app.py

import gradio as gr
import os
from langchain_community.vectorstores import FAISS
from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings import HuggingFaceEmbeddings 
from langchain_community.llms import HuggingFaceEndpoint
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory

api_token = os.getenv("HF_TOKEN")
DEFAULT_LLM = "meta-llama/Meta-Llama-3-8B-Instruct"

def load_and_create_db(files):
    if not files:
        return None, None
        
    try:
        # Create a list of documents
        list_file_paths = []
        for file in files:
            # Save uploaded file temporarily
            file_name = file.name
            if file_name.lower().endswith('.pdf'):
                list_file_paths.append(file_name)
            else:
                raise ValueError(f"Unsupported file format: {file_name}. Please upload PDF files only.")
        
        if not list_file_paths:
            return None, None
            
        # Load documents
        loaders = [PyPDFLoader(path) for path in list_file_paths]
        pages = []
        for loader in loaders:
            pages.extend(loader.load())
            
        # Split documents
        text_splitter = RecursiveCharacterTextSplitter(
            chunk_size=1024,
            chunk_overlap=64
        )
        doc_splits = text_splitter.split_documents(pages)
        
        # Create vector database
        embeddings = HuggingFaceEmbeddings()
        vectordb = FAISS.from_documents(doc_splits, embeddings)
        
        # Initialize QA chain
        qa_chain = initialize_llmchain(vectordb)
        
        return vectordb, qa_chain
    except Exception as e:
        print(f"Error processing files: {str(e)}")
        return None, None

def initialize_llmchain(vector_db, temperature=0.5, max_tokens=4096, top_k=3):
    llm = HuggingFaceEndpoint(
        repo_id=DEFAULT_LLM,
        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 format_citation(source_doc):
    content = source_doc.page_content.strip()
    page = source_doc.metadata["page"] + 1
    return content, page

def conversation(qa_chain, message, history):
    if not qa_chain:
        return (None, gr.update(value=""), history, "", 0, "", 0, "", 0, 
                "Please upload a document first.")
        
    formatted_history = []
    for user_msg, bot_msg in history:
        formatted_history.append(f"User: {user_msg}")
        formatted_history.append(f"Assistant: {bot_msg}")
    
    response = qa_chain.invoke({
        "question": message,
        "chat_history": formatted_history
    })
    
    answer = response["answer"]
    if "Helpful Answer:" in answer:
        answer = answer.split("Helpful Answer:")[-1]

    # Format answer with citation numbers
    sources = response["source_documents"][:3]
    modified_answer = answer
    for i in range(len(sources)):
        modified_answer = modified_answer + f" [{i+1}]"

    # Get citation contents and page numbers
    citations = [format_citation(source) for source in sources]
    source1_content, page1 = citations[0] if len(citations) > 0 else ("", 0)
    source2_content, page2 = citations[1] if len(citations) > 1 else ("", 0)
    source3_content, page3 = citations[2] if len(citations) > 2 else ("", 0)
    
    new_history = history + [(message, modified_answer)]
    return (qa_chain, gr.update(value=""), new_history, 
            source1_content, page1, source2_content, page2, source3_content, page3, "")

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 PDF Chatbot</h1></center>")
        gr.Markdown("""
        <b>Query your PDF documents!</b> This AI agent performs retrieval augmented generation (RAG) 
        on PDF documents. <b>Please do not upload confidential documents.</b>
        """)
        
        with gr.Row():
            with gr.Column(scale=1):
                document = gr.Files(
                    height=300,
                    file_count="multiple",
                    file_types=[".pdf"],
                    label="Upload PDF documents"
                )
                upload_status = gr.Textbox(label="Upload Status", interactive=False)
                
            with gr.Column(scale=2):
                chatbot = gr.Chatbot(height=500)
                with gr.Accordion("Citations", open=False):
                    with gr.Row():
                        doc_source1 = gr.Textbox(label="[1]", lines=2, container=True, scale=20)
                        source1_page = gr.Number(label="Page", scale=1)
                    with gr.Row():
                        doc_source2 = gr.Textbox(label="[2]", lines=2, container=True, scale=20)
                        source2_page = gr.Number(label="Page", scale=1)
                    with gr.Row():
                        doc_source3 = gr.Textbox(label="[3]", lines=2, container=True, scale=20)
                        source3_page = gr.Number(label="Page", scale=1)
                with gr.Row():
                    msg = gr.Textbox(
                        placeholder="Ask a question about your documents...",
                        container=True
                    )
                with gr.Row():
                    submit_btn = gr.Button("Submit")
                    clear_btn = gr.ClearButton([msg, chatbot], value="Clear")
        
        def handle_file_upload(files):
            if not files:
                return None, None, "No files uploaded"
            try:
                vectordb, qa = load_and_create_db(files)
                if vectordb and qa:
                    return vectordb, qa, "Files successfully processed"
                return None, None, "Error processing files"
            except Exception as e:
                return None, None, f"Error: {str(e)}"
        
        # Automatically create vector DB and initialize chain on file upload
        document.upload(
            fn=handle_file_upload,
            inputs=[document],
            outputs=[vector_db, qa_chain, upload_status]
        )
        
        # Clear citations when chat is cleared
        def clear_all():
            return ["", 0, "", 0, "", 0]
            
        # Chatbot events
        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,
                    upload_status]
        )
        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,
                    upload_status]
        )
        clear_btn.click(
            clear_all,
            outputs=[doc_source1, source1_page,
                    doc_source2, source2_page,
                    doc_source3, source3_page]
        )
        
    demo.queue().launch(debug=True)

if __name__ == "__main__":
    demo()