app.py

from ultralytics import YOLO
import matplotlib.pyplot as plt
import gradio as gr
import cv2

import seaborn as sns

def predict(path:str):
    model = YOLO("yolov8n.yaml")
    model = YOLO("best.pt")
    image = cv2.imread(path)
    
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    results = model.predict(source=path) 
    paleta= sns.color_palette("bright", 17)
    fig = plt.figure()
    vectObjs = results[0].masks.xy
    resNumClase = results[0].boxes.cls.numpy().astype(int)
    conf = results[0].boxes.conf.numpy()
    for i in range(len(vectObjs)):
        objDet = vectObjs[i].astype(int)
        color = (paleta[i][0]*255, paleta[i][1]*255, paleta[i][2]*255)
        image = cv2.polylines(image, [objDet], True, color, 4)
        plt.text(objDet[0][0], objDet[0][1], results[0].names[resNumClase[i]]+" "+ str(conf[i]), bbox=dict(facecolor=paleta[i], alpha=0.5))
    
    plt.imshow(image)
    plt.axis('off')
    return plt
gr.Interface(fn=predict, 
             inputs=gr.components.Image(type="filepath", label="Input"),
             outputs=gr.Plot(label="Resultado de detección de objetos con regularizacion")).launch()
             #outputs=gr.components.Image(type="pil", label="Output")).launch()