Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load the image classification pipeline from Hugging Face Transformers
+pipe = pipeline("image-classification", model="heisenberg3376/vit-base-food-items-v1")
+
+# Define the Gradio interface function
+def classify_image(input_image):
+    # Perform classification on the input image
+    results = pipe(input_image)
+    
+    # Prepare the output string with all predictions
+    output_str = "Predictions:\n"
+    for result in results:
+        output_str += f"{result['label']}: {result['score']:.4f}\n"
+    
+    # Return the concatenated string of predictions
+    return output_str
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.inputs.Image(type="pil", label="Upload an image"),
+    outputs="text",
+    title="Image Classification",
+    description="Classify food items in images using heisenberg3376/vit-base-food-items-v1"
+)
+
+# Launch the Gradio interface
+iface.launch()