app.py

import gradio as gr
from transformers import pipeline

# Load the image classification model
pipe = pipeline("image-classification", model="SriramSridhar78/sriram-car-classifier")

# Define the prediction function
def predict(input_img):
    predictions = pipe(input_img)
    return input_img, {p["label"]: p["score"] for p in predictions}

# Create Gradio UI
gradio_app = gr.Interface(
    fn=predict,
    inputs=gr.Image(label="Upload Car Image", sources=['upload', 'webcam'], type="pil"),
    outputs=[
        gr.Image(label="Processed Image"),
        gr.Label(label="Car Model Type", num_top_classes=3)
    ],
    title="Car Classifier",
    description="Upload an image of a car and get the predicted class"
)

# Launch the Gradio app
gradio_app.launch()