Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+from transformers import pipeline
+
+def convert_to_rgb(image):
+    return image.convert('RGB')
+
+
+classifier = pipeline("image-classification", model="google/vit-base-patch16-224")
+
+
+def classify_image(image):
+    image = convert_to_rgb(image)
+    class_scores = classifier(image)
+    highest_probability_class = max(class_scores, key=lambda x: x["score"])
+    highest_probability_class = highest_probability_class["label"]
+    return highest_probability_class, class_scores
+
+
+iface = gr.Interface(fn=classify_image, 
+                     inputs=gr.Image(type="pil"), 
+                     outputs=["text", "json"],
+                     live=True, 
+                     title="Food Image Classification",
+                     description="Classify food items in uploaded images using a pre-trained model.")
+
+iface.launch()