Update app.py

app.py

@@ -1,3 +1,4 @@
+'''
 import gradio as gr
 from transformers import pipeline, GPT2Tokenizer, AutoModelForSequenceClassification, AutoTokenizer
 from IPython.display import clear_output
@@ -240,4 +241,24 @@ with gr.Blocks(theme = gr.themes.Monochrome()) as iface:
       bn_test_d_1.click(fn = d_test_1, inputs = bn_test_d_1_text_input, outputs = bn_test_d_1_text_output)
 
 clear_output()
-iface.launch(share = True, debug = False)
+iface.launch(share = True, debug = False)
+'''
+
+import gradio as gr
+from transformers import pipeline
+
+pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
+
+def predict(input_img):
+    predictions = pipeline(input_img)
+    return input_img, {p["label"]: p["score"] for p in predictions} 
+
+gradio_app = gr.Interface(
+    predict,
+    inputs=gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"),
+    outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)],
+    title="Hot Dog? Or Not?",
+)
+
+if __name__ == "__main__":
+    gradio_app.launch()