import gradio as gr
import os
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_huggingface import HuggingFaceEmbeddings
from langchain_community.llms import HuggingFacePipeline
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
import spaces
from pathlib import Path
import chromadb
from unidecode import unidecode
import os
from huggingface_hub import login
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM


import transformers
import torch
import re

# List of models
list_llm = [
    "mistralai/Mistral-7B-Instruct-v0.2",
    "HuggingFaceH4/zephyr-7b-beta",
    "microsoft/phi-2",
    "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
    # Add more GPU-compatible models here
]
list_llm_simple = [os.path.basename(llm) for llm in list_llm]

@spaces.GPU
@spaces.GPU
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

# Create vector database
def create_db(splits, collection_name):
    # Set CUDA_VISIBLE_DEVICES if GPU is available
    if torch.cuda.is_available():
        os.environ["CUDA_VISIBLE_DEVICES"] = "0"
    
    embedding = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
    new_client = chromadb.EphemeralClient()
    vectordb = Chroma.from_documents(
        documents=splits,
        embedding=embedding,
        client=new_client,
        collection_name=collection_name,
    )
    return vectordb

def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
    progress(0.1, desc="Initializing HF tokenizer...")
    
    # Retrieve the Hugging Face token from environment variables
    hf_token = os.environ.get("HUGGINGFACEHUB_API_TOKEN")
    if not hf_token:
        raise ValueError("Hugging Face token not found. Please set the HF_TOKEN environment variable.")

    # Log in to Hugging Face
    login(token=hf_token)

    # Initialize tokenizer and model with the token
    tokenizer = AutoTokenizer.from_pretrained(llm_model, use_auth_token=hf_token)
    
    progress(0.3, desc="Loading model...")
    try:
        model = AutoModelForCausalLM.from_pretrained(
            llm_model, 
            use_auth_token=hf_token,
            torch_dtype=torch.float16, 
            device_map="auto"
        )
    except RuntimeError as e:
        if "CUDA out of memory" in str(e):
            raise gr.Error("GPU memory exceeded. Try a smaller model or reduce batch size.")
        else:
            raise e
    
    progress(0.5, desc="Initializing HF pipeline...")
    pipeline = transformers.pipeline(
        "text-generation",
        model=model,
        tokenizer=tokenizer,
        torch_dtype=torch.float16,
        device_map="auto",
        max_new_tokens=max_tokens,
        do_sample=True,
        top_k=top_k,
        num_return_sequences=1,
        eos_token_id=tokenizer.eos_token_id
    )
    llm = HuggingFacePipeline(pipeline=pipeline, model_kwargs={'temperature': temperature})


    
    progress(0.75, desc="Defining buffer memory...")
    memory = ConversationBufferMemory(
        memory_key="chat_history",
        output_key='answer',
        return_messages=True
    )
    retriever = vector_db.as_retriever()
    progress(0.8, desc="Defining retrieval chain...")
    qa_chain = ConversationalRetrievalChain.from_llm(
        llm,
        retriever=retriever,
        chain_type="stuff", 
        memory=memory,
        return_source_documents=True,
        verbose=False,
    )
    progress(0.9, desc="Done!")
    return qa_chain

def create_collection_name(filepath):
    collection_name = Path(filepath).stem
    collection_name = collection_name.replace(" ","-") 
    collection_name = unidecode(collection_name)
    collection_name = re.sub('[^A-Za-z0-9]+', '-', collection_name)
    collection_name = collection_name[:50]
    if len(collection_name) < 3:
        collection_name = 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, chunk_size, chunk_overlap, progress=gr.Progress()):
    list_file_path = [x.name for x in list_file_obj if x is not None]
    progress(0.1, desc="Creating collection name...")
    collection_name = create_collection_name(list_file_path[0])
    progress(0.25, desc="Loading document...")
    doc_splits = load_doc(list_file_path, chunk_size, chunk_overlap)
    progress(0.5, desc="Generating vector database...")
    vector_db = create_db(doc_splits, collection_name)
    progress(0.9, desc="Done!")
    return vector_db, collection_name, "Complete!"

