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import os
import gradio as gr
from inference import CityAestheticsMultiModelPipeline, get_model_path
TOKEN = os.environ.get("HFS_TOKEN")
HFREPO = "City96/CityAesthetics"
MODELS = [
"CityAesthetics-Anime-v1.8",
]
article = """\
# About
This is the live demo for the CityAesthetics class of predictors.
For more information, you can check out the [Huggingface Hub](https://huggingface.co/city96/CityAesthetics) or [GitHub page](https://github.com/city96/CityClassifiers).
## CityAesthetics-Anime
This flavor is optimized for scoring anime images with at least one subject present.
### Intentional biases:
- Completely negative towards real life photos (ideal score of 0%)
- Strongly Negative towards text (subtitles, memes, etc) and manga panels
- Fairly negative towards 3D and to some extent 2.5D images
- Negative towards western cartoons and stylized images (chibi, parody)
### Expected output scores:
- Non-anime images should always score below 20%
- Sketches/rough lineart/oekaki get around 20-40%
- Flat shading/TV anime gets around 40-50%
- Above 50% is mostly scored based on my personal style preferences
### Issues:
- Tends to filter male characters.
- Requires at least 1 subject, won't work for scenery/landscapes.
- Noticeable positive bias towards anime characters with animal ears.
- Hit-or-miss with AI generated images due to style/quality not being correlated.
"""
pipeline = CityAestheticsMultiModelPipeline(
[get_model_path(x, HFREPO, TOKEN) for x in MODELS],
)
gr.Interface(
fn = pipeline,
title = "CityAesthetics demo",
article = article,
inputs = gr.Image(label="Input image", type="pil"),
outputs = gr.Label(label="Model prediction", show_label=False),
examples = "./examples" if os.path.isdir("./examples") else None,
allow_flagging = "never",
analytics_enabled = False,
).launch()
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