from fastai.vision.all import *
import gradio as gr

# Load the Fastai model
learn = load_learner('resnet50_30_categories.pkl')

# Prediction function
def classify_image(img):
    pred, idx, probs = learn.predict(img)
    return {learn.dls.vocab[i]: float(probs[i]) for i in range(len(probs))}

# Gradio Interface
iface = gr.Interface(
    fn=classify_image,
    inputs=gr.Image(type="pil"),
    outputs=gr.Label(num_top_classes=3),
    title="Flower Classifier",
    description="Upload an image of a flower and the model will predict its category."
)

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