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README.md

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+
 ---
-title: Vision Classifier
-emoji: 🦀
-colorFrom: gray
-colorTo: purple
+title: Simple Vision Classifier (ViT)
+emoji: 🐼
+colorFrom: green
+colorTo: blue
 sdk: gradio
-sdk_version: 5.47.2
+sdk_version: 4
 app_file: app.py
 pinned: false
+python_version: 3.10
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🐼 Simple Image Classifier (ViT)
+
+Este Space demuestra un **clasificador de imágenes** con modelos del Hub (ViT/DeiT/ResNet) usando `transformers` + `gradio`.
+
+## 🚀 Cómo usarlo (local)
+
+```bash
+pip install -r requirements.txt
+python app.py
+```
+
+## 🔧 Cambiar el modelo
+En la UI hay un desplegable con modelos populares:
+
+- `google/vit-base-patch16-224`
+- `facebook/deit-base-patch16-224`
+- `microsoft/resnet-50`
+
+Puedes añadir más en `MODEL_OPTIONS` dentro de `app.py`.

app.py

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+
+import gradio as gr
+from PIL import Image
+from transformers import pipeline
+import torch
+
+DESCRIPTION = """
+# 🐼 Simple Image Classifier (ViT)
+Sube una imagen y el modelo devuelve las **probabilidades top‑k** de las clases.
+Puedes elegir distintos modelos del Hub (cargados automáticamente).
+"""
+
+DEFAULT_MODEL = "google/vit-base-patch16-224"
+MODEL_OPTIONS = [
+    "google/vit-base-patch16-224",
+    "facebook/deit-base-patch16-224",
+    "microsoft/resnet-50",
+]
+
+# Cache del pipeline para no recargar al cambiar parámetros que no sean el modelo
+_pipes = {}
+
+def get_pipe(model_id: str):
+    if model_id not in _pipes:
+        # device map sencillo: usa GPU si está disponible
+        device = 0 if torch.cuda.is_available() else -1
+        _pipes[model_id] = pipeline(
+            task="image-classification",
+            model=model_id,
+            device=device
+        )
+    return _pipes[model_id]
+
+def classify(image: Image.Image, model_id: str, top_k: int):
+    if image is None:
+        return []
+    pipe = get_pipe(model_id)
+    # Asegurar modo RGB
+    image = image.convert("RGB")
+    preds = pipe(image, top_k=top_k)
+    # Normalizamos a una tabla [(label, score)]
+    rows = [(p["label"], float(p["score"])) for p in preds]
+    return rows
+
+with gr.Blocks() as demo:
+    gr.Markdown(DESCRIPTION)
+    with gr.Row():
+        with gr.Column(scale=2):
+            image = gr.Image(type="pil", label="Imagen de entrada")
+            with gr.Row():
+                model_id = gr.Dropdown(
+                    choices=MODEL_OPTIONS,
+                    value=DEFAULT_MODEL,
+                    label="Modelo"
+                )
+                top_k = gr.Slider(1, 10, value=5, step=1, label="Top‑K")
+            btn = gr.Button("Clasificar")
+        with gr.Column(scale=1):
+            output = gr.Dataframe(
+                headers=["label", "score"],
+                datatype=["str", "number"],
+                label="Resultados"
+            )
+    btn.click(fn=classify, inputs=[image, model_id, top_k], outputs=output)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+
+gradio
+transformers
+torch
+timm
+Pillow
+accelerate
+huggingface_hub