Update app.py

app.py

@@ -16,8 +16,8 @@ class RAGLoader:
     def __init__(self,
                  docs_folder: str = "./docs",
                  splits_folder: str = "./splits",
-                 index_folder: str = "./index",
-                 model_name: str = "intfloat/multilingual-e5-large"):
+                 index_folder: str = "./index",):
+                 # model_name: str = "intfloat/multilingual-e5-large")
         """
         Initialise le RAG Loader
 
@@ -30,7 +30,7 @@ class RAGLoader:
         self.docs_folder = Path(docs_folder)
         self.splits_folder = Path(splits_folder)
         self.index_folder = Path(index_folder)
-        self.model_name = model_name
+        # self.model_name = model_name
 
         # Créer les dossiers s'ils n'existent pas
         self.splits_folder.mkdir(parents=True, exist_ok=True)
@@ -42,10 +42,16 @@ class RAGLoader:
         self.documents_path = self.index_folder / "documents.pkl"
 
         # Initialiser le modèle
-        self.model = None
+        # self.model = None
         self.index = None
         self.indexed_documents = None
 
+    def encode(self,payload):
+        API_URL = "https://api-inference.huggingface.co/models/intfloat/multilingual-e5-large"
+        headers = {"Authorization": "Bearer ffff"} 
+        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
@@ -163,10 +169,10 @@ class RAGLoader:
             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)
+            # # 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:
@@ -178,7 +184,7 @@ class RAGLoader:
 
             print("Création des embeddings...")
             texts = [doc.page_content for doc in documents]
-            embeddings = self.model.encode(texts, show_progress_bar=True)
+            embeddings = self.encode(texts)
 
             # Initialiser l'index FAISS
             dimension = embeddings.shape[1]
@@ -222,12 +228,12 @@ class RAGLoader:
                 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)
+        # 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.model.encode([query])[0]
+            query_embedding = self.encode([query])[0]
 
             # Rechercher les documents similaires
             distances, indices = self.index.search(