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import gradio as gr
from fastai.vision.all import *
from fastai.vision.all import PILImage
# Load the trained model
learn = load_learner('levit.pkl')
# Get the labels from the data loaders
labels = learn.dls.vocab
# Define the prediction function
def predict(img):
img = PILImage.create(img)
img = img.resize((512, 512))
pred, pred_idx, probs = learn.predict(img)
return {labels[i]: float(probs[i]) for i in range(len(labels))}
# Example images for demonstration
examples = ['image.jpg']
# Create the Gradio interface
interface = gr.Interface(
fn=predict,
inputs=gr.Image(),
outputs=gr.Label(num_top_classes=3)
)
# Enable the queue to handle POST requests
interface.queue(api_open=True)
# Launch the interface
interface.launch() |