Create app.py

app.py

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+# app.py
+import gradio as gr
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+
+# Cargar el modelo .h5
+model = tf.keras.models.load_model('sports.h5')
+
+# Definir las clases
+classes = ['americano', 'ciclismo', 'golf', 'futbol', 'tenis', 'basket', 'natacion', 'boxeo', 'beisball', 'f1']
+
+# Función de predicción
+def predict(image):
+    image = Image.fromarray(image).resize((224, 224))
+    image = np.array(image) / 255.0
+    image = np.expand_dims(image, axis=0)
+    predictions = model.predict(image)
+    predicted_class = classes[np.argmax(predictions)]
+    return predicted_class
+
+# Interfaz de Gradio
+interface = gr.Interface(fn=predict, inputs=gr.inputs.Image(type="numpy"), outputs="text")
+
+# Ejecutar la interfaz
+if __name__ == "__main__":
+    interface.launch()