import os
import gradio as gr
from langchain.chains import RetrievalQA
from langchain_community.document_loaders import TextLoader
from langchain_community.document_loaders import UnstructuredExcelLoader
from langchain.indexes import VectorstoreIndexCreator
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain_community.llms import HuggingFaceEndpoint
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain

from dotenv import find_dotenv, load_dotenv 

from langchain.chains import create_retrieval_chain, RetrievalQA
from langchain_community.vectorstores import FAISS

_=load_dotenv(find_dotenv())
hf_api = os.getenv("HUGGINGFACEHUB_API_TOKEN")

def indexdocs (file_path, progress=gr.Progress()):
    
    progress(0.1, desc="Loading documents...")
    
    loaders = [UnstructuredExcelLoader(file, mode="elements") for file in file_path]
    documents=[]
    for loader in loaders:
        documents.extend(loader.load())
    
    progress(0.3, desc="Splitting documents...")
    
    text_splitter = RecursiveCharacterTextSplitter (chunk_size=1500, chunk_overlap=300)
    pages=text_splitter.split_documents(documents)
    embedding = HuggingFaceEmbeddings()
    
    progress(0.5, desc="Creating vectorstore...")
    
    vector=FAISS.from_documents (documents=pages,embedding=embedding)
    retriever = vector.as_retriever()
    
    progress(0.8, desc="Setting up language model...")
    
    memory = ConversationBufferMemory(
            memory_key="chat_history",
            output_key='answer',
            return_messages=True
        )
    
    llm = HuggingFaceEndpoint(
                repo_id="Mistralai/Mistral-7B-Instruct-v0.2", 
                temperature = 0.1, 
                max_new_tokens = 200, 
                top_k = 1 #top_k,
            )
    
    qa_chain = ConversationalRetrievalChain.from_llm(
            llm,
            retriever=retriever,
            chain_type="stuff", 
            memory=memory,
            return_source_documents=True,
            verbose=False,
        )
    return qa_chain, None 

def format_chat_history(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 chat(qa_chain,msg,history):
    formatted_chat_history = format_chat_history(history)
    response = qa_chain.invoke({"question": msg, "chat_history": formatted_chat_history})
    response_answer = response["answer"]
    response_sources=response["source_documents"]
    print (response_sources[0])
    response_source1= response_sources[0].metadata["filename"]
    response_source_sheet= response_sources[0].metadata["page_name"]
    new_history = history + [(msg, response_answer)]
    return qa_chain, gr.update(value=""), new_history, response_source1, response_source_sheet

with gr.Blocks() as demo:
    qa_chain=gr.State()
    
    gr.Markdown(
        """
        # MS Excel Knowledge Base QA using RAG
        """
    )
    with gr.Column():
            file_list = gr.File(label='Upload your MS Excel files...', file_count='multiple', file_types=['.xls,.xlsx'])
            fileuploadbtn= gr.Button ("Index Documents and Start Chatting")
    with gr.Row():
        chatbot=gr.Chatbot(height=300)
    with gr.Row():
        source=gr.Textbox(info="Source",container=False,scale=4)
        source_page=gr.Textbox(info="Sheet",container=False,scale=1)
    with gr.Row():
        prompt=gr.Textbox(placeholder="Please enter your prompt...",container=False, scale=4, visible=True, interactive=False)
        promptsubmit=gr.Button("Submit", scale=1, visible=True, interactive=False)
    gr.Markdown(
        """
        # Responsible AI Usage
        Your documents uploaded to the system or interactions with the chatbot are not saved.
        """
    )

    fileuploadbtn.click(fn=indexdocs, inputs = [file_list], outputs=[qa_chain,chatbot]).then(lambda:[gr.Textbox(interactive=True), gr.Button (interactive=True)], \
    inputs=None, outputs=[prompt,promptsubmit], queue=False)  
    promptsubmit.click(fn=chat, inputs=[qa_chain,prompt,chatbot], outputs=[qa_chain,prompt,chatbot,source,source_page],queue=False)
    prompt.submit(fn=chat, inputs=[qa_chain,prompt,chatbot], outputs=[qa_chain,prompt,chatbot,source,source_page],queue=False)

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