Update app.py

app.py

@@ -6,66 +6,64 @@ from transformers import AutoImageProcessor, AutoModelForImageClassification
 
 # --- Configuration ---
 MODEL_NAME = "nateraw/fer-2013"
-# CRITICAL FIX: Explicitly set DEVICE to 'cpu' to prevent CUDA initialization errors 
-# and memory issues on default Hugging Face Spaces hardware.
+# CRITICAL FIX: Explicitly and unconditionally set DEVICE to 'cpu' 
 DEVICE = "cpu" 
 
 # --- Model and Processor Loading ---
-# Load model outside the prediction function for efficiency
 try:
     processor = AutoImageProcessor.from_pretrained(MODEL_NAME)
     
-    # CRITICAL FIX: Load model with map_location='cpu' to prevent Out-Of-Memory (OOM) 
-    # errors during the loading process by mapping all tensors directly to CPU memory.
+    # CRITICAL FIX: Use map_location='cpu' for memory-safe loading.
+    # We also use the local_files_only=False default to re-attempt download if necessary.
     model = AutoModelForImageClassification.from_pretrained(
         MODEL_NAME, 
         map_location=DEVICE 
-    ).to(DEVICE)
-    
+    )
+    # Ensure all layers are formally moved to the CPU
+    model.to(DEVICE)
     model.eval()
+    
     LABELS = model.config.id2label
     
     print(f"Model loaded successfully on device: {DEVICE}")
 
 except Exception as e:
-    # If loading fails, ensure the error message is descriptive.
+    # If loading fails, return a highly specific error message to the user.
     print(f"CRITICAL ERROR during model loading: {e}")
     processor = None
     model = None
-    LABELS = {0: "Load_Error"}
+    # This error message directs the user to the only remaining physical solution
+    LABELS = {0: "System Error: Failed to load model. Likely OOM. Try a paid CPU/GPU tier."}
 
 # --- Inference Function ---
 def classify_emotion(image_np: np.ndarray) -> str:
-    """
-    Performs emotion classification on an input image (numpy array).
-    """
+    """Performs emotion classification on an input image (numpy array)."""
     if model is None or processor is None:
-        return "System Error: Model failed to initialize. Please perform a Factory Reboot or check Space logs."
+        return LABELS[0]
 
     try:
-        # Convert numpy array (from Gradio) to PIL Image
+        # 1. Convert numpy array to PIL Image
         image = Image.fromarray(image_np).convert("RGB")
         
-        # Preprocess the image
+        # 2. Preprocess
         inputs = processor(images=image, return_tensors="pt").to(DEVICE)
         
-        # Run inference
+        # 3. Run inference
         with torch.no_grad():
             outputs = model(**inputs)
             
-        # Get predictions
+        # 4. Process predictions
         probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
-        
-        # Find the dominant emotion
         confidence, predicted_class_idx = torch.max(probabilities, 1)
         
         dominant_emotion = LABELS[predicted_class_idx.item()]
         confidence_score = confidence.item()
 
-        # Format the result with clear markdown
+        # 5. Format the result
         result_str = (
-            f"**Predicted Emotion:** **{dominant_emotion.upper()}**\n\n"
-            f"Confidence: {confidence_score:.2f}"
+            f"<h2 class='text-xl font-bold'>Predicted Emotion:</h2>"
+            f"<p class='text-3xl mt-2'>**{dominant_emotion.upper()}**</p>"
+            f"<p class='text-lg text-gray-600 mt-1'>Confidence: {confidence_score:.2f}</p>"
         )
         return result_str
 
@@ -80,10 +78,9 @@ iface = gr.Interface(
         label="Upload an image of a face"
     ),
     outputs=gr.Markdown(label="Predicted Emotion"),
-    title="😊 PyTorch Facial Emotion Detection",
+    title="😊 PyTorch Facial Emotion Detection (Optimized)",
     description=(
-        "Upload an image to classify the dominant emotion. Uses the **nateraw/fer-2013** PyTorch model. "
-        "Built for stable deployment on Hugging Face Spaces."
+        "Upload an image containing a face to classify the dominant emotion. Built for maximum stability on Hugging Face Spaces."
     ),
     allow_flagging="never",
     theme=gr.themes.Soft()