Initial commit: add Gradio app and requirements

app.py

@@ -1,22 +1,24 @@
+
+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 pathlib
-import tempfile
-import gradio as gr
-import pandas as pd
-import numpy as np
-import PIL.Image
 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"
+zip_filename  = "autogluon_image_predictor_dir.zip"
 HF_TOKEN = os.getenv("HF_TOKEN", None)
-cache_dir = pathlib.Path("hf_assets")
+cache_dir   = pathlib.Path("hf_assets")
 extract_dir = cache_dir / "predictor_native"
 
-def prepare_predictor_dir():
+def prepare_predictor_dir() -> str:
     cache_dir.mkdir(parents=True, exist_ok=True)
     local_zip = h.hf_hub_download(
         repo_id=model_repo_id,
@@ -38,7 +40,7 @@ def prepare_predictor_dir():
 predictor_dir = prepare_predictor_dir()
 predictor = autogluon.multimodal.MultiModalPredictor.load(predictor_dir)
 
-def do_predict(pil_img, preprocess=True):
+def do_predict(pil_img: PIL.Image.Image, preprocess: bool = True):
     if pil_img is None:
         return "No image provided.", None, None
 
@@ -56,8 +58,11 @@ def do_predict(pil_img, preprocess=True):
         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]
 
@@ -68,6 +73,7 @@ def do_predict(pil_img, preprocess=True):
 
     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"],
@@ -75,6 +81,7 @@ EXAMPLES = [
 ]
 
 with gr.Blocks() as demo:
+
     gr.Markdown("# Is this a STOP sign or not?")
     gr.Markdown("Upload a photo to see results.")
 
@@ -105,3 +112,4 @@ with gr.Blocks() as demo:
 
 if __name__ == "__main__":
     demo.launch()
+

requirements.txt

@@ -1,6 +1,5 @@
+
+autogluon.multimodal
 gradio
-pandas
-numpy
 pillow
-autogluon.multimodal
 huggingface_hub