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.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
+final_model.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,24 @@
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from tensorflow.keras.models import load_model
+from PIL import Image
+
+# Cargar el modelo
+model = load_model("final_model.keras")
+
+# Función para predecir una imagen
+def predict_image(image):
+    image = image.resize((32, 32))  # Redimensionar imagen a tamaño adecuado
+    image = np.array(image) / 255.0  # Normalizar la imagen
+    image = np.expand_dims(image, axis=0)  # Añadir dimensión para lote
+    prediction = model.predict(image)  # Predicción
+    class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']  # Clases de CIFAR-10
+    predicted_class = class_names[np.argmax(prediction)]
+    return predicted_class
+
+# Crear interfaz de Gradio
+iface = gr.Interface(fn=predict_image, inputs="image", outputs="text", live=True)
+
+# Ejecutar la interfaz
+iface.launch()

final_model.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5f15a754c63d62ded65a039b70e11902e2f7dff3966f41dd19ea90735369abe9
+size 26061441

requirements.txt

@@ -0,0 +1,3 @@
+tensorflow
+gradio
+huggingface_hub