Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import pandas as pd
+from tensorflow import keras
+from PIL import Image
+import io
+import base64
+
+from sklearn.metrics.pairwise import cosine_similarity
+
+PATH_MODEL = "./autoencoder.keras"
+PATH_DB = "./mnist_train_small.csv"
+
+# ── Cargar modelo y datos al iniciar ─────────────────────────────────────────
+model    = keras.models.load_model(PATH_MODEL)
+encoder  = model.get_layer("encoder")
+decoder  = model.get_layer("decoder")
+
+data     = pd.read_csv(PATH_DB, header=None)
+X_ref    = data.iloc[:, 1:].values.astype("float32") / 255
+X_latent = encoder.predict(X_ref, verbose=0)
+
+LATENT_DIM = 32
+
+# ── Helper: imagen subida → array (1, 784) ────────────────────────────────────
+def image_to_array(canva):
+    img = canva['composite'].convert("L")
+    img = img.resize((28, 28))
+    arr = 1 - np.array(img, dtype="float32") / 255
+    return arr.reshape(1, 784)
+
+def find_similar(img, top_k):
+    X         = image_to_array(img)
+    query_vec = encoder.predict(X, verbose=0)
+    sims      = cosine_similarity(query_vec, X_latent)[0]
+    top_idx   = np.argsort(sims)[::-1][:int(top_k)]
+
+    best_arr = (X_ref[top_idx[0]].reshape(28, 28) * 255).astype(np.uint8)
+    best_img = Image.fromarray(best_arr)
+
+    table = [[int(i), round(float(sims[i]), 4)] for i in top_idx]
+
+    gallery_imgs = [
+        Image.fromarray((X_ref[i].reshape(28, 28) * 255).astype(np.uint8))
+        for i in top_idx
+    ]
+    return table, best_img, gallery_imgs
+
+with gr.Blocks() as demo:
+
+    with gr.Tab("Búsqueda"):
+
+        gr.Markdown("## Búsqueda en espacio latente")
+        with gr.Row():
+            with gr.Column():
+              canvas = gr.Sketchpad(label="Dibuja", type='pil')
+            with gr.Column():
+              topk = gr.Slider(1, 50, value=10, step=1, label="top_k")
+              btn  = gr.Button("Buscar similares")
+
+        gallery = gr.Gallery(label="Imágenes similares", columns=5, object_fit="contain")
+
+    with gr.Tab("Metadatos"):
+
+        results = gr.Dataframe(
+            headers=["index", "cosine_similarity"],
+            datatype=["number", "number"],
+            label="Ranking",
+            interactive=False
+        )
+
+    btn.click(find_similar, inputs=[canvas, topk], outputs=[results, best, gallery])
+demo.launch(server_port=7860)