Update app.py

app.py

@@ -7,6 +7,7 @@ import os
 import asyncio
 import nltk
 from nltk.tokenize import sent_tokenize
+import torch
 
 # Téléchargement de punkt_tab avec gestion d'erreur
 try:
@@ -20,15 +21,23 @@ if not HF_TOKEN:
     st.error("Erreur : Clé API Hugging Face (HF_TOKEN) manquante. Veuillez configurer HF_TOKEN dans les variables d'environnement.")
     st.stop()
 
-# Initialisation des modèles dans st.session_state pour éviter le rechargement
-if 'classifier' not in st.session_state:
-    st.session_state.classifier = pipeline("sentiment-analysis", model="mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis", device="cpu")
+# Fonctions pour charger les modèles avec st.cache_resource
+@st.cache_resource
+def load_classifier():
+    return pipeline("sentiment-analysis", model="mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis", device="cpu", map_location="cpu")
 
-if 'translator_to_en' not in st.session_state:
-    st.session_state.translator_to_en = pipeline("translation", model="Helsinki-NLP/opus-mt-mul-en", device="cpu")
+@st.cache_resource
+def load_translator_to_en():
+    return pipeline("translation", model="Helsinki-NLP/opus-mt-mul-en", device="cpu", map_location="cpu")
 
-if 'translator_to_fr' not in st.session_state:
-    st.session_state.translator_to_fr = pipeline("translation", model="Helsinki-NLP/opus-mt-en-fr", device="cpu")
+@st.cache_resource
+def load_translator_to_fr():
+    return pipeline("translation", model="Helsinki-NLP/opus-mt-en-fr", device="cpu", map_location="cpu")
+
+# Charger les modèles une seule fois
+classifier = load_classifier()
+translator_to_en = load_translator_to_en()
+translator_to_fr = load_translator_to_fr()
 
 # Fonction pour appeler l'API Zephyr avec des paramètres ajustés
 async def call_zephyr_api(prompt, mode, hf_token=HF_TOKEN):
@@ -55,7 +64,7 @@ def safe_translate_to_fr(text, max_length=512):
         sentences = sent_tokenize(text)
         translated_sentences = []
         for sentence in sentences:
-            translated = st.session_state.translator_to_fr(sentence, max_length=max_length)[0]['translation_text']
+            translated = translator_to_fr(sentence, max_length=max_length)[0]['translation_text']
             translated_sentences.append(translated)
         return " ".join(translated_sentences)
     except Exception as e:
@@ -110,13 +119,13 @@ async def full_analysis(text, mode, detail_mode, history):
     progress_bar.progress(25)
 
     if lang != "en":
-        text_en = st.session_state.translator_to_en(text, max_length=512)[0]['translation_text']
+        text_en = translator_to_en(text, max_length=512)[0]['translation_text']
     else:
         text_en = text
 
     # Étape 2 : Analyse du sentiment
     status_text.write("Analyse en cours... (Étape 2 : Analyse du sentiment)")
-    result = st.session_state.classifier(text_en)  # Utilisation du modèle depuis st.session_state
+    result = classifier(text_en)
     result = result[0]
     sentiment_output = f"Sentiment prédictif : {result['label']} (Score: {result['score']:.2f})"
     sentiment_gauge = create_sentiment_gauge(result['label'], result['score'])