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from inference import Inference
import argparse
import gradio as gr
import glob
from huggingface_hub import hf_hub_download
import os
def parse_option():
parser = argparse.ArgumentParser('MetaFG Inference script', add_help=False)
parser.add_argument('--cfg', type=str, metavar="FILE", help='path to config file')
# easy config modification
parser.add_argument('--model-path', type=str, help="path to model data")
parser.add_argument('--img-size', type=int, default=384, help='path to image')
parser.add_argument('--meta-path', default="meta.txt", type=str, help='path to meta data')
parser.add_argument('--names-path', default="names_mf2.txt", type=str, help='path to meta data')
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_option()
if not args.model_path:
model_path = hf_hub_download(repo_id="joshvm/inaturalist_sgd_4k",
filename="ckpt_epoch_35.pth",
token=os.environ["HUGGINGFACE_TOKEN"])
else:
model_path = args.model_path
if not args.cfg:
model_config = hf_hub_download(repo_id="joshvm/inaturalist_sgd_4k",
filename="MetaFG_2_224.yaml",
token=os.environ["HUGGINGFACE_TOKEN"])
else:
model_config = args.cfg
if not args.names_path:
names_path = hf_hub_download(repo_id="joshvm/inaturalist_sgd_4k",
filename="names_mf2.txt",
token=os.environ["HUGGINGFACE_TOKEN"])
else:
names_path = args.names_path
model = Inference(config_path=model_config,
model_path=model_path,
names_path=names_path)
def classify(image):
preds = model.infer(img_path=image, meta_data_path="meta.txt")
#confidences = {c: float(preds[i]) for i,c in enumerate(model.classes)}
return preds
gr.Interface(fn=classify,
inputs=gr.Image(shape=(args.img_size, args.img_size), type="pil"),
outputs=gr.Label(num_top_classes=10),
examples=glob.glob("./example_images/*")).launch()
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