Update app.py

app.py

@@ -20,9 +20,9 @@ model.load_state_dict(torch.load("retinanet_best_model.pth", map_location=device
 model.eval()
 
 # Prediction function
-def predict_image(image, isFrame):
+def predict_image(image, is_frame):
 
-    if isFrame == False:
+    if is_frame == "No":
         # Preprocess the image
         img = Image.fromarray(image).convert('RGB')  # Convert Gradio input to PIL Image
         input_tensor = image_transform(img).unsqueeze(0).to(device)
@@ -76,8 +76,13 @@ with gr.Blocks() as demo:
     with gr.Row():
         image_input = gr.Image(label="Upload Image", type="numpy")
         output_text = gr.Textbox(label="Prediction Result")
+    is_frame_radio = gr.Radio(
+        choices=["Yes", "No"],  # Options for the radio button
+        label="Is this a frame from a video?",  # Label for the radio button
+        value="Not a Frame"  # Default selected option
+    )
     predict_button = gr.Button("Predict")
-    predict_button.click(predict_image, inputs=image_input, outputs=output_text)
+    predict_button.click(predict_image, inputs=[image_input, is_frame_radio], outputs=output_text)
 
 # Launch the app
 demo.launch()