fix

app.py

@@ -8,22 +8,31 @@ idpred = from_pretrained_keras("aegishield/idpred")
 fingpred = from_pretrained_keras("aegishield/fingpred")
 
 def predict_image(img):
+    # Ensure the image is a PIL Image
+    if not isinstance(img, Image.Image):
+        img = Image.fromarray(img)
+
     # Resize the image and convert to grayscale
     img_resized = img.resize((96, 96)).convert('L')
+
+    # Convert the resized grayscale image to a numpy array
     img_array = np.array(img_resized)
+
+    # Add a channel dimension since the model expects (96, 96, 1)
     img_array = np.expand_dims(img_array, axis=-1)
-    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
 
-    # Predictions and confidence
+    # Add batch dimension
+    img_array = np.expand_dims(img_array, axis=0)
+
+    # Predictions
     y_SubjectID_pred = idpred.predict(img_array)
     y_fingerNum_pred = fingpred.predict(img_array)
 
+    # Extract prediction and confidence
     subject_id = np.argmax(y_SubjectID_pred, axis=1)[0]
     finger_num = np.argmax(y_fingerNum_pred, axis=1)[0]
-
-    # Get the maximum probability as confidence
-    subject_confidence = np.max(y_SubjectID_pred) * 100  # Convert to percentage
-    finger_confidence = np.max(y_fingerNum_pred) * 100  # Convert to percentage
+    subject_confidence = np.max(y_SubjectID_pred) * 100
+    finger_confidence = np.max(y_fingerNum_pred) * 100
 
     return (f'Subject ID: {subject_id} (Confidence: {subject_confidence:.2f}%)',
             f'Finger Number: {finger_num} (Confidence: {finger_confidence:.2f}%)')