app.py

import gradio as gr
from transformers import pipeline

# Load the image classification model from Hugging Face
classifier = pipeline("image-classification", model="microsoft/resnet-50")

def classify_image(image):
    # Perform image classification
    results = classifier(image)
    
    # Format the results
    return {result["label"]: f"{result['score']:.4f}" for result in results}

# Create the Gradio interface
demo = gr.Interface(
    fn=classify_image,
    inputs=gr.Image(type="pil"),
    outputs=gr.Label(num_top_classes=5),
    title="Image Classification with ResNet-50",
    description="Upload an image to classify it into one of 1000 ImageNet categories."
)

# Launch the app
if __name__ == "__main__":
    demo.launch()