Update app.py

app.py

@@ -8,33 +8,27 @@ from transformers import pipeline
 logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
 
 app = FastAPI()
-
 models = None
 
 def load_models():
     global models
     if models is not None:
         return
-
     logging.info("Loading models...")
 
-    # === NEW ENGLISH MODEL ===
-    en_vectorizer = joblib.load(hf_hub_download("E-motionAssistant/English_LR_Model_New", "tfidf_vectorizer.joblib"))
-    en_classifier = joblib.load(hf_hub_download("E-motionAssistant/English_LR_Model_New", "logreg_model.joblib"))
-    en_label_encoder = joblib.load(hf_hub_download("E-motionAssistant/English_LR_Model_New", "label_encoder.joblib"))
+    en_vectorizer = joblib.load(hf_hub_download("E-motionAssistant/Englsih_Trained_Model_LR", "tfidf_vectorizer.joblib"))
+    en_classifier = joblib.load(hf_hub_download("E-motionAssistant/Englsih_Trained_Model_LR", "logreg_model.joblib"))
+    en_label_encoder = joblib.load(hf_hub_download("E-motionAssistant/Englsih_Trained_Model_LR", "label_encoder.joblib"))
 
-    # Sinhala Model (unchanged)
     si_vectorizer = joblib.load(hf_hub_download("E-motionAssistant/Sinhala_Text_Emotion_Model_LR", "tfidf_vectorizer.joblib"))
     si_classifier = joblib.load(hf_hub_download("E-motionAssistant/Sinhala_Text_Emotion_Model_LR", "logreg_model.joblib"))
     si_label_encoder = joblib.load(hf_hub_download("E-motionAssistant/Sinhala_Text_Emotion_Model_LR", "label_encoder.joblib"))
 
-    # Tamil Model (unchanged)
     tamil_pipe = pipeline("text-classification", model="E-motionAssistant/Tamil_Emotion_Recognition_Model", device=-1)
 
     models = (en_vectorizer, en_classifier, en_label_encoder,
               si_vectorizer, si_classifier, si_label_encoder, tamil_pipe)
-
-    logging.info("✅ All models loaded successfully.")
+    logging.info("✅ All models loaded.")
 
 @app.on_event("startup")
 def startup_event():
@@ -56,25 +50,22 @@ def predict(req: PredictRequest):
     en_vec, en_clf, en_le, si_vec, si_clf, si_le, tamil_pipe = models
 
     try:
-        if req.language.lower() == "english":
+        if req.language == "English":
             X = en_vec.transform([req.text])
             pred = en_clf.predict(X)[0]
             emotion = en_le.inverse_transform([pred])[0]
             return {"emotion": emotion, "language": "English"}
 
-        elif req.language.lower() == "sinhala":
+        elif req.language == "Sinhala":
             X = si_vec.transform([req.text])
             pred = si_clf.predict(X)[0]
             emotion = si_le.inverse_transform([pred])[0]
             return {"emotion": emotion, "language": "Sinhala"}
 
-        elif req.language.lower() == "tamil":
+        elif req.language == "Tamil":
             res = tamil_pipe(req.text)[0]
-            return {
-                "emotion": res["label"], 
-                "confidence": round(res["score"], 3), 
-                "language": "Tamil"
-            }
+            return {"emotion": res["label"], "confidence": round(res["score"], 3), "language": "Tamil"}
+
         else:
             return {"error": f"Unsupported language: {req.language}"}