Upload 4 files

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

app.py

@@ -0,0 +1,34 @@
+"""
+Streamlit app – generates 1-10 MNIST-style digits using your trained cGAN
+Run: streamlit run app.py
+"""
+
+import streamlit as st
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+LATENT_DIM   = 100
+NUM_CLASSES  = 10
+MODEL_FILE   = "generator_full.keras"   # <— same name you downloaded
+
+# ---------- 1. Load generator only once per worker ----------
+@st.cache_resource(show_spinner="Cargando modelo…")
+def load_generator(model_path=MODEL_FILE):
+    # load_model includes architecture, so no need to rebuild by hand
+    return tf.keras.models.load_model(model_path, compile=False)
+
+gen = load_generator()
+
+# ---------- 2. Streamlit UI ----------
+st.title("✍️ Generador de dígitos manuscritos (cGAN, 20 epochs)")
+digit = st.number_input("Dígito (0-9)", min_value=0, max_value=9, value=4, step=1)
+num   = 5
+
+if st.button("Generar"):
+    z   = tf.random.normal([num, LATENT_DIM])
+    lbl = tf.constant([[digit]] * num)
+    imgs = (gen([z, lbl], training=False) + 1) / 2      # scale [-1,1] → [0,1]
+    cols = st.columns(num)
+    for c, img in zip(cols, imgs.numpy().squeeze()):
+        c.image(Image.fromarray((img * 255).astype("uint8"), "L"), use_column_width=True)

generator_full.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4fd39f72321f992b73084050c57ea025760d4dfa0a3460d85fbd74db3bab1033
+size 9386112

requirements.txt

@@ -0,0 +1,4 @@
+streamlit>=1.34.0
+tensorflow-cpu>=2.17.0
+numpy
+Pillow

runtime.txt

@@ -0,0 +1 @@
+python-3.10.12