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app.py

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+from transformers import DetrImageProcessor, DetrForObjectDetection
+import torch
+from PIL import Image
+import gradio as gr
+
+# Cargar el procesador y el modelo
+processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
+model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+
+# Función para procesar la imagen y detectar objetos
+def detect_objects(image):
+    inputs = processor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    # Ajustar tamaño de salida al de la imagen
+    target_sizes = torch.tensor([image.size[::-1]])
+    results = processor.post_process_object_detection(
+        outputs, target_sizes=target_sizes, threshold=0.9
+    )[0]
+
+    detected_objects = []
+    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+        label_name = model.config.id2label[label.item()]
+        detected_objects.append(
+            f"Objeto: {label_name}, Score: {score:.2f}, Box: {box.tolist()}"
+        )
+
+    return "\n".join(detected_objects)
+
+# Crear interfaz con Gradio
+def create_interface():
+    interface = gr.Interface(
+        fn=detect_objects,
+        inputs=gr.Image(type="pil"),
+        outputs=gr.Textbox(),
+        live=True,
+        title="Detección de Objetos con Transformers",
+        description="Sube una imagen y descubre qué objetos puede detectar."
+    )
+    interface.launch()
+
+if __name__ == "__main__":
+    create_interface()
+
+
+

requirements.txt

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+torch
+transformers
+pillow
+gradio