Adicioanndo arquivos

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+trained_model.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,42 @@
+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()

category_encoder.pkl

@@ -0,0 +1,3 @@
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+size 1280

color_encoder.pkl

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+oid sha256:b4432d811f63ada3387ab0891bc83982524c5b82cd69ea9b512fc3ce1fac3fd7
+size 639

saved_model.zip

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+oid sha256:c6a4ee34242f8067858cc99a8a49cd72faf412387bcc5359ab220985ddd40d11
+size 31526274

trained_model.keras

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+size 51228499