Atualizando app.py

app.py

@@ -1,42 +1,53 @@
-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()
+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
+import zipfile
+import os
+
+# Repositório no Hugging Face
+repo_id = "davebraga/wrdbTI6"
+
+# Baixar o modelo compactado
+model_zip_path = hf_hub_download(repo_id, "saved_model.zip")
+
+# Descompactar o modelo se ainda não existir
+if not os.path.exists("saved_model"):
+    with zipfile.ZipFile(model_zip_path, 'r') as zip_ref:
+        zip_ref.extractall("saved_model")
+
+# Baixar os encoders
+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("saved_model")
+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)
+
+# Função de 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 Gradio
+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()