Delete app.py

app.py

@@ -1,37 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-
-model = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
-def predict(image):
-    # Define your prediction function here
-    # For example:
-    images = [image.crop((0, 0, 100, 100)), image.crop((100, 100, 200, 200))]
-    predictions = ['cat', 'dog']
-    return images, predictions
-
-# Create Gradio interface
-iface = gr.Interface(
-    fn=predict,
-    inputs=gr.inputs.Image(type='pil'),
-    outputs=[gr.outputs.Image(type='pil', label='Images'), gr.outputs.Label(label='Predictions')]
-)
-
-# Launch Gradio app
-iface.launch()
-
-# def predict(image):
-#     predictions = model(image)
-#     return {p["label"]: p["score"] for p in predictions}
-
-# gr.Interface(
-#     predict,
-#     inputs=gr.inputs.Image(label="Upload hot dog candidate", type="filepath"),
-#     outputs=[gr.outputs.Image(type='pil', label='Image'), gr.outputs.Label(num_top_classes=2)], # gr.outputs.Label(num_top_classes=2),
-#     title="Hot Dog? Or Not?",
-# ).launch()
-
-# def greet(name):
-#     return "Hello " + name + "!!"
-
-# iface = gr.Interface(fn=greet, inputs="text", outputs="text"+" is silly! :P")
-# iface.launch()