Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load the car 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 the Gradio interface
+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"
+)
+
+if __name__ == "__main__":
+    gradio_app.launch()