import os
import gradio as gr
import torch
from langchain.chains import ConversationalRetrievalChain
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.document_loaders import PyPDFLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Chroma
from langchain_community.llms import HuggingFacePipeline
from transformers import pipeline

# Set Hugging Face Cache Directory
os.environ["HF_HOME"] = "/tmp/huggingface_cache"

# Check for GPU availability
DEVICE = "cuda:0" if torch.cuda.is_available() else "cpu"

# Global variables
conversation_retrieval_chain = None
chat_history = []
llm_pipeline = None
embeddings = None
persist_directory = "/tmp/chroma_db"  # Storage for vector DB


def init_llm():
    """Initialize LLM and Embeddings"""
    global llm_pipeline, embeddings

    hf_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
    if not hf_token:
        raise ValueError("HUGGINGFACEHUB_API_TOKEN is not set in environment variables.")

    model_id = "tiiuae/falcon-7b-instruct"
    hf_pipeline = pipeline("text-generation", model=model_id, device=DEVICE)
    llm_pipeline = HuggingFacePipeline(pipeline=hf_pipeline)

    embeddings = HuggingFaceEmbeddings(
        model_name="sentence-transformers/all-MiniLM-L6-v2",
        model_kwargs={"device": DEVICE}
    )


def process_document(file):
    """Process uploaded PDF and create a retriever"""
    global conversation_retrieval_chain

    if not llm_pipeline or not embeddings:
        init_llm()

    # Load PDF and split text
    loader = PyPDFLoader(file.name)
    documents = loader.load()
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1024, chunk_overlap=64)
    texts = text_splitter.split_documents(documents)

    # Load or create ChromaDB
    if os.path.exists(persist_directory):
        db = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
    else:
        db = Chroma.from_documents(texts, embedding=embeddings, persist_directory=persist_directory)

    retriever = db.as_retriever(search_type="similarity", search_kwargs={'k': 6})

    # Initialize ConversationalRetrievalChain
    conversation_retrieval_chain = ConversationalRetrievalChain.from_llm(
        llm=llm_pipeline, retriever=retriever
    )

    return "📄 PDF uploaded and processed successfully! You can now ask questions."


def process_prompt(prompt, chat_history_display):
    """Generate a response using the retrieval chain"""
    global conversation_retrieval_chain, chat_history

    if not conversation_retrieval_chain:
        return chat_history_display + [("❌ No document uploaded.", "Please upload a PDF first.")]

    output = conversation_retrieval_chain({"question": prompt, "chat_history": chat_history})
    answer = output["answer"]

    chat_history.append((prompt, answer))
    
    return chat_history


# Define Gradio UI
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("<h1 style='text-align: center;'>Personal Data Assistant</h1>")

    with gr.Row():
        dark_mode = gr.Checkbox(label="🌙 Toggle light/dark mode")
    
    with gr.Column():  # ✅ Replace `gr.Box()` with `gr.Column()`
        gr.Markdown("Hello there! I'm your friendly data assistant, ready to answer any questions regarding your data. Could you please upload a PDF file for me to analyze?")
        file_input = gr.File(label="Upload File")
        upload_button = gr.Button("📂 Upload File")

    status_output = gr.Textbox(label="Status", interactive=False)

    chat_history_display = gr.Chatbot(label="Chat History")
    
    with gr.Row():
        user_input = gr.Textbox(placeholder="Type your message here...", scale=4)
        submit_button = gr.Button("📩", scale=1)
        clear_button = gr.Button("🔄", scale=1)

    # Button Click Actions
    upload_button.click(process_document, inputs=file_input, outputs=status_output)
    submit_button.click(process_prompt, inputs=[user_input, chat_history_display], outputs=chat_history_display)
    clear_button.click(lambda: [], outputs=chat_history_display)

# Launch Gradio App
if __name__ == "__main__":
    demo.launch(share=True)