import gradio as gr
from fastcore.all import *
from  fastai.vision.all import *
# import pathlib
# temp = pathlib.PosixPath
# pathlib.PosixPath = pathlib.WindowsPath


learn = load_learner('export.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))}

title = "my little Food Classifier"
description = "My first Food Classifier trained on some food I know. I made this to learn making models in fastAI and deploying them. "
examples = ['barfi.webp',"brocoli.jfif","dal.jpg","gulabjamun.jfif","jalebi.jpg","ladoo.webp","pakora.webp","samosa.webp"]
interpretation='default'
enable_queue=True

gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=5),title=title,description=description,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch()