init

.gitignore

@@ -0,0 +1,116 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+.idea/

README.md

@@ -1,6 +1,6 @@
 ---
 title: Best Anime Or Not
-emoji: 💻
+emoji: ❤️🖼️
 colorFrom: purple
 colorTo: indigo
 sdk: gradio

app.py

@@ -0,0 +1,26 @@
+import cv2
+import numpy as np
+import gradio as gr
+import onnxruntime as rt
+from huggingface_hub import hf_hub_download
+
+
+def predict(img):
+    img = img.astype(np.float32) / 255
+    s = 640
+    h, w = img.shape[:-1]
+    h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
+    ph, pw = s - h, s - w
+    img_input = np.zeros([s, s, 3], dtype=np.float32)
+    img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(img, (w, h))
+    img_input = np.transpose(img_input, (2, 0, 1))
+    img_input = img_input[np.newaxis, :]
+    pred = model.run(None, {"img": img_input})[0][0]
+    return {"not best": pred[0].item(), "best": pred[1].item()}
+
+
+if __name__ == "__main__":
+    model_path = hf_hub_download(repo_id="skytnt/anime_quality", filename="classifier.onnx")
+    model = rt.InferenceSession(model_path, providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
+    app = gr.Interface(predict, gr.Image(label="input image"), gr.Label(label="result"),title="Best Anime or Not", allow_flagging="never")
+    app.launch()

requirements.txt

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+onnxruntime-gpu
+opencv-python
+numpy