app.py

import gradio as gr
from transformers import pipeline
from huggingface_hub import InferenceClient
import requests
from bs4 import BeautifulSoup

# Initialize the text generation pipeline
pipe = pipeline("text-generation", model="microsoft/Phi-3-mini-4k-instruct", trust_remote_code=True)
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

def web_search(query):
    # Simulate a web search using Google
    response = requests.get(f"https://www.google.com/search?q={query}")
    soup = BeautifulSoup(response.text, "html.parser")
    results = []
    for g in soup.find_all('div', class_='BNeawe vvjwJb AP7Wnd'):
        results.append(g.get_text())
    return results

def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    messages = [{"role": "system", "content": system_message}]

    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

    # Check if message is a search request
    if "search:" in message.lower():
        search_query = message.split("search:", 1)[1].strip()
        search_results = web_search(search_query)
        response = "\n".join(search_results[:5])  # Return top 5 search results
    else:
        messages.append({"role": "user", "content": message})
        response = ""
        for message in client.chat_completion(
            messages,
            max_tokens=max_tokens,
            stream=True,
            temperature=temperature,
            top_p=top_p,
        ):
            token = message.choices[0].delta.content
            response += token
            yield response

    yield response


demo = gr.ChatInterface(
    respond,
    title="INDONESIAN CHATBOT",
    additional_inputs=[
        gr.Textbox(value="You are a friendly AI Assistens Speak in indonesian", label="System message", visible=False),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
)


if __name__ == "__main__":
    demo.launch()