Upload app.py

app.py

@@ -4,13 +4,13 @@ import cv2
 
 learn = load_learner('resnet18_model.pkl')
 
-categories = ('Black','White')
-
+categories = ('Black', 'White')
 
 
 def classify_image(image):
-    is_people,_,probs = learn.predict(PILImage.create(image))
-    return dict(zip(categories,map(float,probs)))
+    is_people, _, probs = learn.predict(PILImage.create(image))
+    return dict(zip(categories, map(float, probs)))
+
 
 def capture_image(cam):
     cam = cv2.VideoCapture(0)
@@ -27,17 +27,14 @@ def webcam():
         frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
         yield frame
 
+
 image = gr.inputs.Image(shape=(192, 192), label="Upload Image", source="upload")
 webcam = gr.inputs.Image(shape=(192, 192), label="Webcam Image", source="webcam")
 label = gr.outputs.Label()
 
-examples=['example1.jpg','example2.jpg','example3.jpg']
+examples = [['example1.jpg'], ['example2.jpg'], ['example3.jpg']]  # Nested list for multiple input components
 
 intf = gr.Interface(fn=classify_image, inputs=[image, webcam], outputs=label, examples=examples,
-                    allow_flagging=False, capture_session=True)
-
-intf.launch(inline=False)
-
-
-
+                    allow_flagging='never')  # Set allow_flagging parameter as a string
 
+intf.launch()