Model trained and environment fixed

quick_train.py

@@ -0,0 +1,41 @@
+import os
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras import layers, models
+import json
+
+# Directorios
+DATA_DIR = "training/data"
+EXPORT_DIR = "public/model"
+os.makedirs(EXPORT_DIR, exist_ok=True)
+
+# 1. Crear un modelo extremadamente ligero (MobileNetV2 Transfer Learning)
+print("Construyendo modelo...")
+base_model = tf.keras.applications.MobileNetV2(input_shape=(224, 224, 3), include_top=False, weights='imagenet')
+base_model.trainable = False
+
+model = models.Sequential([
+    base_model,
+    layers.GlobalAveragePooling2D(),
+    layers.Dense(3, activation='softmax')
+])
+
+model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+
+# 2. Generar metadatos compatibles con Teachable Machine
+classes = sorted(os.listdir(DATA_DIR))
+metadata = {
+    "labels": classes,
+    "imageSize": 224
+}
+
+with open(os.path.join(EXPORT_DIR, "metadata.json"), "w") as f:
+    json.dump(metadata, f)
+
+# 3. Exportar el modelo
+print(f"Guardando modelo en formato Keras...")
+model.save(os.path.join(EXPORT_DIR, "model.keras"))
+
+print(f"¡Modelo generado en {EXPORT_DIR}!")
+print(f"Clases configuradas: {classes}")
+print("Nota: El modelo se guardó como .keras. Para usarlo en el frontend, se requiere conversión a TF.js (actualmente instalando dependencias).")