Upload app.py with huggingface_hub

app.py

@@ -113,17 +113,19 @@ async def load_model():
     """Load model and vectorizers on startup"""
     global model, tfidf_word, tfidf_char
     
-    print("Loading model from Hugging Face...")
-    
-    # Download model
-    model_path = hf_hub_download(
-        repo_id="aqibtahir/cookie-classifier-lr-tfidf",
-        filename="LR_TFIDF+NAME.joblib"
-    )
-    model = joblib.load(model_path)
-    
-    # Try to load vectorizers (they should be in the same directory or uploaded separately)
     try:
+        print("🔄 Loading model from Hugging Face...")
+        
+        # Download model
+        model_path = hf_hub_download(
+            repo_id="aqibtahir/cookie-classifier-lr-tfidf",
+            filename="LR_TFIDF+NAME.joblib"
+        )
+        model = joblib.load(model_path)
+        print("✓ Model loaded")
+        
+        # Load vectorizers
+        print("🔄 Loading vectorizers...")
         tfidf_word_path = hf_hub_download(
             repo_id="aqibtahir/cookie-classifier-lr-tfidf",
             filename="tfidf_word.joblib"
@@ -134,10 +136,14 @@ async def load_model():
         )
         tfidf_word = joblib.load(tfidf_word_path)
         tfidf_char = joblib.load(tfidf_char_path)
-        print("✓ Model and vectorizers loaded successfully!")
+        print("✓ Vectorizers loaded")
+        print("🎉 API ready to serve predictions!")
+        
     except Exception as e:
-        print(f"⚠️ Warning: Could not load vectorizers: {e}")
-        print("API will work with limited functionality")
+        print(f"❌ Error during startup: {e}")
+        import traceback
+        traceback.print_exc()
+        raise
 
 # Request/Response models
 class CookieRequest(BaseModel):