import gradio as gr
from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Chroma
from langchain.chains import ConversationalRetrievalChain
from langchain_community.embeddings import HuggingFaceEmbeddings 
from langchain_community.llms import HuggingFacePipeline, HuggingFaceEndpoint
from langchain.memory import ConversationBufferMemory
from pathlib import Path
import chromadb
import re

def load_doc(list_file_path, chunk_size=600, chunk_overlap=40):
    loaders = [PyPDFLoader(x) for x in list_file_path]
    pages = [page for loader in loaders for page in loader.load()]
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
    doc_splits = text_splitter.split_documents(pages)
    return doc_splits

def create_db(splits, collection_name):
    embedding = HuggingFaceEmbeddings()
    client = chromadb.EphemeralClient()
    vectordb = Chroma.from_documents(
        documents=splits,
        embedding=embedding,
        client=client,
        collection_name=collection_name,
    )
    return vectordb

def initialize_llmchain(llm_model, vector_db, progress=gr.Progress()):
    llm = HuggingFaceEndpoint(
        repo_id=llm_model,
        temperature=0.7,
        max_new_tokens=1024,
        top_k=3,
    )
    memory = ConversationBufferMemory(memory_key="chat_history", output_key='answer', return_messages=True)
    retriever = vector_db.as_retriever()
    qa_chain = ConversationalRetrievalChain.from_llm(
        llm,
        retriever=retriever,
        chain_type="stuff",
        memory=memory,
        return_source_documents=True,
        verbose=False,
    )
    return qa_chain

def create_collection_name(filepath):
    collection_name = Path(filepath).stem
    collection_name = re.sub('[^A-Za-z0-9]+', '-', collection_name)[:50]
    if len(collection_name) < 3:
        collection_name += 'xyz'
    if not collection_name[0].isalnum():
        collection_name = 'A' + collection_name[1:]
    if not collection_name[-1].isalnum():
        collection_name = collection_name[:-1] + 'Z'
    return collection_name

def initialize_database(list_file_obj, progress=gr.Progress()):
    list_file_path = [x.name for x in list_file_obj if x is not None]
    collection_name = create_collection_name(list_file_path[0])
    doc_splits = load_doc(list_file_path)
    vector_db = create_db(doc_splits, collection_name)
    return vector_db, collection_name, "Complete!"

def initialize_LLM(llm_model, vector_db, progress=gr.Progress()):
    qa_chain = initialize_llmchain(llm_model, vector_db, progress)
    return qa_chain, "Complete!"

def conversation(qa_chain, message, history):
    formatted_chat_history = [(f"User: {user_message}", f"Assistant: {bot_message}") for user_message, bot_message in history]
    response = qa_chain({"question": message, "chat_history": formatted_chat_history})
    response_answer = response["answer"]
    if "Helpful Answer:" in response_answer:
        response_answer = response_answer.split("Helpful Answer:")[-1]
    response_sources = response["source_documents"]
    response_source1 = response_sources[0].page_content.strip()
    response_source2 = response_sources[1].page_content.strip()
    response_source3 = response_sources[2].page_content.strip()
    response_source1_page = response_sources[0].metadata["page"] + 1
    response_source2_page = response_sources[1].metadata["page"] + 1
    response_source3_page = response_sources[2].metadata["page"] + 1
    new_history = history + [(message, response_answer)]
    return qa_chain, gr.update(value=""), new_history, response_source1, response_source1_page, response_source2, response_source2_page, response_source3, response_source3_page

def demo():
    with gr.Blocks(theme="base") as demo:
        vector_db = gr.State()
        qa_chain = gr.State()
        collection_name = gr.State()
        
        gr.Markdown(
            """<center><h2>PDF-based chatbot (powered by LangChain and open-source LLMs)</center></h2>
            <h3>Ask any questions about your PDF documents, along with follow-ups</h3>
            <b>Note:</b> This AI assistant performs retrieval-augmented generation from your PDF documents.
            When generating answers, it takes past questions into account (via conversational memory), and includes document references for clarity purposes.
            <br><b>Warning:</b> This space uses the free CPU Basic hardware from Hugging Face. Some steps and LLM models used below (free inference endpoints) can take some time to generate an output.<br>
            """)

        with gr.Tab("Step 1 - Document pre-processing"):
            document = gr.Files(height=100, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload your PDF documents (single or multiple)")
            with gr.Row():
                db_progress = gr.Textbox(label="Vector database initialization", value="None")
            with gr.Row():
                db_btn = gr.Button("Generate vector database...")

        with gr.Tab("Step 2 - QA chain initialization"):
            llm_btn = gr.Radio(["mistralai/Mistral-7B-Instruct-v0.2"], label="LLM models", value="mistralai/Mistral-7B-Instruct-v0.2", type="index", info="Choose your LLM model")
            with gr.Row():
                llm_progress = gr.Textbox(value="None", label="QA chain initialization")
            with gr.Row():
                qachain_btn = gr.Button("Initialize question-answering chain...")

        with gr.Tab("Step 3 - Conversation with chatbot"):
            chatbot = gr.Chatbot(height=300)
            with gr.Row():
                msg = gr.Textbox(placeholder="Type message", container=True)
            with gr.Row():
                submit_btn = gr.Button("Submit")
                clear_btn = gr.ClearButton([msg, chatbot])

        db_btn.click(initialize_database, inputs=[document], outputs=[vector_db, collection_name, db_progress])
        qachain_btn.click(initialize_LLM, inputs=[llm_btn, vector_db], outputs=[qa_chain, llm_progress]).then(lambda:[None,"",0,"",0,"",0], inputs=None, outputs=[chatbot], queue=False)

        msg.submit(conversation, inputs=[qa_chain, msg, chatbot], outputs=[qa_chain, msg, chatbot], queue=False)
        submit_btn.click(conversation, inputs=[qa_chain, msg, chatbot], outputs=[qa_chain, msg, chatbot], queue=False)
        clear_btn.click(lambda:[None,"",0,"",0,"",0], inputs=None, outputs=[chatbot], queue=False)

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

if __name__ == "__main__":
    demo()