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 pathlib import Path
import chromadb

# List of available LLM models
list_llm = ["mistralai/Mistral-7B-Instruct-v0.2", "mistralai/Mixtral-8x7B-Instruct-v0.1", "mistralai/Mistral-7B-Instruct-v0.1",
            "google/gemma-7b-it", "google/gemma-2b-it",
            "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]

# Load PDF document and create doc splits
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):
    embedding = HuggingFaceEmbeddings()
    new_client = chromadb.EphemeralClient()
    vectordb = Chroma.from_documents(
        documents=splits,
        embedding=embedding,
        client=new_client,
        collection_name=collection_name
    )
    return vectordb

# Initialize langchain LLM chain
def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
    if llm_model == "mistralai/Mixtral-8x7B-Instruct-v0.1":
        model_kwargs = {"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "load_in_8bit": True}
    elif llm_model == "microsoft/phi-2":
        raise gr.Error("phi-2 model requires 'trust_remote_code=True', currently not supported by langchain HuggingFaceHub...")
    elif llm_model == "TinyLlama/TinyLlama-1.1B-Chat-v1.0":
        model_kwargs = {"temperature": temperature, "max_new_tokens": 250, "top_k": top_k}
    else:
        model_kwargs = {"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k}
    
    llm = HuggingFaceHub(
        repo_id=llm_model,
        model_kwargs=model_kwargs
    )

    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
    )

    progress(0.9, desc="Done!")
    return qa_chain

def initialize_demo(list_file_obj, chunk_size, chunk_overlap, db_progress):
    list_file_path = [file.name for file in list_file_obj if file is not None]
    collection_name = Path(list_file_path[0]).stem.replace(" ", "-")[:50]
    doc_splits = load_doc(list_file_path, chunk_size, chunk_overlap)
    vector_db = create_db(doc_splits, collection_name)
    qa_chain = initialize_llmchain(
        list_llm[0],  # Using Mistral-7B-Instruct-v0.2 as the LLM model
        0.7,  # Temperature
        1024,  # Max Tokens
        3,  # Top K
        vector_db,
        db_progress
    )
    return vector_db, collection_name, qa_chain, "Complete!"

def upload_file(file_obj):
    list_file_path = []
    for file in file_obj:
        if file is not None:
            file_path = file.name
            list_file_path.append(file_path)
    return list_file_path

def demo():
    with gr.Blocks(theme="base") as demo:
        vector_db = gr.State()
        collection_name = gr.State()
        qa_chain = gr.State()
        
        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)")
            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 2 - QA chain initialization"):
            llm_progress = gr.Textbox(value="None", label="QA chain initialization")
            qachain_btn = gr.Button("Initialize question-answering chain...")

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

        document.upload(initialize_demo, inputs=[document, slider_chunk_size, slider_chunk_overlap, db_progress], outputs=[vector_db, collection_name, qa_chain, db_progress])
        qachain_btn.click(initialize_llmchain, inputs=[qa_chain, llm_progress], outputs=[qa_chain, llm_progress])
        submit_btn.click(lambda: None, inputs=None, outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page])