| import gradio as gr | |
| from transformers import pipeline | |
| #pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog") | |
| #pipeline = pipeline(task="image-classification", model="C:\hotdog-not-hotdog") | |
| pipeline = pipeline(task="image-classification", model="chennaiai/my-hotdog-not-hotdog") | |
| print("<<<<<< App is running >>>>>>") | |
| def predict(image): | |
| predictions = pipeline(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.Label(num_top_classes=2), | |
| title="Hot Dog? Or Not?", | |
| allow_flagging="manual" | |
| ).launch() |