from fastai.vision.all import *
from fastai.learner import load_learner
from huggingface_hub import from_pretrained_fastai, hf_hub_download
import gradio as gr
import skimage

learn = load_learner('PetNet50.pkl')
# learn = from_pretrained_fastai("kurianbenoy/course_v5_lesson2_pets_convnext_base_in22k")
#learn = load_learner(
#    hf_hub_download("kurianbenoy/course_v5_lesson2_pets_convnext_base_in22k", "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))}

title = "Pet Breed Classifier"
description = """
🐶🐱
🇬🇧 = A pet breed classifier (Dogs and Cats) trained on the Oxford Pets dataset using fastai. Created as a demo from the course by Jeremy Howard.
For best results use photos of your pets.
🇪🇸 = Un clasificador de razas de mascotas (perros y gatos) entrenado en el dataset Oxford Pets.
Usa fotos de tus mascotas para obtener la raza.

👨‍👨‍👧‍👦 CZDJ ❤️
"""

article="<p style='text-align: center'><a href='https://course.fast.ai/' target='_blank'>Go to course!</a></p>"
examples = ['siamese.jpg','pug.jpg']

gr.Interface(fn=predict,
             inputs=gr.Image(),
             outputs=gr.Label(num_top_classes=3),
             title=title,
             description=description,
             article=article,
             examples=examples).launch()