Update app.py

app.py

@@ -1,6 +1,7 @@
 from fastapi import FastAPI
 from pydantic import BaseModel
 from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
+from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModelForSequenceClassification
 from duckduckgo_search import DDGS
 from fastapi.middleware.cors import CORSMiddleware
 from mtranslate import translate
@@ -26,21 +27,17 @@ tokenizer_c, model_c = None, None
 
 def load_models():
     global tokenizer, model, bert_tokenizer, bert_model, tokenizer_c, model_c
-    from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModelForSequenceClassification
-
     if tokenizer is None or model is None:
         tokenizer = AutoTokenizer.from_pretrained("fatmata/gpt-psybot")
         model = AutoModelForCausalLM.from_pretrained("fatmata/gpt-psybot")
-
     if bert_tokenizer is None or bert_model is None:
         bert_tokenizer = AutoTokenizer.from_pretrained("fatmata/bert_model")
         bert_model = AutoModelForSequenceClassification.from_pretrained("fatmata/bert_model")
-
     if tokenizer_c is None or model_c is None:
         tokenizer_c = AutoTokenizer.from_pretrained("fatmata/mini_bert")
         model_c = AutoModelForSequenceClassification.from_pretrained("fatmata/mini_bert")
 
-# ---------------- Nettoyage des réponses ----------------
+# ---------------- Nettoyage ----------------
 def clean_response(text):
     text = re.sub(r'<[^>]+>', '', text)
     text = re.split(r'</(Bot|name|opinion|User|[a-zA-Z]*)>', text)[0]
@@ -84,7 +81,7 @@ def search_duckduckgo(query, max_results=3):
     try:
         results = list(DDGS().text(query, max_results=max_results))
         return [r["body"] for r in results if "body" in r] or ["Je n'ai pas trouvé d'informations."]
-    except Exception as e:
+    except:
         return [f"Erreur recherche : {str(e)}"]
 
 def generate_response(user_input):
@@ -133,12 +130,10 @@ def classify_and_respond(text, original_lang):
         response = search_duckduckgo(text)
         translated_response = [translate(r, original_lang) for r in response]
         return translated_response, "recherche", [], ["Recherche externe"]
-    
     compound, is_unacceptable, emotions = classify_emotion(text)
     if is_unacceptable and abs(compound)>50:
         alert = translate("Je ressens beaucoup de tension dans votre message. Essayez de vous calmer.", original_lang)
         return [alert], "non acceptable", emotions, ["Emotion inacceptable"]
-    
     gpt_response = generate_response(text)
     translated_gpt = translate(gpt_response, original_lang)
     return [translated_gpt], "gpt", emotions, ["Réponse GPT"]