fix: reshape

app.py

@@ -1,6 +1,6 @@
 import gradio as gr
 from huggingface_hub import from_pretrained_keras
-from tensorflow.keras.preprocessing import image
+from PIL import Image
 import numpy as np
 
 # Load models
@@ -8,19 +8,22 @@ idpred = from_pretrained_keras("aegishield/idpred")
 fingpred = from_pretrained_keras("aegishield/fingpred")
 
 def predict_image(img):
-    # Preprocess the image (example, adjust based on your model's needs)
-    img = img.resize((224, 224))  # Adjust the size according to your model input
-    img_array = image.img_to_array(img)
+    # Preprocess the image (resize to 96x96 as per your model's requirement)
+    img = img.resize((96, 96))
+    img_array = np.array(img)
+
+    # Ensure the image array is in the correct shape expected by your models
     img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
 
     # Predictions
     y_SubjectID_pred = idpred.predict(img_array)
     y_fingerNum_pred = fingpred.predict(img_array)
 
-    # Process predictions to readable format if necessary
-    # For example, if your predictions are one-hot encoded, convert them to labels
+    # Format the output
+    subject_id = np.argmax(y_SubjectID_pred, axis=1)[0]  # Adjust if necessary
+    finger_num = np.argmax(y_fingerNum_pred, axis=1)[0]  # Adjust if necessary
 
-    return f'Subject ID: {y_SubjectID_pred[0]}, Finger Number: {y_fingerNum_pred[0]}'
+    return f'Subject ID: {subject_id}, Finger Number: {finger_num}'
 
 # Create Gradio interface
 iface = gr.Interface(fn=predict_image, inputs="image", outputs="text")