import gradio as gr
import os
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.chains import ConversationalRetrievalChain
from langchain.embeddings import HuggingFaceEmbeddings 
from langchain.llms import HuggingFaceHub
from langchain.memory import ConversationBufferMemory
import chromadb
from transformers import AutoTokenizer
import transformers
import torch

# Constants and configuration
list_llm = [
    "mistralai/Mixtral-8x7B-Instruct-v0.1", "mistralai/Mistral-7B-Instruct-v0.2",
    "mistralai/Mistral-7B-Instruct-v0.1", "HuggingFaceH4/zephyr-7b-beta",
    "meta-llama/Llama-2-7b-chat-hf", "microsoft/phi-2",
    "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "mosaicml/mpt-7b-instruct",
    "tiiuae/falcon-7b-instruct", "google/flan-t5-xxl"
]
list_llm_simple = [os.path.basename(llm) for llm in list_llm]

# Function placeholders (actual function implementations from the original script)
def load_doc(list_file_path, chunk_size, chunk_overlap):
    loaders = [PyPDFLoader(x) for x in list_file_path]
    pages = []
    for loader in loaders:
        pages.extend(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()
    new_client = chromadb.EphemeralClient()
    vectordb = Chroma.from_documents(
        documents=splits,
        embedding=embedding,
        client=new_client,
        collection_name=collection_name,
    )
    return vectordb

def load_db():
    embedding = HuggingFaceEmbeddings()
    vectordb = Chroma(embedding_function=embedding)
    return vectordb

def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db):
    if llm_model == "mistralai/Mixtral-8x7B-Instruct-v0.1":
        llm = HuggingFaceHub(
            repo_id=llm_model, 
            model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "load_in_8bit": True}
        )
    else:
        llm = HuggingFaceHub(
            repo_id=llm_model, 
            model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k}
        )
    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,
        return_generated_question=False,
    )
    return qa_chain

def initialize_database(list_file_obj, chunk_size, chunk_overlap):
    list_file_path = [x.name for x in list_file_obj if x is not None]
    collection_name = os.path.basename(list_file_path[0]).replace(" ","-")[:50]
    doc_splits = load_doc(list_file_path, chunk_size, chunk_overlap)
    vector_db = create_db(doc_splits, collection_name)
    return vector_db, collection_name, "Complete!"

def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db):
    llm_name = list_llm[llm_option]
    qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db)
    return qa_chain, "Complete!"

def format_chat_history(message, chat_history):
    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):
    formatted_chat_history = format_chat_history(message, history)
    response = qa_chain({"question": message, "chat_history": formatted_chat_history})
    response_answer = response["answer"]
    response_sources = response["source_documents"]
    response_source1 = response_sources[0].page_content.strip()
    response_source2 = response_sources[1].page_content.strip()
    response_source1_page = response_sources[0].metadata["page"] + 1
    response_source2_page = response_sources[1].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

def upload_file(file_obj):
    list_file_path = [file.name for file in file_obj]
    return list_file_path


def gradio_ui():
    with gr.Blocks(theme="base") as demo:
        # States
        vector_db, qa_chain, collection_name = gr.State(), gr.State(), gr.State()
        db_progress, llm_progress = gr.Textbox(), gr.Textbox()
        chatbot, doc_source1, source1_page, doc_source2, source2_page = gr.Chatbot(), gr.Textbox(), gr.Number(), gr.Textbox(), gr.Number()
        msg = gr.Textbox(placeholder="Type message")

        with gr.Tabs():
            # Tab 1: Document Pre-processing
            with gr.Tab("Step 1 - Document Pre-processing"):
                with gr.Row():
                    document = gr.File(label="Upload your PDF document", file_types=["pdf"])
                with gr.Row():
                    chunk_size = gr.Slider(minimum=100, maximum=1000, value=600, step=50, label="Chunk size", interactive=True)
                    chunk_overlap = gr.Slider(minimum=10, maximum=200, value=50, step=10, label="Chunk overlap", interactive=True)
                with gr.Row():
                    db_init_btn = gr.Button("Initialize Vector Database")

            # Tab 2: QA Chain Initialization
            with gr.Tab("Step 2 - QA Chain Initialization"):
                with gr.Row():
                    llm_selection = gr.Radio(list_llm_simple, label="Choose LLM Model", value=list_llm_simple[0])
                with gr.Row():
                    temperature = gr.Slider(minimum=0.0, maximum=1.0, value=0.7, step=0.1, label="Temperature", interactive=True)
                    max_tokens = gr.Slider(minimum=64, maximum=1024, value=256, step=64, label="Max Tokens", interactive=True)
                    top_k = gr.Slider(minimum=1, maximum=10, value=3, step=1, label="Top K", interactive=True)
                with gr.Row():
                    qa_init_btn = gr.Button("Initialize QA Chain")

            # Tab 3: Conversation with Chatbot
            with gr.Tab("Step 3 - Conversation with Chatbot"):
                chat_history = gr.State()
                with gr.Row():
                    chatbot
                with gr.Row():
                    msg
                    submit_btn = gr.Button("Submit")

        # Handlers
        db_init_btn.click(initialize_database, inputs=[document, chunk_size, chunk_overlap], outputs=[vector_db, collection_name, db_progress])
        qa_init_btn.click(initialize_LLM, inputs=[llm_selection, temperature, max_tokens, top_k, vector_db], outputs=[qa_chain, llm_progress])
        submit_btn.click(conversation, inputs=[qa_chain, msg, chat_history], outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page])

    return demo

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