import gradio as gr
import os
from typing import List, Dict
import numpy as np
from datasets import load_dataset
from langchain.text_splitter import (
    RecursiveCharacterTextSplitter,
    CharacterTextSplitter,
    TokenTextSplitter
)
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
from sentence_transformers import SentenceTransformer, util
import torch

# Constants and setup
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")

# Initialize sentence transformer for evaluation
sentence_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')

# 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
        ),
        "token": TokenTextSplitter(
            chunk_size=chunk_size,
            chunk_overlap=chunk_overlap
        )
    }
    return splitters.get(strategy)

# Custom evaluation metrics
def calculate_semantic_similarity(text1: str, text2: str) -> float:
    embeddings1 = sentence_model.encode([text1], convert_to_tensor=True)
    embeddings2 = sentence_model.encode([text2], convert_to_tensor=True)
    similarity = util.pytorch_cos_sim(embeddings1, embeddings2)
    return float(similarity[0][0])

def evaluate_response(question: str, answer: str, ground_truth: str, contexts: List[str]) -> Dict[str, float]:
    # Answer similarity with ground truth
    answer_similarity = calculate_semantic_similarity(answer, ground_truth)
    
    # Context relevance - average similarity between question and contexts
    context_scores = [calculate_semantic_similarity(question, ctx) for ctx in contexts]
    context_relevance = np.mean(context_scores)
    
    # Answer relevance - similarity between question and answer
    answer_relevance = calculate_semantic_similarity(question, answer)
    
    return {
        "answer_similarity": answer_similarity,
        "context_relevance": context_relevance,
        "answer_relevance": answer_relevance,
        "average_score": np.mean([answer_similarity, context_relevance, answer_relevance])
    }

# 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)

def load_evaluation_dataset():
    dataset = load_dataset("explodinggradients/fiqa", split="test", trust_remote_code=True)
    return dataset

def evaluate_rag_pipeline(qa_chain, dataset):
    # Sample a few examples for evaluation
    eval_samples = dataset.select(range(5))
    
    results = []
    for sample in eval_samples:
        question = sample["question"]
        
        # Get response from the chain
        response = qa_chain.invoke({
            "question": question,
            "chat_history": []
        })
        
        # Evaluate response
        eval_result = evaluate_response(
            question=question,
            answer=response["answer"],
            ground_truth=sample["answer"],
            contexts=[doc.page_content for doc in response["source_documents"]]
        )
        
        results.append(eval_result)
    
    # Calculate average scores across all samples
    avg_results = {
        metric: float(np.mean([r[metric] for r in results]))
        for metric in results[0].keys()
    }
    
    return avg_results

# Initialize langchain LLM chain
def initialize_llmchain(llm_choice, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
    # Get the full model name from the index
    llm_model = list_llm[llm_choice]
    
    llm = HuggingFaceEndpoint(
        repo_id=llm_model,
        huggingfacehub_api_token=api_token,
        temperature=temperature,
        max_new_tokens=max_tokens,
        top_k=top_k,
        model=llm_model  # Add model parameter
    )
    
    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, "LLM initialized successfully!"

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!"

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.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", "token"],
                        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_llmchain,  # Fixed function name here
            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()