app.py

import os
import sys
import yaml
import gradio as gr
import uuid

current_dir = os.path.dirname(os.path.abspath(__file__))

from src.document_retrieval import DocumentRetrieval
from utils.parsing.sambaparse import parse_doc_universal # added
from utils.vectordb.vector_db import VectorDb

def handle_userinput(user_question, conversation_chain, history):
    if user_question:
        try:
            # Generate response
            response = conversation_chain.invoke({"question": user_question})

            # Append user message and response to chat history
            history = history + [(user_question, response["answer"])]

            return history, "" 
        except Exception as e:
            error_msg = f"An error occurred: {str(e)}"
            history = history + [(user_question, error_msg)]
            return history, "" 
    else:
        return history, ""

def process_documents(files, collection_name, document_retrieval, vectorstore, conversation_chain, save_location=None):
    try:
        document_retrieval = DocumentRetrieval()
        _, _, text_chunks = parse_doc_universal(doc=files)
        print(len(text_chunks))
        print(text_chunks[0])
        embeddings = document_retrieval.load_embedding_model()
        collection_id = str(uuid.uuid4())
        collection_name = f"collection_{collection_id}"
        vectorstore = document_retrieval.create_vector_store(text_chunks, embeddings, output_db=save_location, collection_name=collection_name)
        document_retrieval.init_retriever(vectorstore)
        conversation_chain = document_retrieval.get_qa_retrieval_chain()
        return conversation_chain, vectorstore, document_retrieval, collection_name, "Complete! You can now ask questions."
    except Exception as e:
        return conversation_chain, vectorstore, document_retrieval, collection_name, f"An error occurred while processing: {str(e)}"

caution_text = """⚠️ Note: depending on the size of your document, this could take several minutes.
"""

with gr.Blocks() as demo:
    vectorstore = gr.State()
    conversation_chain = gr.State()
    document_retrieval = gr.State()
    collection_name=gr.State()

    gr.Markdown("# Enterprise Knowledge Retriever", 
            elem_id="title")
    
    gr.Markdown("Powered by LLama3.1-8B-Instruct on SambaNova Cloud. Get your API key [here](https://cloud.sambanova.ai/apis).")

    api_key = gr.Textbox(label="API Key", type="password", placeholder="(Optional) Enter your API key here for more availability")

    # Step 1: Add PDF file   
    gr.Markdown("## 1️⃣ Upload PDF")
    docs = gr.File(label="Add PDF file (single)", file_types=["pdf"], file_count="single")
        
    # Step 2: Process PDF file
    gr.Markdown(("## 2️⃣ Process document and create vector store"))
    db_btn = gr.Radio(["ChromaDB"], label="Vector store type", value = "ChromaDB", type="index", info="Choose your vector store")
    setup_output = gr.Textbox(label="Processing status", visible=True, value="None") 
    process_btn = gr.Button("🔄 Process")
    gr.Markdown(caution_text)
      
    # Preprocessing events
    process_btn.click(process_documents, inputs=[docs, collection_name, document_retrieval, vectorstore, conversation_chain], outputs=[conversation_chain, vectorstore, document_retrieval, collection_name, setup_output], concurrency_limit=20)
    
    # Step 3: Chat with your data
    gr.Markdown("## 3️⃣ Chat with your document")
    chatbot = gr.Chatbot(label="Chatbot", show_label=True, show_share_button=False, show_copy_button=True, likeable=True)
    msg = gr.Textbox(label="Ask questions about your data", show_label=True, placeholder="Enter your message...")
    clear_btn = gr.Button("Clear chat")
    sources_output = gr.Textbox(label="Sources", visible=False)

    # Chatbot events
    msg.submit(handle_userinput, inputs=[msg, conversation_chain, chatbot], outputs=[chatbot, msg], queue=False)
    clear_btn.click(lambda: [None, ""], inputs=None, outputs=[chatbot, msg], queue=False)

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