onClick={handleLogin}>
+        Login
+      </button>
+    </div>
+  );
+};
+
+export default Login;

frontend/src/components/MainPage.css

@@ -0,0 +1,312 @@
+.page-container {
+  min-height: 100vh;
+  background: #014737;
+  color: white;
+}
+
+.main-shell {
+  width: min(1120px, calc(100% - 32px));
+  margin: 0 auto;
+  padding: 104px 0 48px;
+}
+
+.hero-panel {
+  display: flex;
+  flex-direction: column;
+  gap: 20px;
+}
+
+.text-container {
+  display: flex;
+  flex-direction: column;
+  align-items: flex-start;
+}
+
+.header-text {
+  font-size: clamp(2.5rem, 7vw, 4.8rem);
+  line-height: 1;
+  margin: 0 0 4px;
+}
+
+.subheader-text {
+  font-size: clamp(1.35rem, 3vw, 2rem);
+  letter-spacing: 0;
+  margin: 0;
+  color: #f3e1b6;
+}
+
+.strategy-control {
+  display: grid;
+  grid-template-columns: repeat(3, minmax(0, 1fr));
+  gap: 8px;
+  background: rgba(255, 255, 255, 0.08);
+  border: 1px solid rgba(255, 255, 255, 0.14);
+  border-radius: 8px;
+  padding: 8px;
+}
+
+.strategy-button,
+.compare-button {
+  min-height: 44px;
+  border: 0;
+  border-radius: 6px;
+  font-weight: 700;
+  cursor: pointer;
+  transition: transform 160ms ease, background 160ms ease, color 160ms ease;
+}
+
+.strategy-button {
+  background: transparent;
+  color: #f6f2e9;
+}
+
+.strategy-button.active {
+  background: #f3e1b6;
+  color: #014737;
+}
+
+.strategy-button:disabled {
+  color: rgba(255, 255, 255, 0.36);
+  cursor: not-allowed;
+}
+
+.strategy-card {
+  display: grid;
+  gap: 8px;
+  background: #c7dfc5;
+  color: #014737;
+  border-radius: 8px;
+  padding: 16px;
+}
+
+.strategy-card span,
+.score-grid span,
+.alternatives span {
+  display: block;
+  font-size: 0.76rem;
+  font-weight: 800;
+  letter-spacing: 0.08em;
+  text-transform: uppercase;
+  opacity: 0.72;
+}
+
+.strategy-card strong {
+  display: block;
+  font-size: 1.1rem;
+}
+
+.strategy-card p {
+  margin: 0;
+  line-height: 1.5;
+}
+
+.strategy-warning {
+  color: #8a2c17;
+  font-weight: 700;
+}
+
+.input-container {
+  display: flex;
+  align-items: center;
+  position: relative;
+}
+
+.input-field {
+  width: 100%;
+  min-height: 56px;
+  padding: 0.75rem 4rem 0.75rem 1.2rem;
+  border-radius: 999px;
+  background-color: #f3e1b6;
+  color: #10231e;
+  font-size: 1.1rem;
+  border: 2px solid transparent;
+  outline: none;
+}
+
+.input-field:focus {
+  border-color: #c7dfc5;
+}
+
+.submit-button {
+  position: absolute;
+  right: 8px;
+  width: 44px;
+  height: 44px;
+  border-radius: 50%;
+  background: #014737;
+  border: none;
+  cursor: pointer;
+  color: white;
+  font-size: 1.3rem;
+  font-weight: 800;
+}
+
+.submit-button:disabled {
+  cursor: not-allowed;
+  opacity: 0.5;
+}
+
+.action-row {
+  display: flex;
+  justify-content: flex-end;
+}
+
+.compare-button {
+  padding: 0 18px;
+  background: #ffffff;
+  color: #014737;
+}
+
+.compare-button:disabled {
+  cursor: not-allowed;
+  opacity: 0.64;
+}
+
+.error-state,
+.empty-state,
+.compare-errors {
+  border-radius: 8px;
+  padding: 14px 16px;
+  line-height: 1.5;
+}
+
+.error-state {
+  background: #ffe1d7;
+  color: #84230e;
+}
+
+.empty-state {
+  background: rgba(255, 255, 255, 0.1);
+  color: #f6f2e9;
+}
+
+.results-section {
+  display: grid;
+  grid-template-columns: minmax(0, 1fr);
+  gap: 16px;
+  margin-top: 24px;
+}
+
+.results-section.compare-layout {
+  grid-template-columns: repeat(3, minmax(0, 1fr));
+}
+
+.result-card {
+  display: flex;
+  flex-direction: column;
+  gap: 14px;
+  background: #f6f2e9;
+  color: #014737;
+  border-radius: 8px;
+  padding: 20px;
+  min-width: 0;
+  box-shadow: 0 18px 40px rgba(0, 0, 0, 0.22);
+}
+
+.result-card-header {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  gap: 10px;
+}
+
+.strategy-pill,
+.emotion-pill {
+  display: inline-flex;
+  align-items: center;
+  min-height: 28px;
+  border-radius: 999px;
+  padding: 0 10px;
+  font-size: 0.8rem;
+  font-weight: 800;
+}
+
+.strategy-pill {
+  background: #014737;
+  color: #ffffff;
+}
+
+.emotion-pill {
+  background: #c7dfc5;
+  color: #014737;
+}
+
+.quote-text {
+  font-family: Merriweather, serif;
+  font-size: clamp(1.05rem, 2vw, 1.3rem);
+  line-height: 1.55;
+  margin: 0;
+}
+
+.quote-author {
+  font-family: Merriweather, serif;
+  margin: 0;
+  font-weight: 700;
+}
+
+.score-grid {
+  display: grid;
+  grid-template-columns: repeat(4, minmax(0, 1fr));
+  gap: 8px;
+}
+
+.score-grid div {
+  background: #e5ecd8;
+  border-radius: 6px;
+  padding: 10px;
+}
+
+.score-grid strong {
+  display: block;
+  margin-top: 4px;
+}
+
+.rerank-score {
+  background: #f3e1b6;
+  border-radius: 6px;
+  padding: 10px;
+}
+
+.alternatives {
+  display: grid;
+  gap: 8px;
+  border-top: 1px solid rgba(1, 71, 55, 0.16);
+  padding-top: 12px;
+}
+
+.alternatives p {
+  margin: 0;
+  font-size: 0.92rem;
+  line-height: 1.4;
+}
+
+.compare-errors {
+  margin-top: 16px;
+  background: rgba(255, 225, 215, 0.9);
+  color: #84230e;
+}
+
+.compare-errors p {
+  margin: 0 0 6px;
+}
+
+@media (max-width: 880px) {
+  .main-shell {
+    padding-top: 88px;
+  }
+
+  .strategy-control,
+  .results-section.compare-layout,
+  .score-grid {
+    grid-template-columns: 1fr;
+  }
+
+  .strategy-button {
+    text-align: center;
+  }
+
+  .result-card-header {
+    align-items: flex-start;
+    flex-direction: column;
+  }
+}