Delete app.py with huggingface_hub

app.py

@@ -1,115 +0,0 @@
-
-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()
-