import gradio as gr
from langchain_mistralai.chat_models import ChatMistralAI
from langchain.prompts import ChatPromptTemplate
import os
from pathlib import Path
import json
import faiss
import numpy as np
from langchain.schema import Document
import pickle
import re
import requests
from functools import lru_cache
import torch
from sentence_transformers import SentenceTransformer
import threading
from queue import Queue
import concurrent.futures

class OptimizedRAGLoader:
    def __init__(self,
                 docs_folder: str = "./docs",
                 splits_folder: str = "./splits",
                 index_folder: str = "./index"):
        
        self.docs_folder = Path(docs_folder)
        self.splits_folder = Path(splits_folder)
        self.index_folder = Path(index_folder)
        
        # Create folders if they don't exist
        for folder in [self.splits_folder, self.index_folder]:
            folder.mkdir(parents=True, exist_ok=True)
            
        # File paths
        self.splits_path = self.splits_folder / "splits.json"
        self.index_path = self.index_folder / "faiss.index"
        self.documents_path = self.index_folder / "documents.pkl"
        
        # Initialize components
        self.index = None
        self.indexed_documents = None
        
        # Initialize encoder model
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.encoder = SentenceTransformer("intfloat/multilingual-e5-large")
        self.encoder.to(self.device)
        
        # Initialize thread pool
        self.executor = concurrent.futures.ThreadPoolExecutor(max_workers=4)
        
        # Initialize response cache
        self.response_cache = {}
        
    @lru_cache(maxsize=1000)
    def encode(self, text: str):
        """Cached encoding function"""
        with torch.no_grad():
            embeddings = self.encoder.encode(
                text,
                convert_to_numpy=True,
                normalize_embeddings=True
            )
        return embeddings
    
    def batch_encode(self, texts: list):
        """Batch encoding for multiple texts"""
        with torch.no_grad():
            embeddings = self.encoder.encode(
                texts,
                batch_size=32,
                convert_to_numpy=True,
                normalize_embeddings=True,
                show_progress_bar=False
            )
        return embeddings

    def load_and_split_texts(self):
        if self._splits_exist():
            return self._load_existing_splits()
            
        documents = []
        futures = []
        
        for file_path in self.docs_folder.glob("*.txt"):
            future = self.executor.submit(self._process_file, file_path)
            futures.append(future)
            
        for future in concurrent.futures.as_completed(futures):
            documents.extend(future.result())
            
        self._save_splits(documents)
        return documents
    
    def _process_file(self, file_path):
        with open(file_path, 'r', encoding='utf-8') as file:
            text = file.read()
            chunks = [s.strip() for s in re.split(r'(?<=[.!?])\s+', text) if s.strip()]
            
            return [
                Document(
                    page_content=chunk,
                    metadata={
                        'source': file_path.name,
                        'chunk_id': i,
                        'total_chunks': len(chunks)
                    }
                )
                for i, chunk in enumerate(chunks)
            ]

    def load_index(self) -> bool:
        """
        Charge l'index FAISS et les documents associés s'ils existent

        Returns:
            bool: True si l'index a été chargé, False sinon
        """
        if not self._index_exists():
            print("Aucun index trouvé.")
            return False

        print("Chargement de l'index existant...")
        try:
            # Charger l'index FAISS
            self.index = faiss.read_index(str(self.index_path))

            # Charger les documents associés
            with open(self.documents_path, 'rb') as f:
                self.indexed_documents = pickle.load(f)

            print(f"Index chargé avec {self.index.ntotal} vecteurs")
            return True

        except Exception as e:
            print(f"Erreur lors du chargement de l'index: {e}")
            return False

    def create_index(self, documents=None):
        if documents is None:
            documents = self.load_and_split_texts()
            
        if not documents:
            return False
            
        texts = [doc.page_content for doc in documents]
        embeddings = self.batch_encode(texts)
        
        dimension = embeddings.shape[1]
        self.index = faiss.IndexFlatL2(dimension)
        
        if torch.cuda.is_available():
            # Use GPU for FAISS if available
            res = faiss.StandardGpuResources()
            self.index = faiss.index_cpu_to_gpu(res, 0, self.index)
            
        self.index.add(np.array(embeddings).astype('float32'))
        self.indexed_documents = documents
        
        # Save index and documents
        cpu_index = faiss.index_gpu_to_cpu(self.index) if torch.cuda.is_available() else self.index
        faiss.write_index(cpu_index, str(self.index_path))
        
        with open(self.documents_path, 'wb') as f:
            pickle.dump(documents, f)
            
        return True

    def _index_exists(self) -> bool:
        """Vérifie si l'index et les documents associés existent"""
        return self.index_path.exists() and self.documents_path.exists()

    def get_retriever(self, k: int = 5):
        if self.index is None:
            if not self.load_index():
                if not self.create_index():
                    raise ValueError("Unable to load or create index")

        def retriever_function(query: str) -> list:
            # Check cache first
            cache_key = f"{query}_{k}"
            if cache_key in self.response_cache:
                return self.response_cache[cache_key]

            query_embedding = self.encode(query)
            
            distances, indices = self.index.search(
                np.array([query_embedding]).astype('float32'),
                k
            )
            
            results = [
                self.indexed_documents[idx]
                for idx in indices[0]
                if idx != -1
            ]
            
