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

examples = ["apple.jpg", "avocado.jpg", 
        "mixed_fruit.jpg", 
        "nectarine.jpg", "passion_fruit.jpg",
        "lemon.jpg"]
title = "Fruit prediction"
description = "A fruit prediction app trained using a pretrained-Resnet50 model via the fastai library. The model is trained using 5000 images collected from duckduckgo."

learn = load_learner("model.pkl")
labels = learn.dls.vocab
def predict(img):
    img = PILImage.create(img)
    pred,pred_idx,probs = learn.predict(img)
    return {labels[i]: float(probs[i]) for i in range(len(labels))}

gr_interface = gr.Interface(fn=predict, 
                    inputs = gr.inputs.Image(shape = (512, 512)), 
                    outputs = gr.outputs.Label(num_top_classes = 3),
                    title = title,description=description,
                    examples = examples
                    )
    
gr_interface.launch(share=True)