Update app.py

app.py

@@ -0,0 +1,44 @@
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+
+IMG_SIZE = (224, 224)
+CLASS_NAMES = ['no_valido', 'valido']
+
+def preprocess_image(img):
+    img = tf.image.resize(img, IMG_SIZE)
+    img_array = tf.expand_dims(img, 0)
+    img_array = img_array / 255.0
+    return img_array
+
+def predict(rx_image, keras_model_file):
+    model_path = "temp_model.keras"
+    with open(model_path, "wb") as f:
+        f.write(keras_model_file.read())
+    
+    model = tf.keras.models.load_model(model_path)
+    img_array = preprocess_image(rx_image)
+    preds = model.predict(img_array)
+    score = tf.nn.softmax(preds[0])
+    
+    predicted_index = np.argmax(score)
+    confidence = np.max(score) * 100
+    predicted_class = CLASS_NAMES[predicted_index]
+    
+    other_index = 1 - predicted_index
+    other_class = CLASS_NAMES[other_index]
+    other_confidence = score[other_index] * 100
+    
+    return f"Resultado: {predicted_class.upper()} ({confidence:.2f}%)\n" + \
+           f"Probabilidad {other_class}: {other_confidence:.2f}%"
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Image(type="numpy", label="Sube tu RX"),
+        gr.File(file_types=[".keras"], label="Sube tu modelo Keras")
+    ],
+    outputs=gr.Textbox(label="Resultado")
+)
+
+iface.launch()