def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
    llm_name = list_llm[llm_option]
    qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db, progress)
    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"]
    if response_answer.find("Helpful Answer:") != -1:
        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>GPU-Accelerated PDF-based Chatbot</center></h2>
        <h3>Ask any questions about your PDF documents</h3>""")
        gr.Markdown(
        """<b>Note:</b> This AI assistant uses GPU acceleration for faster processing. 
        It performs retrieval-augmented generation (RAG) from your PDF documents using Langchain and open-source LLMs. 
        The chatbot takes past questions into account and includes document references.""")
        
        with gr.Tab("Step 1 - Upload PDF"):
            document = gr.Files(height=100, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload your PDF documents (single or multiple)")
        
        with gr.Tab("Step 2 - Process document"):
            db_btn = gr.Radio(["ChromaDB"], label="Vector database type", value = "ChromaDB", type="index", info="Choose your vector database")
            with gr.Accordion("Advanced options - Document text splitter", open=False):
                slider_chunk_size = gr.Slider(minimum = 100, maximum = 1000, value=600, step=20, label="Chunk size", info="Chunk size", interactive=True)
                slider_chunk_overlap = gr.Slider(minimum = 10, maximum = 200, value=40, step=10, label="Chunk overlap", info="Chunk overlap", interactive=True)
            db_progress = gr.Textbox(label="Vector database initialization", value="None")
            db_btn = gr.Button("Generate vector database")
            
        with gr.Tab("Step 3 - Initialize QA chain"):
            llm_btn = gr.Radio(list_llm_simple, label="LLM models", value = list_llm_simple[0], type="index", info="Choose your LLM model")
            with gr.Accordion("Advanced options - LLM model", open=False):
                slider_temperature = gr.Slider(minimum = 0.01, maximum = 1.0, value=0.7, step=0.1, label="Temperature", info="Model temperature", interactive=True)
                slider_maxtokens = gr.Slider(minimum = 224, maximum = 4096, value=1024, step=32, label="Max Tokens", info="Model max tokens", interactive=True)
                slider_topk = gr.Slider(minimum = 1, maximum = 10, value=3, step=1, label="top-k samples", info="Model top-k samples", interactive=True)
            llm_progress = gr.Textbox(value="None",label="QA chain initialization")
            qachain_btn = gr.Button("Initialize Question Answering chain")

        with gr.Tab("Step 4 - Chatbot"):
            chatbot = gr.Chatbot(height=300)
            with gr.Accordion("Advanced - Document references", open=False):
                doc_source1 = gr.Textbox(label="Reference 1", lines=2, container=True, scale=20)
                source1_page = gr.Number(label="Page", scale=1)
                doc_source2 = gr.Textbox(label="Reference 2", lines=2, container=True, scale=20)
                source2_page = gr.Number(label="Page", scale=1)
                doc_source3 = gr.Textbox(label="Reference 3", lines=2, container=True, scale=20)
                source3_page = gr.Number(label="Page", scale=1)
            msg = gr.Textbox(placeholder="Type message (e.g. 'What is this document about?')", container=True)
            submit_btn = gr.Button("Submit message")
            clear_btn = gr.ClearButton([msg, chatbot], value="Clear conversation")
            
        # Preprocessing events
        db_btn.click(initialize_database, 
            inputs=[document, slider_chunk_size, slider_chunk_overlap], 
            outputs=[vector_db, collection_name, db_progress])
        qachain_btn.click(initialize_LLM, 
            inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db], 
            outputs=[qa_chain, llm_progress]).then(lambda:[None,"",0,"",0,"",0], 
            inputs=None, 
            outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], 
            queue=False)

        # Chatbot events
        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], 
            queue=False)
        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], 
            queue=False)
        clear_btn.click(lambda:[None,"",0,"",0,"",0], 
            inputs=None, 
            outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], 
            queue=False)
    demo.queue().launch(debug=True)

if __name__ == "__main__":
    demo()