app.py

import gradio as gr
from transformers import pipeline

# Load a pre-trained image classification model from Hugging Face
model = pipeline("image-classification", model="google/vit-base-patch16-224")

# Define the prediction function
def classify_image(image):
    predictions = model(image)
    return {pred["label"]: pred["score"] for pred in predictions}

# Gradio Interface
demo = gr.Interface(
    fn=classify_image,
    inputs=gr.Image(type="pil"),
    outputs=gr.Label(num_top_classes=5),
    title="Image Recognition AI",
    description="Upload an image to classify it using a pre-trained model from Hugging Face."
)

# Launch the app
demo.launch()