Agrego app.py

README.md

@@ -1,13 +1,12 @@
----
-title: PRY Transformers
-emoji: 👁
-colorFrom: yellow
-colorTo: green
-sdk: gradio
-sdk_version: 5.44.1
-app_file: app.py
-pinned: false
-short_description: Taller de Transformes Maestría Ciencia de Datos
----
+# DETR Object Detection (Transformers + Gradio)
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## Ejecutar localmente
+```bash
+python -m venv .venv
+# Windows
+.venv\Scripts\activate
+# macOS/Linux
+source .venv/bin/activate
+
+pip install -r requirements.txt
+python app.py

app.py

@@ -0,0 +1,104 @@
+import gradio as gr
+import torch
+from PIL import Image, ImageDraw, ImageFont
+from transformers import DetrImageProcessor, DetrForObjectDetection
+
+# --- Cargar modelo una sola vez ---
+processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
+model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50").eval()
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(DEVICE)
+ID2LABEL = model.config.id2label
+ALL_CLASSES = sorted(set(ID2LABEL.values()))
+
+def _annotate(image: Image.Image, detections):
+    annotated = image.copy()
+    draw = ImageDraw.Draw(annotated)
+    try:
+        font = ImageFont.truetype("arial.ttf", 16)
+    except Exception:
+        font = ImageFont.load_default()
+
+    for d in detections:
+        x0, y0, x1, y1 = d["box_xyxy"]
+        label = d["label"]
+        score = d["score"]
+        txt = f"{label} {score:.2f}"
+
+        # Caja
+        draw.rectangle([x0, y0, x1, y1], outline="red", width=3)
+
+        # Texto con fondo
+        try:
+            tw = draw.textlength(txt, font=font)  # Pillow 10+
+            th = 16
+        except Exception:
+            tw, th = font.getsize(txt)  # fallback
+        draw.rectangle([x0, y0 - th - 4, x0 + tw + 6, y0], fill="red")
+        draw.text((x0 + 3, y0 - th - 2), txt, fill="white", font=font)
+    return annotated
+
+def detect(image, threshold=0.9, classes=None, topk=0):
+    """
+    Detecta objetos con DETR y retorna (imagen_anotada, lista_detecciones).
+    Cada detección: {'label', 'score', 'box_xyxy'} con valores redondeados.
+    """
+    if image is None:
+        return None, []
+
+    inputs = processor(images=image, return_tensors="pt").to(DEVICE)
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    target_sizes = torch.tensor([image.size[::-1]], device=DEVICE)  # (alto, ancho)
+    results = processor.post_process_object_detection(
+        outputs, target_sizes=target_sizes, threshold=float(threshold)
+    )[0]
+
+    dets = []
+    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+        dets.append({
+            "label_id": int(label),
+            "label": ID2LABEL[int(label)],
+            "score": float(score),
+            "box_xyxy": [float(v) for v in box.tolist()]
+        })
+
+    # Filtro por clases (opcional)
+    if classes:
+        allow = set(classes)
+        dets = [d for d in dets if d["label"] in allow]
+
+    # Top-K por score (0 = sin límite)
+    if topk and int(topk) > 0:
+        dets = sorted(dets, key=lambda d: d["score"], reverse=True)[:int(topk)]
+
+    annotated = _annotate(image, dets)
+    nice_dets = [
+        {
+            "label": d["label"],
+            "score": round(d["score"], 4),
+            "box_xyxy": [round(v, 2) for v in d["box_xyxy"]],
+        }
+        for d in dets
+    ]
+    return annotated, nice_dets
+
+with gr.Blocks(title="DETR Object Detection") as demo:
+    gr.Markdown("## DETR Object Detection (Transformers + Gradio)\nSube una imagen, ajusta umbral y filtros.")
+    with gr.Row():
+        img = gr.Image(type="pil", label="Imagen de entrada")
+        with gr.Column():
+            thr = gr.Slider(0.10, 0.99, value=0.90, step=0.01, label="Umbral (threshold)")
+            classes = gr.CheckboxGroup(choices=ALL_CLASSES, label="Filtrar por clases (opcional)")
+            topk = gr.Slider(0, 200, value=0, step=1, label="Top-K por score (0 = sin límite)")
+            btn = gr.Button("Detectar", variant="primary")
+
+    out_img = gr.Image(type="pil", label="Imagen anotada")
+    out_json = gr.JSON(label="Detecciones (JSON)")
+
+    btn.click(detect, inputs=[img, thr, classes, topk], outputs=[out_img, out_json])
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,7 @@
+transformers>=4.42.0
+torch>=2.2.0
+torchvision>=0.17.0
+gradio>=4.29.0
+pillow>=10.3.0
+safetensors>=0.4.3
+timm>=0.9.16,<1.0.0