import gradio as gr
import os
from typing import List, Dict
from ragas import evaluate
from ragas.metrics import (
    answer_relevancy,
    faithfulness,
    context_recall,
    context_precision,
    answer_correctness,
    answer_similarity
)
from datasets import load_dataset
from langchain.text_splitter import (
    RecursiveCharacterTextSplitter,
    CharacterTextSplitter,
    SemanticTextSplitter
)
from langchain_community.vectorstores import FAISS, Chroma, Qdrant
from langchain_community.document_loaders import PyPDFLoader
from langchain.chains import ConversationalRetrievalChain
from langchain_community.embeddings import HuggingFaceEmbeddings 
from langchain_community.llms import HuggingFaceEndpoint
from langchain.memory import ConversationBufferMemory
import torch

# Constants
list_llm = ["meta-llama/Meta-Llama-3-8B-Instruct", "mistralai/Mistral-7B-Instruct-v0.2"]
list_llm_simple = [os.path.basename(llm) for llm in list_llm]
api_token = os.getenv("HF_TOKEN")

# Text splitting strategies
def get_text_splitter(strategy: str, chunk_size: int = 1024, chunk_overlap: int = 64):
    splitters = {
        "recursive": RecursiveCharacterTextSplitter(
            chunk_size=chunk_size,
            chunk_overlap=chunk_overlap
        ),
        "fixed": CharacterTextSplitter(
            chunk_size=chunk_size,
            chunk_overlap=chunk_overlap
        ),
        "semantic": SemanticTextSplitter(
            embedding_function=HuggingFaceEmbeddings().embed_query,
            chunk_size=chunk_size,
            chunk_overlap=chunk_overlap
        )
    }
    return splitters.get(strategy)

# Load and split PDF document
def load_doc(list_file_path: List[str], splitting_strategy: str = "recursive"):
    loaders = [PyPDFLoader(x) for x in list_file_path]
    pages = []
    for loader in loaders:
        pages.extend(loader.load())
    
    text_splitter = get_text_splitter(splitting_strategy)
    doc_splits = text_splitter.split_documents(pages)
    return doc_splits

# Vector database creation functions
def create_faiss_db(splits, embeddings):
    return FAISS.from_documents(splits, embeddings)

def create_chroma_db(splits, embeddings):
    return Chroma.from_documents(splits, embeddings)

def create_qdrant_db(splits, embeddings):
    return Qdrant.from_documents(
        splits,
        embeddings,
        location=":memory:",
        collection_name="pdf_docs"
    )

def create_db(splits, db_choice: str = "faiss"):
    embeddings = HuggingFaceEmbeddings()
    db_creators = {
        "faiss": create_faiss_db,
        "chroma": create_chroma_db,
        "qdrant": create_qdrant_db
    }
    return db_creators[db_choice](splits, embeddings)

# Updated evaluation functions
def load_evaluation_dataset():
    # Load example dataset from RAGAS
    dataset = load_dataset("explodinggradients/fiqa", split="test")
    return dataset

def prepare_ragas_dataset(qa_chain, dataset):
    # Sample a few examples for evaluation
    eval_samples = dataset.select(range(5))
    
    ragas_dataset = []
    for sample in eval_samples:
        question = sample["question"]
        
        # Get response from the chain
        response = qa_chain.invoke({
            "question": question,
            "chat_history": []
        })
        
        ragas_dataset.append({
            "question": question,
            "answer": response["answer"],
            "contexts": [doc.page_content for doc in response["source_documents"]],
            "ground_truth": sample["answer"]
        })
    
    return ragas_dataset

def evaluate_rag_pipeline(qa_chain, dataset):
    ragas_dataset = prepare_ragas_dataset(qa_chain, dataset)
    
    # Run RAGAS evaluation
    results = evaluate(
        ragas_dataset,
        metrics=[
            context_precision,
            faithfulness,
            answer_relevancy,
            context_recall,
            answer_correctness,
            answer_similarity
        ]
    )
    
    # Convert results to a dictionary
    return {
        "context_precision": float(results["context_precision"]),
        "faithfulness": float(results["faithfulness"]),
        "answer_relevancy": float(results["answer_relevancy"]),
        "context_recall": float(results["context_recall"]),
        "answer_correctness": float(results["answer_correctness"]),
        "answer_similarity": float(results["answer_similarity"])
    }

