use

app.py

@@ -1,11 +1,29 @@
 import gradio as gr
 from huggingface_hub import from_pretrained_keras
+from tensorflow.keras.preprocessing import image
+import numpy as np
 
-def greet(name):
-    return "Hello " + name + "!!"
-
+# Load models
 idpred = from_pretrained_keras("aegishield/idpred")
 fingpred = from_pretrained_keras("aegishield/fingpred")
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+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)
+    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
+
+    return f'Subject ID: {y_SubjectID_pred[0]}, Finger Number: {y_fingerNum_pred[0]}'
+
+# Create Gradio interface
+iface = gr.Interface(fn=predict_image, inputs="image", outputs="text")
+
+# Launch interface
 iface.launch()