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



# repo_id = "YOUR_USERNAME/YOUR_LEARNER_NAME"
repo_id = "mipedro1/clasificador-vehiculos"

learner = from_pretrained_fastai(repo_id)
labels = learner.dls.vocab

# Definimos una función que se encarga de llevar a cabo las predicciones
def predict(img):
    if isinstance(img, dict):  # Gradio newer format
        img = img["image"]
    img = PILImage.create(img)
    pred,pred_idx,probs = learner.predict(img)
    return {labels[i]: float(probs[i]) for i in range(len(labels))}
    
# Creamos la interfaz y la lanzamos. 
gr.Interface(fn=predict, inputs=gr.Image(type="pil"), outputs=gr.Label(num_top_classes=3),examples=['Bike (10).jpg','Car (105).jpg','Auto Rickshaw (10).jpg','Motorcycle (101).jpg','Plane (101).jpg','Ship (10) (1).jpg','Train (102).png']).launch(share=False)