# Initialize langchain LLM chain
def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
    if llm_model == "meta-llama/Meta-Llama-3-8B-Instruct":
        llm = HuggingFaceEndpoint(
            repo_id=llm_model,
            huggingfacehub_api_token=api_token,
            temperature=temperature,
            max_new_tokens=max_tokens,
            top_k=top_k,
        )
    else:
        llm = HuggingFaceEndpoint(
            huggingfacehub_api_token=api_token,
            repo_id=llm_model,
            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,
        verbose=False,
    )
    return qa_chain

# Initialize database with chunking strategy and vector DB choice
def initialize_database(list_file_obj, splitting_strategy, db_choice, progress=gr.Progress()):
    list_file_path = [x.name for x in list_file_obj if x is not None]
    doc_splits = load_doc(list_file_path, splitting_strategy)
    vector_db = create_db(doc_splits, db_choice)
    return vector_db, f"Database created using {splitting_strategy} splitting and {db_choice} vector database!"

# Formatting chat history
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

# Conversation function
def conversation(qa_chain, message, history):
    formatted_chat_history = format_chat_history(message, history)
    response = qa_chain.invoke({
        "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=gr.themes.Default(primary_hue="red", secondary_hue="pink", neutral_hue="sky")) as demo:
        vector_db = gr.State()
        qa_chain = gr.State()
        
        gr.HTML("<center><h1>Enhanced RAG PDF Chatbot</h1></center>")
        gr.Markdown("""<b>Query your PDF documents with advanced RAG capabilities!</b>""")
        
        with gr.Row():
            with gr.Column(scale=86):
                gr.Markdown("<b>Step 1 - Configure and Initialize RAG Pipeline</b>")
                with gr.Row():
                    document = gr.Files(height=300, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload PDF documents")
                
                with gr.Row():
                    splitting_strategy = gr.Radio(
                        ["recursive", "fixed", "semantic"],
                        label="Text Splitting Strategy",
                        value="recursive"
                    )
                    db_choice = gr.Radio(
                        ["faiss", "chroma", "qdrant"],
                        label="Vector Database",
                        value="faiss"
                    )
                
                with gr.Row():
                    db_btn = gr.Button("Create vector database")
                    evaluate_btn = gr.Button("Evaluate RAG Pipeline")
                
                with gr.Row():
                    db_progress = gr.Textbox(value="Not initialized", show_label=False)
                    evaluation_results = gr.JSON(label="Evaluation Results")
                
                gr.Markdown("<b>Select Large Language Model (LLM) and input parameters</b>")
                with gr.Row():
                    llm_btn = gr.Radio(list_llm_simple, label="Available LLMs", value=list_llm_simple[0], type="index")
                
                with gr.Row():
                    with gr.Accordion("LLM input parameters", open=False):
                        slider_temperature = gr.Slider(minimum=0.01, maximum=1.0, value=0.5, step=0.1, label="Temperature")
                        slider_maxtokens = gr.Slider(minimum=128, maximum=9192, value=4096, step=128, label="Max New Tokens")
                        slider_topk = gr.Slider(minimum=1, maximum=10, value=3, step=1, label="top-k")
                
                with gr.Row():
                    qachain_btn = gr.Button("Initialize Question Answering Chatbot")
                    llm_progress = gr.Textbox(value="Not initialized", show_label=False)
            
            with gr.Column(scale=200):
                gr.Markdown("<b>Step 2 - Chat with your Document</b>")
                chatbot = gr.Chatbot(height=505)
                
                with gr.Accordion("Relevant context from the source document", open=False):
                    with gr.Row():
                        doc_source1 = gr.Textbox(label="Reference 1", lines=2, container=True, scale=20)
                        source1_page = gr.Number(label="Page", scale=1)
                    with gr.Row():
                        doc_source2 = gr.Textbox(label="Reference 2", lines=2, container=True, scale=20)
                        source2_page = gr.Number(label="Page", scale=1)
                    with gr.Row():
                        doc_source3 = gr.Textbox(label="Reference 3", lines=2, container=True, scale=20)
                        source3_page = gr.Number(label="Page", scale=1)
                
                with gr.Row():
                    msg = gr.Textbox(placeholder="Ask a question", container=True)
                with gr.Row():
                    submit_btn = gr.Button("Submit")
                    clear_btn = gr.ClearButton([msg, chatbot], value="Clear")
        
        # Event handlers
        db_btn.click(
            initialize_database,
            inputs=[document, splitting_strategy, db_choice],
            outputs=[vector_db, db_progress]
        )
        
        evaluate_btn.click(
            lambda qa_chain: evaluate_rag_pipeline(qa_chain, load_evaluation_dataset()) if qa_chain else None,
            inputs=[qa_chain],
            outputs=[evaluation_results]
        )
        
        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
        )
        
        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()