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"""Provide an image of a chessboard and get the FEN (https://en.wikipedia.org/wiki/Forsyth–Edwards_Notation ) representation of the board."""
import argparse
import json
import logging
import os
from pathlib import Path
from typing import Callable
import gradio as gr
from PIL import ImageStat
from PIL.Image import Image
import requests
from image_to_fen.fen import ImageToFen
import image_to_fen.util as util
os.environ["CUDA_VISIBLE_DEVICES"] = "" # do not use GPU
logging.basicConfig(level=logging.INFO)
APP_DIR = Path(__file__).resolve().parent
FAVICON = APP_DIR / "265f.png"
README = APP_DIR / "README.md"
DEFAULT_PORT = 11700
def main(args):
predictor = PredictorBackend(url=args.model_url)
frontend = make_frontend(
predictor.run
)
frontend.launch()
def make_frontend(
fn: Callable[[Image], str],
app_name: str = "image-to-fen"
):
"""Creates a gradio.Interface frontend for an image to text function."""
examples_dir = Path("image_to_fen") / "tests" / "support" / "boards"
example_fnames = [elem for elem in os.listdir(examples_dir) if elem.endswith(".png")]
example_paths = [examples_dir / fname for fname in example_fnames]
examples = [[str(path)] for path in example_paths]
allow_flagging = "never"
readme = _load_readme(with_logging=allow_flagging == "manual")
# build a basic browser interface to a Python function
frontend = gr.Interface(
fn=fn,
outputs=gr.components.Textbox(),
inputs=gr.components.Image(type="pil", label="Chess Board"),
title="♟️ Image to Fen",
thumbnail="FAVICON",
description=__doc__,
article=readme,
examples=examples,
cache_examples=False,
allow_flagging=allow_flagging
)
return frontend
class PredictorBackend:
"""Interface to a backend that serves predictions.
To communicate with a backend accessible via a URL, provide the url kwarg.
Otherwise, runs a predictor locally.
"""
def __init__(self, url=None):
if url is not None:
self.url = url
self._predict = self._predict_from_endpoint
else:
model = ImageToFen()
self._predict = model.predict
def run(self, image):
pred, metrics = self._predict_with_metrics(image)
self._log_inference(pred, metrics)
return pred
def _predict_with_metrics(self, image):
pred = self._predict(image)
stats = ImageStat.Stat(image)
metrics = {
"image_mean_intensity": stats.mean,
"image_median": stats.median,
"image_extrema": stats.extrema,
"image_area": image.size[0] * image.size[1],
"pred_length": len(pred),
}
return pred, metrics
def _predict_from_endpoint(self, image):
"""Send an image to an endpoint that accepts JSON and return the predicted text.
The endpoint should expect a base64 representation of the image, encoded as a string,
under the key "image". It should return the predicted text under the key "pred".
Parameters
----------
image
A PIL image of a chess board.
Returns
-------
pred
A string containing the predictor's guess of the FEN representation of the chess board.
"""
encoded_image = util.encode_b64_image(image)
headers = {"Content-type": "application/json"}
payload = json.dumps({"image": "data:image/png;base64," + encoded_image})
response = requests.post(self.url, data=payload, headers=headers)
pred = response.json()["pred"]
return pred
def _log_inference(self, pred, metrics):
for key, value in metrics.items():
logging.info(f"METRIC {key} {value}")
logging.info(f"PRED >begin\n{pred}\nPRED >end")
def _load_readme(with_logging=False):
with open(README) as f:
lines = f.readlines()
# if not with_logging:
# lines = lines[: lines.index("<!-- logging content below -->\n")]
readme = "".join(lines)
return readme
def _make_parser():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--model_url",
default=None,
type=str,
help="Identifies a URL to which to send image data. Data is base64-encoded, converted to a utf-8 string, and then set via a POST request as JSON with the key 'image'. Default is None, which instead sends the data to a model running locally.",
)
parser.add_argument(
"--port",
default=DEFAULT_PORT,
type=int,
help=f"Port on which to expose this server. Default is {DEFAULT_PORT}.",
)
return parser
if __name__ == "__main__":
parser = _make_parser()
args = parser.parse_args()
main(args) |