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import gradio as gr
import PIL.Image
import numpy as np
import pandas as pd
import pathlib
import tempfile
import os
import shutil
import zipfile
import huggingface_hub as h
from huggingface_hub import HfApi, Repository
import autogluon.multimodal
model_repo_id = "nadakandrew/sign-identification-autogluon"
zip_filename = "autogluon_image_predictor_dir.zip"
HF_TOKEN = os.getenv("HF_TOKEN", None)
cache_dir = pathlib.Path("hf_assets")
extract_dir = cache_dir / "predictor_native"
def prepare_predictor_dir() -> str:
cache_dir.mkdir(parents=True, exist_ok=True)
local_zip = h.hf_hub_download(
repo_id=model_repo_id,
filename=zip_filename,
repo_type="model",
token=HF_TOKEN,
local_dir=str(cache_dir),
local_dir_use_symlinks=False,
)
if extract_dir.exists():
shutil.rmtree(extract_dir)
extract_dir.mkdir(parents=True, exist_ok=True)
with zipfile.ZipFile(local_zip, "r") as zf:
zf.extractall(str(extract_dir))
contents = list(extract_dir.iterdir())
predictor_root = contents[0] if (len(contents) == 1 and contents[0].is_dir()) else extract_dir
return str(predictor_root)
predictor_dir = prepare_predictor_dir()
predictor = autogluon.multimodal.MultiModalPredictor.load(predictor_dir)
def do_predict(pil_img: PIL.Image.Image, preprocess: bool = True):
if pil_img is None:
return "No image provided.", None, None
original_img = pil_img.copy()
preprocessed_img = None
if preprocess:
target_size = (224, 224)
preprocessed_img = pil_img.resize(target_size).convert("RGB")
tmpdir = pathlib.Path(tempfile.mkdtemp())
img_path = tmpdir / "input.png"
preprocessed_img.save(img_path)
else:
tmpdir = pathlib.Path(tempfile.mkdtemp())
img_path = tmpdir / "input.png"
pil_img.save(img_path)
df = pd.DataFrame({"image": [str(img_path)]})
proba_df = predictor.predict_proba(df)
proba_df = proba_df.rename(columns={0: "class_0", 1: "class_1"})
row = proba_df.iloc[0]
pretty_dict = {
"Not a STOP sign": float(row.get("class_0", 0.0)),
"STOP sign": float(row.get("class_1", 0.0)),
}
return pretty_dict, original_img, preprocessed_img
EXAMPLES = [
["https://universalsigns.com/wp-content/uploads/2022/08/StopSign-3.jpg"],
["https://images.roadtrafficsigns.com/img/pla/K/student-drop-off-area-sign-k-2459_pl.png"],
["https://hansonsign.com/wp-content/uploads/2024/05/donatos-662x646.jpg"]
]
with gr.Blocks() as demo:
gr.Markdown("# Is this a STOP sign or not?")
gr.Markdown("Upload a photo to see results.")
with gr.Row():
image_in = gr.Image(type="pil", label="Input image", sources=["upload", "webcam"])
original_img_out = gr.Image(type="pil", label="Original image")
preprocessed_img_out = gr.Image(type="pil", label="Preprocessed image")
with gr.Row():
preprocess_checkbox = gr.Checkbox(label="Apply Preprocessing", value=True)
proba_pretty = gr.Label(num_top_classes=2, label="Class probabilities")
image_in.change(
fn=do_predict,
inputs=[image_in, preprocess_checkbox],
outputs=[proba_pretty, original_img_out, preprocessed_img_out]
)
gr.Examples(
examples=EXAMPLES,
inputs=[image_in],
label="Choose any one",
examples_per_page=8,
cache_examples=False,
)
if __name__ == "__main__":
demo.launch()
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