import gradio as gr import PIL import torch from fastai.vision.all import models from icevision.all import ClassMap, tfms # repo_id = "YOUR_USERNAME/YOUR_LEARNER_NAME" repo_id = "el-filatova/Practica2" class_map = ClassMap(["kangaroo"]) state_dict = torch.load("fasterRCNNFkangaroo.pth") model = models.torchvision.faster_rcnn.model( backbone=models.torchvision.faster_rcnn.backbones.resnet50_fpn, num_classes=len(class_map), ) model.load_state_dict(state_dict) size = 384 # Definimos una funciĆ³n que se encarga de llevar a cabo las predicciones def predict(img_file): img = PIL.Image.open(img_file) infer_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(size), tfms.A.Normalize()]) pred_dict = models.torchvision.faster_rcnn.end2end_detect( img, infer_tfms, model.to("cpu"), class_map=class_map, detection_threshold=0.5 ) return pred_dict["img"] # Creamos la interfaz y la lanzamos. gr.Interface( fn=predict, inputs=gr.inputs.Image(shape=(128, 128)), outputs=gr.outputs.Label(num_top_classes=3), examples=["image.jpg"], ).launch(share=False)