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import gradio as gr
from transformers import AutoImageProcessor, AutoModelForObjectDetection
import torch
from PIL import Image, ImageDraw
# Cargar modelo desde el Hub (Recomendado) o Local
# Si subiste tu modelo con trainer.push_to_hub(), usa tu ID: NO 'yolo_finetuned_raccoon' local.
# Ejemplo: model_id = "daniihc16/yolo_finetuned_raccoon" (Sustituye por tu usuario)
# AQUÍ DEBES PONER EL ID DE TU MODELO SUBIDO A HUGGINGFACE
model_id = "daniihc16/yolo_finetuned_raccoon"
try:
image_processor = AutoImageProcessor.from_pretrained(model_id)
model = AutoModelForObjectDetection.from_pretrained(model_id)
except Exception as e:
print(f"Error cargando modelo: {e}. Asegúrate de poner el ID correcto.")
raise e
def predict(image):
if image is None: return None
inputs = image_processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
target_sizes = torch.tensor([image.size[::-1]])
# Usamos un umbral de 0.5 para mostrar solo detecciones firmes
results = image_processor.post_process_object_detection(outputs, threshold=0.5, target_sizes=target_sizes)[0]
draw = ImageDraw.Draw(image)
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
box = [round(i, 2) for i in box.tolist()]
x, y, x2, y2 = tuple(box)
# Dibujar caja
draw.rectangle((x, y, x2, y2), outline="red", width=3)
# Dibujar etiqueta
label_name = model.config.id2label[label.item()]
draw.text((x, y), f"{label_name}: {round(score.item(), 2)}", fill="red")
return image
iface = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil"),
outputs=gr.Image(type="pil"),
title="Detector de Mapaches (Raccoon Detection)",
description="Sube una imagen para detectar mapaches usando un modelo YOLOS Finetuned.",
examples=['raccoon-1.jpg', 'raccoon-12.jpg']
)
if __name__ == "__main__":
iface.launch()
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