Update app.py

app.py

@@ -10,11 +10,19 @@ onnx_model = ort.InferenceSession("onnx_model.onnx")
 # Preprocess image function
 def preprocess_image(image):
     """Preprocess image to match model input requirements"""
-    image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2GRAY)  # Convert to grayscale
-    image_resized = cv2.resize(image, (48, 48))  # Resize to 48x48
-    image_input = np.expand_dims(image_resized, axis=0)  # Add batch dimension
-    image_input = np.expand_dims(image_input, axis=3)  # Add channel dimension
-    image_input = image_input.astype(np.float32) / 255.0  # Normalize
+    # Convert the image to grayscale
+    image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2GRAY)
+    
+    # Resize image to 48x48 (model's expected input size)
+    image_resized = cv2.resize(image, (48, 48))
+    
+    # Add batch dimension and channels (for grayscale: 1 channel)
+    image_input = np.expand_dims(image_resized, axis=0)  # Add batch dimension (1, 48, 48)
+    image_input = np.expand_dims(image_input, axis=1)  # Add channel dimension (1, 1, 48, 48)
+    
+    # Normalize the image
+    image_input = image_input.astype(np.float32) / 255.0
+    
     return image_input
 
 # Predict emotion using the ONNX model