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import gradio as gr
import numpy as np
from tensorflow.keras.models import load_model
from huggingface_hub import hf_hub_download
import pickle
from PIL import Image
# Baixar os arquivos
repo_id = "davebraga/wrdbTI6"
model_path = hf_hub_download(repo_id, "trained_model.keras")
category_encoder_path = hf_hub_download(repo_id, "category_encoder.pkl")
color_encoder_path = hf_hub_download(repo_id, "color_encoder.pkl")
# Carregar modelo e encoders
model = load_model(model_path)
with open(category_encoder_path, "rb") as f:
category_encoder = pickle.load(f)
with open(color_encoder_path, "rb") as f:
color_encoder = pickle.load(f)
# Previsão
def predict(image):
image = image.resize((160, 160))
image_array = np.array(image) / 255.0
image_array = np.expand_dims(image_array, axis=0)
category_pred, color_pred = model.predict(image_array)
category = category_encoder.inverse_transform([np.argmax(category_pred)])[0]
color = color_encoder.inverse_transform([np.argmax(color_pred)])[0]
return f"Categoria: {category}", f"Cor: {color}"
# Interface
iface = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil"),
outputs=["text", "text"],
title="Classificador de Categoria e Cor",
description="Faça upload de uma imagem de uma peça de roupa para prever a categoria e a cor."
)
iface.launch()
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