Update app.py

app.py

@@ -8,27 +8,44 @@ 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...")
 
-    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"))
+    try:
+        # ==================== NEW ENGLISH LR MODEL ====================
+        repo_id = "E-motionAssistant/English_LR_Model_New"
+
+        en_vectorizer = joblib.load(hf_hub_download(repo_id, "tfidf_vectorizer.joblib"))
+        en_classifier = joblib.load(hf_hub_download(repo_id, "logreg_model.joblib"))
+        en_label_encoder = joblib.load(hf_hub_download(repo_id, "label_encoder.joblib"))
 
-    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"))
+        # ==================== 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_pipe = pipeline("text-classification", model="E-motionAssistant/Tamil_Emotion_Recognition_Model", device=-1)
+        # ==================== 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.")
+        models = (en_vectorizer, en_classifier, en_label_encoder,
+                  si_vectorizer, si_classifier, si_label_encoder, tamil_pipe)
+
+        logging.info("✅ All models loaded successfully.")
+
+    except Exception as e:
+        logging.error(f"❌ Model loading failed: {type(e).__name__} - {e}")
+        raise   # Important: This will show the real error in HF Space logs
 
 @app.on_event("startup")
 def startup_event():
@@ -36,7 +53,7 @@ def startup_event():
 
 class PredictRequest(BaseModel):
     text: str
-    language: str  # "English", "Sinhala", or "Tamil"
+    language: str   # "English", "Sinhala", or "Tamil"
 
 @app.get("/")
 def root():
@@ -44,31 +61,34 @@ def root():
 
 @app.post("/predict")
 def predict(req: PredictRequest):
-    if not req.text.strip():
+    if not req.text or not req.text.strip():
         return {"error": "Text cannot be empty"}
 
     en_vec, en_clf, en_le, si_vec, si_clf, si_le, tamil_pipe = models
 
     try:
-        if req.language == "English":
+        if req.language.lower() == "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 == "Sinhala":
+        elif req.language.lower() == "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 == "Tamil":
+        elif req.language.lower() == "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}"}
 
     except Exception as e:
         logging.error(f"Prediction error: {e}")
-        return {"error": str(e)}
+        return {"error": "Prediction failed. Please try again."}