Upload app.py

app.py

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+import gradio as gr
+from PIL import Image
+import numpy as np
+import tensorflow as tf
+import os
+
+# Configurar variables de entorno para reducir advertencias
+os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+
+# Configuración inicial
+cifar10_labels = [
+    'avión', 'automóvil', 'pájaro', 'gato', 'venado',
+    'perro', 'rana', 'caballo', 'barco', 'camión'
+]
+
+model = tf.keras.models.load_model('my_model.keras')
+
+def preprocess_image(image):
+    """Preprocesado de imagen para el modelo"""
+    img = image.resize((32, 32)).convert('RGB')
+    return np.array(img).astype('float32') / 255
+
+def predict(image):
+    """Realizar predicción y formatear resultados"""
+    if image is None:
+        raise gr.Error("¡Por favor sube una imagen o toma una foto!")
+    
+    processed_img = preprocess_image(image)
+    preds = model.predict(np.expand_dims(processed_img, axis=0))[0]
+    return {label: float(preds[i]) for i, label in enumerate(cifar10_labels)}
+
+# Configurar ejemplos
+examples = [
+    ["ejemplos/avion.jpg"],
+    ["ejemplos/automovil.jpg"],
+    ["ejemplos/pajaro.jpg"],
+    ["ejemplos/gato.jpg"],
+    ["ejemplos/venado.jpg"],
+
+]
+
+# Construir interfaz
+with gr.Blocks(theme=gr.themes.Soft(), css="""
+    .examples-grid {display: flex !important; flex-direction: column; gap: 1rem}
+    .examples-row {display: flex !important; gap: 1rem; justify-content: center}
+    """) as app:
+    
+    gr.Markdown("# 📷 Clasificador CIFAR-10 by Aryy :3")
+    
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(
+                sources=["upload", "webcam", "clipboard"],
+                type="pil",
+                label="Entrada de imagen",
+                height=250
+            )
+            with gr.Row():
+                submit_btn = gr.Button("Predecir", variant="primary")
+                clear_btn = gr.Button("Limpiar")
+        
+        with gr.Column():
+            output_label = gr.Label(
+                label="Resultados",
+                num_top_classes=3,
+                show_label=True
+            )
+    
+    # Sección de ejemplos con interacción
+    gr.Markdown("## Ejemplos de categorías")
+    with gr.Column(elem_classes=["examples-grid"]):
+        # Primera fila
+        with gr.Row(elem_classes=["examples-row"]):
+            for example, label in zip(examples[:5], cifar10_labels[:5]):
+                gr.Examples(
+                    examples=example,
+                    inputs=[input_image],
+                    label=label.capitalize(),
+                    examples_per_page=1,
+                    fn=predict,
+                    outputs=[output_label],
+                )
+        # Segunda fila
+        with gr.Row(elem_classes=["examples-row"]):
+            for example, label in zip(examples[5:], cifar10_labels[5:]):
+                gr.Examples(
+                    examples=example,
+                    inputs=[input_image],
+                    label=label.capitalize(),
+                    examples_per_page=1,
+                    fn=predict,
+                    outputs=[output_label],
+                )
+
+    # Conectar eventos
+    submit_btn.click(
+        fn=predict,
+        inputs=input_image,
+        outputs=output_label,
+        api_name="predict"
+    )
+    
+    clear_btn.click(
+        fn=lambda: [None, None],
+        inputs=None,
+        outputs=[input_image, output_label]
+    )
+
+if __name__ == "__main__":
+    app.launch()