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

# Extraer zip
with zipfile.ZipFile("dataset.zip", 'r') as zip_ref:
    zip_ref.extractall(".")  # crea dataset/

# Asegurarse de que existe la carpeta correcta
dataset_path = Path("dataset")
if not dataset_path.exists():
    raise FileNotFoundError("La carpeta 'dataset' no existe después de descomprimir el zip. Revisa la estructura del zip.")

# Crear DataLoaders
dls = ImageDataLoaders.from_folder(
    dataset_path,
    valid_pct=0.2,
    seed=42,
    item_tfms=Resize(224)
)

# Cargar modelo
learn = vision_learner(dls, resnet34)
learn.load("model_lab")

labels = learn.dls.vocab

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()