Update app.py

app.py

@@ -1,47 +1,44 @@
-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()
-
-
-
+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, threshold=0.9):
+    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=threshold
+    )[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: { [round(v,2) for v in box.tolist()] }"
+        )
+
+    return "\n".join(detected_objects)
+
+# Interfaz con la API nueva de Gradio
+demo = gr.Interface(
+    fn=detect_objects,
+    inputs=[gr.Image(type="pil", label="Imagen"), gr.Slider(0, 1, value=0.9, step=0.05, label="Umbral")],
+    outputs=gr.Textbox(label="Detecciones"),
+    title="Detección de Objetos con Transformers (DETR)",
+    description="Sube una imagen y descubre qué objetos puede detectar."
+)
+
+if __name__ == "__main__":
+    demo.launch()
+
+
+