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from huggingface_hub import from_pretrained_fastai
import gradio as gr
from fastai.vision.all import *
from icevision.all import *
from icevision.models.checkpoint import *
import PIL
checkpoint_path = "efficientdetMapaches.pth"
checkpoint_and_model = model_from_checkpoint(checkpoint_path)
model = checkpoint_and_model["model"]
model_type = checkpoint_and_model["model_type"]
class_map = checkpoint_and_model["class_map"]
img_size = checkpoint_and_model["img_size"]
valid_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(img_size), tfms.A.Normalize()])
# Definimos una función que se encarga de llevar a cabo las predicciones
def predict(img):
img = PIL.Image.open(img)
pred_dict = model_type(img, valid_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.Image(shape(128,128)),
examples=['raccoon-161.jpg','raccoon-162.jpg']).launch(share=False) |