app.py

# import gradio as gr
# from langchain_mistralai.chat_models import ChatMistralAI
# from langchain.prompts import ChatPromptTemplate
# import os
# from pathlib import Path
# from typing import List, Dict, Optional
# import json
# import faiss
# import numpy as np
# from langchain.schema import Document
# from sentence_transformers import SentenceTransformer
# import pickle
# import re

# os.environ.get('HUGGINGFACE_TOKEN')

# class RAGLoader:
#     def __init__(self,
#                  docs_folder: str = "./docs",
#                  splits_folder: str = "./splits",
#                  index_folder: str = "./index",):
#                  # model_name: str = "intfloat/multilingual-e5-large")
#         """
#         Initialise le RAG Loader

#         Args:
#             docs_folder: Dossier contenant les documents sources
#             splits_folder: Dossier où seront stockés les morceaux de texte
#             index_folder: Dossier où sera stocké l'index FAISS
#             model_name: Nom du modèle SentenceTransformer à utiliser
#         """
#         self.docs_folder = Path(docs_folder)
#         self.splits_folder = Path(splits_folder)
#         self.index_folder = Path(index_folder)
#         # self.model_name = model_name

#         # Créer les dossiers s'ils n'existent pas
#         self.splits_folder.mkdir(parents=True, exist_ok=True)
#         self.index_folder.mkdir(parents=True, exist_ok=True)

#         # Chemins des fichiers
#         self.splits_path = self.splits_folder / "splits.json"
#         self.index_path = self.index_folder / "faiss.index"
#         self.documents_path = self.index_folder / "documents.pkl"

#         # Initialiser le modèle
#         # self.model = None
#         self.index = None
#         self.indexed_documents = None

#     def encode(self,payload):
#         token = os.environ.get('HUGGINGFACE_TOKEN')
#         API_URL = "https://api-inference.huggingface.co/models/intfloat/multilingual-e5-large"
#         headers = {"Authorization": "Bearer {token}"} 
#         response = requests.post(API_URL, headers=headers, json=payload)
#         return response.json()

#     def load_and_split_texts(self) -> List[Document]:
#         """
#         Charge les textes du dossier docs, les découpe en morceaux et les sauvegarde
#         dans un fichier JSON unique.

#         Returns:
#             Liste de Documents contenant les morceaux de texte et leurs métadonnées
#         """
#         documents = []

#         # Vérifier d'abord si les splits existent déjà
#         if self._splits_exist():
#             print("Chargement des splits existants...")
#             return self._load_existing_splits()

#         print("Création de nouveaux splits...")
#         # Parcourir tous les fichiers du dossier docs
#         for file_path in self.docs_folder.glob("*.txt"):
#             with open(file_path, 'r', encoding='utf-8') as file:
#                 text = file.read()

#                 # Découper le texte en phrases
#                 # chunks = [chunk.strip() for chunk in text.split('.') if chunk.strip()]
#                 chunks = [s.strip() for s in re.split(r'(?<=[.!?])\s+', text) if s.strip()]

#                 # Créer un Document pour chaque morceau
#                 for i, chunk in enumerate(chunks):
#                     doc = Document(
#                         page_content=chunk,
#                         metadata={
#                             'source': file_path.name,
#                             'chunk_id': i,
#                             'total_chunks': len(chunks)
#                         }
#                     )
#                     documents.append(doc)

#         # Sauvegarder tous les splits dans un seul fichier JSON
#         self._save_splits(documents)

#         print(f"Nombre total de morceaux créés: {len(documents)}")
#         return documents

#     def _splits_exist(self) -> bool:
#         """Vérifie si le fichier de splits existe"""
#         return self.splits_path.exists()

#     def _save_splits(self, documents: List[Document]):
#         """Sauvegarde tous les documents découpés dans un seul fichier JSON"""
#         splits_data = {
#             'splits': [
#                 {
#                     'text': doc.page_content,
#                     'metadata': doc.metadata
#                 }
#                 for doc in documents
#             ]
#         }

#         with open(self.splits_path, 'w', encoding='utf-8') as f:
#             json.dump(splits_data, f, ensure_ascii=False, indent=2)

#     def _load_existing_splits(self) -> List[Document]:
#         """Charge les splits depuis le fichier JSON unique"""
#         with open(self.splits_path, 'r', encoding='utf-8') as f:
#             splits_data = json.load(f)

#         documents = [
#             Document(
#                 page_content=split['text'],
#                 metadata=split['metadata']
#             )
#             for split in splits_data['splits']
#         ]

#         print(f"Nombre de splits chargés: {len(documents)}")
#         return documents

#     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: Optional[List[Document]] = None) -> bool:
#         """
#         Crée un nouvel index FAISS à partir des documents.
#         Si aucun document n'est fourni, charge les documents depuis le fichier JSON.