            # Cache the results
            self.response_cache[cache_key] = results
            return results
            
        return retriever_function

# Initialize components
mistral_api_key = os.getenv("mistral_api_key")
llm = ChatMistralAI(
    model="mistral-large-latest",
    mistral_api_key=mistral_api_key,
    temperature=0.1  # Lower temperature for faster responses
)

rag_loader = OptimizedRAGLoader()
retriever = rag_loader.get_retriever(k=10)  # Reduced k for faster retrieval

# Cache for processed questions
question_cache = {}

prompt_template = ChatPromptTemplate.from_messages([
    ("system", """أنت مساعد مفيد يجيب على الأسئلة باللغة العربية باستخدام المعلومات المقدمة.
    استخدم المعلومات التالية للإجابة على السؤال:

    {context}

    إذا لم تكن المعلومات كافية للإجابة على السؤال بشكل كامل، قم بتوضيح ذلك.
    أجب بشكل موجز ودقيق."""),
    ("human", "{question}")
])

# def process_question(question: str) -> tuple[str, str]:
#     # Check cache first
#     if question in question_cache:
#         return question_cache[question]
    
#     relevant_docs = retriever(question)
#     context = "\n".join([doc.page_content for doc in relevant_docs])
    
#     prompt = prompt_template.format_messages(
#         context=context,
#         question=question
#     )
    
#     response = llm(prompt)
#     result = (response.content, context)
    
#     # Cache the result
#     question_cache[question] = result
#     return result

# # Custom CSS for right-aligned text in textboxes
# custom_css = """
# .rtl-text {
#     text-align: right !important;
#     direction: rtl !important;
# }
# .rtl-text textarea {
#     text-align: right !important;
#     direction: rtl !important;
# }
# """

# # Gradio interface with queue
# with gr.Blocks(css=custom_css) as iface:
#     with gr.Column():
#         input_text = gr.Textbox(
#             label="السؤال",
#             placeholder="اكتب سؤالك هنا...",
#             lines=2,
#             elem_classes="rtl-text"
#         )
        
#         with gr.Row():
#             answer_box = gr.Textbox(
#                 label="الإجابة",
#                 lines=4,
#                 elem_classes="rtl-text"
#             )
#             context_box = gr.Textbox(
#                 label="السياق المستخدم",
#                 lines=8,
#                 elem_classes="rtl-text"
#             )
        
#         submit_btn = gr.Button("إرسال")
        
#         submit_btn.click(
#             fn=process_question,
#             inputs=input_text,
#             outputs=[answer_box, context_box],
#             api_name="predict"
#         )

# if __name__ == "__main__":
#     iface.launch(
#         share=True,
#         server_name="0.0.0.0",
#         server_port=7860,
#         max_threads=3,  # Controls concurrency
#         show_error=True
#     )

def process_question(question: str):  
    """  
    Process the question and yield the answer progressively.  
    """  
    # Check cache first  
    if question in question_cache:  
        yield question_cache[question]  # Retourne directement depuis le cache si disponible  

    relevant_docs = retriever(question)  
    context = "\n".join([doc.page_content for doc in relevant_docs])  

    prompt = prompt_template.format_messages(  
        context=context,  
        question=question  
    )  

    response = ""  # Initialise la réponse  
    # Ici, nous supposons que 'llm.stream' est un générateur qui renvoie des chunks  
    for chunk in llm.stream(prompt):  # suppose que llm.stream renvoie des chunks de réponse  
        if isinstance(chunk, str):  
            response += chunk  # Accumulez la réponse si c'est déjà une chaîne  
        else:  
            response += chunk.content  # Sinon, prenez le contenu du chunk (si chunk est un type d'objet spécifique)  

        yield response, context  # Renvoie la réponse mise à jour et le contexte  

    # Mettez le résultat en cache à la fin  
    question_cache[question] = (response, context)   

# Custom CSS for right-aligned text in textboxes  
custom_css = """  
.rtl-text {  
    text-align: right !important;  
    direction: rtl !important;  
}  
.rtl-text textarea {  
    text-align: right !important;  
    direction: rtl !important;  
}  
"""  

# Gradio interface with queue  
with gr.Blocks(css=custom_css) as iface:  
    with gr.Column():  
        input_text = gr.Textbox(  
            label="السؤال",  
            placeholder="اكتب سؤالك هنا...",  
            lines=2,  
            elem_classes="rtl-text"  
        )  
        
        with gr.Row():  
            answer_box = gr.Textbox(  
                label="الإجابة",  
                lines=4,  
                elem_classes="rtl-text"  
            )  
            context_box = gr.Textbox(  
                label="السياق المستخدم",  
                lines=8,  
                elem_classes="rtl-text"  
            )  
        
        submit_btn = gr.Button("إرسال")  
        
        submit_btn.click(  
            fn=process_question,  
            inputs=input_text,  
            outputs=[answer_box, context_box],  
            api_name="predict",  
            queue=True  # Utiliser le système de queue pour un traitement asynchrone  
        )  

if __name__ == "__main__":  
    iface.launch(  
        share=True,  
        server_name="0.0.0.0",  
        server_port=7860,  
        max_threads=3,  # Controls concurrency  
        show_error=True  
    )