import gradio as gr
from fastai.vision.all import load_learner
from PIL import Image
from fastcore.all import *
from fastbook import PILImage
# Load the pretrained FastAI model
learn = load_learner("model.pkl")

def classify_image(image):
    # Convert Gradio input to PIL Image
    image_pil = Image.fromarray(image.astype('uint8'), 'RGB')
    
    # Save the image temporarily
    image_pil.save("temp_image.jpg")
    
    # Perform inference
    classification,_,probs = learn.predict(PILImage.create('temp_image.jpg'))
    
    return f"This is a {classification}"

# Define Gradio interface
inputs = gr.Image()
label = gr.Textbox(label="Prediction")
interface = gr.Interface(fn=classify_image, inputs=inputs, outputs=label)

# Run the interface
interface.launch(share=True)