Upload 4 files

data.csv

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+Patient_Name,Country,Disease,CUI,Snomed,Oxygen_Rate,Med_Type,Admission_Date
+John,India,Severe Fever,CUI012345,SNO1234,92,Commercial,04-04-2020
+Surya,USA,Cancer,CUI012346,SNO1235,98,Commercial,23-09-2020
+Ajith,Russia,Diabetes,CUI012347,SNO1236,96,Medicare,04-03-2020
+Aman,India,Severe Fever,CUI012348,SNO1237,98,Commercial,07-03-2020
+Ben,USA,Severe Fever,CUI012349,SNO1238,89,Medicare,15-01-2020
+Ravi,India,Edema,CUI012350,SNO1239,99,Medicaid,05-05-2020
+Jitu,USA,Alzheimer,CUI012351,SNO1240,95,Medicaid,02-12-2020
+Anjali,Russia,Alzheimer,CUI012352,SNO1241,94,Medicare,22-04-2020
+Priya,India,Cardiac Arrest,CUI012353,SNO1242,94,Medicaid,11-08-2020
+Dinesh,USA,Pneumonia,CUI012354,SNO1243,93,Medicare,02-09-2020

pasta.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Fri May 26 14:07:22 2023
+
+@author: vibin
+"""
+
+import streamlit as st
+from pandasql import sqldf
+import pandas as pd
+import re
+from typing import List
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+import re
+
+
+
+
+
+
+### Main
+
+nav = st.sidebar.radio("Navigation",["TAPAS","Text2SQL"])
+if nav == "TAPAS":
+
+    
+    
+    col1 , col2, col3 = st.columns(3)
+    col2.title("TAPAS")
+    
+    col3 , col4 = st.columns([3,12])
+    col4.text("Tabular Data Text Extraction using text")
+    
+    table = pd.read_csv("data.csv")
+    table = table.astype(str)
+    st.text("DataSet - ")
+    st.dataframe(table,width=3000,height= 400)
+    
+    st.title("")
+    
+    lst_q = ["Which country has low medicare","Who are the patients from india","Who are the patients from india","Patients who have Edema","CUI code for diabetes patients","Patients having oxygen less than 94 but 91"]
+   
+    v2 = st.selectbox("Choose your text",lst_q,index = 0)
+
+    st.title("")
+    
+    sql_txt = st.text_area("TAPAS Input",v2)
+    
+    if st.button("Predict"):
+        tqa = pipeline(task="table-question-answering", 
+                   model="google/tapas-base-finetuned-wtq")
+        
+        txt_sql = tqa(table=table, query=sql_txt)["answer"]
+        st.text("Output - ") 
+        st.success(f"{txt_sql}")
+        # st.write(all_students)
+    
+    
+    
+elif nav == "Text2SQL":
+    
+    ### Function
+
+    def prepare_input(question: str, table: List[str]):
+        table_prefix = "table:"
+        question_prefix = "question:"
+        join_table = ",".join(table)
+        inputs = f"{question_prefix} {question} {table_prefix} {join_table}"
+        input_ids = tokenizer(inputs, max_length=512, return_tensors="pt").input_ids
+        return input_ids
+
+    def inference(question: str, table: List[str]) -> str:
+        input_data = prepare_input(question=question, table=table)
+        input_data = input_data.to(model.device)
+        outputs = model.generate(inputs=input_data, num_beams=10, top_k=10, max_length=700)
+        result = tokenizer.decode(token_ids=outputs[0], skip_special_tokens=True)
+        return result
+    
+    
+    col1 , col2, col3 = st.columns(3)
+    col2.title("Text2SQL")
+    
+    col3 , col4 = st.columns([1,20])
+    col4.text("Text will be converted to SQL Query and can extract the data from DataSet")
+    
+    # Import Data
+    
+    df_qna = pd.read_csv("qnacsv.csv", encoding= 'unicode_escape')
+    
+    st.title("")
+    
+    st.text("DataSet - ")
+    st.dataframe(df_qna,width=3000,height= 500)
+    
+    st.title("")
+    
+    lst_q = ["what interface is measure indicator code = 72_HR_ABX and version is 1 and source is TD", "get class code with measure = 72_HR_ABX", "get sum of version for Class_Code is Antibiotic Stewardship", "what interface is measure indicator code = 72_HR_ABX"]
+    v2 = st.selectbox("Choose your text",lst_q,index = 0)
+
+    st.title("")
+    
+    
+    sql_txt = st.text_area("Text for SQL Conversion",v2)
+    
+    
+    if st.button("Predict"):
+        tokenizer = AutoTokenizer.from_pretrained("juierror/flan-t5-text2sql-with-schema")
+        model = AutoModelForSeq2SeqLM.from_pretrained("juierror/flan-t5-text2sql-with-schema")
+        
+        # text = "what interface is measure indicator code = 72_HR_ABX and version is 1 and source is TD"
+        table_name = "df_qna"
+        table_col = ["Type","Class_Code", "Version","Measure_Indicator_Code","Measure_Indicator","Name","Description_Definition", "Source", "Interfaces"]
+        
+        txt_sql = inference(question=sql_txt, table=table_col)
+        
+        
+        ### SQL Modification
+        txt_sql = txt_sql.replace("table",table_name)
+        sql_quotes = []
+        for match in re.finditer("=",txt_sql):
+            new_txt = txt_sql[match.span()[1]+1:]
+            try:
+                match2 = re.search("AND",new_txt)
+                sql_quotes.append((new_txt[:match2.span()[0]]).strip())
+            except:
+                sql_quotes.append(new_txt.strip())
+        
+        for i in sql_quotes:
+            qts = "'" + i + "'"
+            txt_sql = txt_sql.replace(i, qts)
+            
+        st.success(f"{txt_sql}")
+        all_students = sqldf(txt_sql)
+        
+        st.text("Output - ")
+        st.write(all_students)
+        
+        
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requirements.txt

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+pip
+Cmake
+wheel
+pandas
+jinja2
+pandasql
+Cython
+datasets
+huggingface-hub
+tapas
+torch
+transformers
+streamlit