app.py

import gradio as gr
import os
from bs4 import BeautifulSoup
import requests
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory
from langchain_community.llms import HuggingFaceEndpoint
from langchain.schema import Document

# Initialize environment
api_token =os.getenv("HF_TOKEN")
list_llm = ["meta-llama/Meta-Llama-3-8B-Instruct", "mistralai/Mistral-7B-Instruct-v0.2"]
list_llm_simple = [os.path.basename(llm) for llm in list_llm]

def scrape_website(url):
    """Scrape text content from a website"""
    try:
        response = requests.get(url)
        soup = BeautifulSoup(response.text, 'html.parser')
        
        for script in soup(["script", "style"]):
            script.decompose()
            
        text = soup.get_text()
        lines = (line.strip() for line in text.splitlines())
        chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
        text = ' '.join(chunk for chunk in chunks if chunk)
        
        return text
    except Exception as e:
        return f"Error scraping website: {str(e)}"

def process_text(text):
    """Split text into chunks"""
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=1024,
        chunk_overlap=64
    )
    chunks = text_splitter.split_text(text)
    return chunks

def create_db(splits):
    """Create vector database"""
    embeddings = HuggingFaceEmbeddings()
    documents = [Document(page_content=text, metadata={}) for text in splits]
    vectordb = FAISS.from_documents(documents, embeddings)
    return vectordb

def initialize_database(url, progress=gr.Progress()):
    """Initialize database from URL"""
    # Scrape website content
    text_content = scrape_website(url)
    if text_content.startswith("Error"):
        return None, text_content
    
    # Create text chunks
    doc_splits = process_text(text_content)
    
    # Create vector database
    vector_db = create_db(doc_splits)
    return vector_db, "Database created successfully!"

def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db):
    """Initialize LLM chain"""
    llm = HuggingFaceEndpoint(
        repo_id=llm_model,
        huggingfacehub_api_token=api_token,
        temperature=temperature,
        max_new_tokens=max_tokens,
        top_k=top_k,
    )
    
    memory = ConversationBufferMemory(
        memory_key="chat_history",
        output_key='answer',
        return_messages=True
    )

    qa_chain = ConversationalRetrievalChain.from_llm(
        llm,
        retriever=vector_db.as_retriever(),
        chain_type="stuff",
        memory=memory,
        return_source_documents=True,
        verbose=False,
    )
    return qa_chain

def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
    """Initialize LLM with selected options"""
    llm_name = list_llm[llm_option]
    qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db)
    return qa_chain, "QA chain initialized. Chatbot is ready!"

def format_chat_history(message, chat_history):
    """Format chat history for the model"""
    formatted_chat_history = []
    for user_message, bot_message in chat_history:
        formatted_chat_history.append(f"User: {user_message}")
        formatted_chat_history.append(f"Assistant: {bot_message}")
    return formatted_chat_history

def conversation(qa_chain, message, history):
    """Handle conversation with the model"""
    formatted_chat_history = format_chat_history(message, history)
    response = qa_chain.invoke({"question": message, "chat_history": formatted_chat_history})
    
    response_answer = response["answer"]
    if response_answer.find("Helpful Answer:") != -1:
        response_answer = response_answer.split("Helpful Answer:")[-1]
    
    response_sources = response["source_documents"][:3]
    sources = []
    for i in range(3):
        if i < len(response_sources):
            sources.append((response_sources[i].page_content.strip(), 1))
        else:
            sources.append(("", 1))
    
    new_history = history + [(message, response_answer)]
    return (qa_chain, gr.update(value=""), new_history, 
            sources[0][0], sources[0][1],
            sources[1][0], sources[1][1],
            sources[2][0], sources[2][1])

def demo():
    with gr.Blocks(theme=gr.themes.Default(primary_hue="red", secondary_hue="pink", neutral_hue="sky")) as demo:
        vector_db = gr.State()
        qa_chain = gr.State()
        
        gr.HTML("<center><h1>RAG Website Chatbot</h1></center>")
        gr.Markdown("""<b>Query any website content!</b> This AI agent performs retrieval augmented generation (RAG) on website content.""")
        
        with gr.Row():
            with gr.Column(scale=86):
                gr.Markdown("<b>Step 1 - Enter Website URL and Initialize RAG pipeline</b>")
                with gr.Row():
                    url_input = gr.Textbox(label="Website URL", placeholder="Enter website URL here...")
                with gr.Row():
                    db_btn = gr.Button("Create vector database")
                with gr.Row():
                    db_progress = gr.Textbox(value="Not initialized", show_label=False)
                
                gr.Markdown("<b>Select Large Language Model (LLM) and input parameters</b>")
                with gr.Row():
                    llm_btn = gr.Radio(list_llm_simple, label="Available LLMs", value=list_llm_simple[0], type="index")
                with gr.Row():
                    with gr.Accordion("LLM input parameters", open=False):
                        slider_temperature = gr.Slider(0.01, 1.0, 0.5, step=0.1, label="Temperature")
                        slider_maxtokens = gr.Slider(128, 9192, 4096, step=128, label="Max New Tokens")
                        slider_topk = gr.Slider(1, 10, 3, step=1, label="top-k")
                with gr.Row():
                    qachain_btn = gr.Button("Initialize Question Answering Chatbot")
                with gr.Row():
                    llm_progress = gr.Textbox(value="Not initialized", show_label=False)

            with gr.Column(scale=200):
                gr.Markdown("<b>Step 2 - Chat about the Website Content</b>")
                chatbot = gr.Chatbot(height=505)
                with gr.Accordion("Relevant context from the source", open=False):
                    with gr.Row():
                        doc_source1 = gr.Textbox(label="Reference 1", lines=2, container=True, scale=20)
                        source1_page = gr.Number(label="Section", scale=1)
                    with gr.Row():
                        doc_source2 = gr.Textbox(label="Reference 2", lines=2, container=True, scale=20)
                        source2_page = gr.Number(label="Section", scale=1)
                    with gr.Row():
                        doc_source3 = gr.Textbox(label="Reference 3", lines=2, container=True, scale=20)
                        source3_page = gr.Number(label="Section", scale=1)
                with gr.Row():
                    msg = gr.Textbox(placeholder="Ask a question", container=True)
                with gr.Row():
                    submit_btn = gr.Button("Submit")
                    clear_btn = gr.ClearButton([msg, chatbot], value="Clear")

        # Event handlers
        db_btn.click(initialize_database,
                    inputs=[url_input],
                    outputs=[vector_db, db_progress])
        
        qachain_btn.click(initialize_LLM,
                         inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db],
                         outputs=[qa_chain, llm_progress])
        
        msg.submit(conversation,
                  inputs=[qa_chain, msg, chatbot],
                  outputs=[qa_chain, msg, chatbot, doc_source1, source1_page,
                          doc_source2, source2_page, doc_source3, source3_page])
        
        submit_btn.click(conversation,
                        inputs=[qa_chain, msg, chatbot],
                        outputs=[qa_chain, msg, chatbot, doc_source1, source1_page,
                                doc_source2, source2_page, doc_source3, source3_page])
        
        clear_btn.click(lambda: [None, "", 0, "", 0, "", 0],
                       inputs=None,
                       outputs=[chatbot, doc_source1, source1_page,
                               doc_source2, source2_page, doc_source3, source3_page])

    demo.queue().launch(debug=True)

if __name__ == "__main__":
    demo()