Subido modelo corregido

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

app.py

@@ -0,0 +1,35 @@
+import gradio as gr
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+
+# Cargamos el modelo guardado
+model = tf.keras.models.load_model("model.keras")
+
+# Tus clases
+labels = ['pituitary', 'meningioma', 'notumor', 'glioma']
+
+# Preprocesamiento (ajustado al modelo Deit-Tiny 224x224)
+IMG_SIZE = (224, 224)
+
+def predict(image):
+    # Convertimos a array
+    img = image.resize(IMG_SIZE)
+    img = np.array(img) / 255.0
+    img = np.expand_dims(img, axis=0)
+
+    # Predicción
+    preds = model.predict(img)[0]
+    probs = {labels[i]: float(preds[i]) for i in range(len(labels))}
+
+    return probs
+
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Sube una imagen"),
+    outputs=gr.Label(num_top_classes=4, label="Predicción"),
+    title="Clasificador de Tumores Cerebrales",
+    description="Clasificador entrenado en 4 categorías: pituitary, meningioma, notumor, glioma"
+)
+
+interface.launch()

model.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:194e1eed276e0316e6351b8e7bcee2ae66670a4fc7b795093b4f911fe6113a61
+size 4926205