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import cv2
import numpy as np
import gradio as gr
from ultralytics import YOLO
def predict(path:str, threshold: float = 0.6):
model = YOLO("best.pt")
imagen = cv2.imread(path)
results = model.predict(source=path)
for r in results:
# Mantener solo las cajas con una probabilidad mayor al umbral
boxes = [box for box in r.boxes if box.conf > threshold]
r.boxes = boxes # Actualizar las cajas filtradas
return r.plot()
gr.Interface(fn=predict,
inputs=gr.components.Image(type="filepath", label="Input"),
outputs=gr.components.Image(type="numpy", label="Output")).launch(debug=False) |