Upload app.py

app.py

@@ -120,7 +120,12 @@ def predict(image):
     Make prediction on input image.
     """
     if model is None:
-        return {"error": "Model not loaded"}
+        # Return zero confidence for all classes when model not loaded
+        return {class_name: 0.0 for class_name in class_names}
+
+    if image is None:
+        # Return zero confidence for all classes when no image provided
+        return {class_name: 0.0 for class_name in class_names}
 
     try:
         # Preprocess image
@@ -132,19 +137,22 @@ def predict(image):
         # Get probabilities for each class
         probabilities = predictions[0]
 
-        # Create result dictionary
-        results = {class_names[i]: float(probabilities[i])
-                   for i in range(len(class_names))}
-
-        # Get predicted class
-        predicted_class_idx = np.argmax(probabilities)
-        predicted_class = class_names[predicted_class_idx]
-        confidence = float(probabilities[predicted_class_idx])
+        # Create result dictionary with validated float values
+        results = {}
+        for i in range(len(class_names)):
+            prob = probabilities[i]
+            # Ensure the probability is a valid number
+            if prob is None or (isinstance(prob, float) and (np.isnan(prob) or np.isinf(prob))):
+                results[class_names[i]] = 0.0
+            else:
+                results[class_names[i]] = float(prob)
 
         return results
 
     except Exception as e:
-        return {"error": str(e)}
+        print(f"Prediction error: {e}")
+        # Return zero confidence for all classes on error
+        return {class_name: 0.0 for class_name in class_names}
 
 
 # -----------------------------