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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()
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