#         Args:
#             documents: Liste optionnelle de Documents à indexer

#         Returns:
#             bool: True si l'index a été créé avec succès, False sinon
#         """
#         try:
#             # # Initialiser le modèle si nécessaire
#             # if self.model is None:
#             #     print("Chargement du modèle...")
#             #     self.model = SentenceTransformer(self.model_name)

#             # Charger les documents si non fournis
#             if documents is None:
#                 documents = self.load_and_split_texts()

#             if not documents:
#                 print("Aucun document à indexer.")
#                 return False

#             print("Création des embeddings...")
#             texts = [doc.page_content for doc in documents]
#             embeddings = self.encode(texts)

#             # Initialiser l'index FAISS
#             dimension = embeddings.shape[1]
#             self.index = faiss.IndexFlatL2(dimension)

#             # Ajouter les vecteurs à l'index
#             self.index.add(np.array(embeddings).astype('float32'))

#             # Sauvegarder l'index
#             print("Sauvegarde de l'index...")
#             faiss.write_index(self.index, str(self.index_path))

#             # Sauvegarder les documents associés
#             self.indexed_documents = documents
#             with open(self.documents_path, 'wb') as f:
#                 pickle.dump(documents, f)

#             print(f"Index créé avec succès : {self.index.ntotal} vecteurs")
#             return True

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

#     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 = 10):
#         """
#         Crée un retriever pour l'utilisation avec LangChain

#         Args:
#             k: Nombre de documents similaires à retourner

#         Returns:
#             Callable: Fonction de recherche compatible avec LangChain
#         """
#         if self.index is None:
#             if not self.load_index():
#                 if not self.create_index():
#                     raise ValueError("Impossible de charger ou créer l'index")

#         # if self.model is None:
#         #     self.model = SentenceTransformer(self.model_name)

#         def retriever_function(query: str) -> List[Document]:
#             # Créer l'embedding de la requête
#             query_embedding = self.encode([query])[0]

#             # Rechercher les documents similaires
#             distances, indices = self.index.search(
#                 np.array([query_embedding]).astype('float32'),
#                 k
#             )

#             # Retourner les documents trouvés
#             results = []
#             for idx in indices[0]:
#                 if idx != -1:  # FAISS retourne -1 pour les résultats invalides
#                     results.append(self.indexed_documents[idx])

#             return results

#         return retriever_function

# # Initialize the RAG system
# llm = ChatMistralAI(model="mistral-large-latest", mistral_api_key="QK0ZZpSxQbCEVgOLtI6FARQVmBYc6WGP")
# rag_loader = RAGLoader()
# retriever = rag_loader.get_retriever(k=10)

# 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]:
#     """
#     Process a question and return both the answer and the relevant context
#     """
#     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)
#     return response.content, context

# def gradio_interface(question: str) -> tuple[str, str]:
#     """
#     Gradio interface function that returns both answer and context as a tuple
#     """
#     return process_question(question)

# # 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;
# }
# """

# # Define the Gradio interface
# 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"
#         )

#         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=gradio_interface,
#             inputs=input_text,
#             outputs=[answer_box, context_box]
#         )

# # Launch the interface
# if __name__ == "__main__":
#     iface.launch(share=True)


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 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 get_retriever(self, k: int = 10):
        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
llm = ChatMistralAI(
    model="mistral-large-latest",
    mistral_api_key="QK0ZZpSxQbCEVgOLtI6FARQVmBYc6WGP",
    temperature=0.1  # Lower temperature for faster responses
)

rag_loader = OptimizedRAGLoader()
retriever = rag_loader.get_retriever(k=5)  # Reduced k for faster retrieval

# Cache for processed questions
question_cache = {}

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

# 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"
        )

# Launch with optimized settings
if __name__ == "__main__":
    iface.queue(concurrency_count=3).launch(
        share=True,
        server_name="0.0.0.0",
        server_port=7860,
        enable_queue=True
    )