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

# Rutas relativas dentro del Space
path = Path("dataset")  # la carpeta que subiste

# Crear los DataLoaders desde las carpetas
dls = ImageDataLoaders.from_folder(
    path,
    valid_pct=0.2,
    seed=42,
    item_tfms=Resize(224)
)

# Cargar tu modelo .pth
learn = vision_learner(dls, resnet34)
learn.load("model_lab")  # si lo guardaste en models/, usar "models/model_lab"

labels = learn.dls.vocab  # toma las clases automáticamente

def predict(img):
    img = PILImage.create(img)
    _, _, probs = learn.predict(img)
    return {labels[i]: float(probs[i]) for i in range(len(labels))}

demo = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="filepath"),
    outputs=gr.Label(num_top_classes=3),
    title="Lab Utensils Classifier"
)

demo.launch()