show image name

app.py

@@ -23,7 +23,7 @@ def show_fingername(fingernum):
     else: fingername += "thumb"
     return fingername
 
-def predict_image(img):
+def predict_image(img, image_file):
     # Ensure the image is a PIL Image
     if not isinstance(img, Image.Image):
         img = Image.fromarray(img)
@@ -44,15 +44,17 @@ def predict_image(img):
     y_SubjectID_pred = idpred.predict(img_array)
     y_fingerNum_pred = fingpred.predict(img_array)
 
+    image_name = image_file.name if image_file is not None else "No file name"
+
     # Extract prediction and confidence
-    subject_id = np.argmax(y_SubjectID_pred, axis=1)[0]
+    subject_id = np.argmax(y_SubjectID_pred, axis=1)[0] + 1
     finger_num = np.argmax(y_fingerNum_pred, axis=1)[0]
     subject_confidence = np.max(y_SubjectID_pred) * 100
     finger_confidence = np.max(y_fingerNum_pred) * 100
 
-    print(y_SubjectID_pred)
-    return (f'Subject ID: {subject_id} (Confidence: {subject_confidence:.2f}%)',
-            f'Finger Number: {show_fingername(finger_num)} (Confidence: {finger_confidence:.2f}%)')
+    output_message = f"Image Name: {image_name}\nPredicted Subject ID: {subject_id} (Confidence: {subject_confidence:.2f}%)\nPredicted Finger Type: {finger_num} (Confidence: {finger_confidence:.2f}%)"
+
+    return output_message
 
 # Create Gradio interface
 iface = gr.Interface(fn=predict_image, inputs="image", outputs=["text", "text"])