from uuid import uuid4
import gradio as gr
from laia.scripts.htr.decode_ctc import run as decode
from laia.common.arguments import CommonArgs, DataArgs, TrainerArgs, DecodeArgs
import sys
from tempfile import NamedTemporaryFile, mkdtemp
from pathlib import Path
from contextlib import redirect_stdout
import re
from huggingface_hub import snapshot_download

images = Path(mkdtemp())

IMAGE_ID_PATTERN = r"(?P<image_id>[-a-z0-9]{36})"
CONFIDENCE_PATTERN = r"(?P<confidence>[0-9.]+)"  # For line
TEXT_PATTERN = r"\s*(?P<text>.*)\s*"
LINE_PREDICTION = re.compile(rf"{IMAGE_ID_PATTERN} {CONFIDENCE_PATTERN} {TEXT_PATTERN}")
models_name = ["Teklia/pylaia-rimes", "Teklia/pylaia-belfort", "Teklia/pylaia-casia-hwdb2", "Teklia/pylaia-esposalles", "Teklia/pylaia-himanis", "Teklia/pylaia-home-alcar", "Teklia/pylaia-iam", "Teklia/pylaia-newseye-austrian", "Teklia/pylaia-norhand-v1", "Teklia/pylaia-norhand-v2", "Teklia/pylaia-norhand-v3", "Teklia/pylaia-popp", "Teklia/pylaia-rimes", "Teklia/pylaia-PELLET-CasimirMarius"]     
]
MODELS = {}
DEFAULT_HEIGHT = 128


def get_width(image, height=DEFAULT_HEIGHT):
    aspect_ratio = image.width / image.height
    return height * aspect_ratio


def load_model(model_name):
    if model_name not in MODELS:
        MODELS[model_name] = Path(snapshot_download(model_name))
    return MODELS[model_name]


def predict(model_name, input_img):
    model_dir = load_model(model_name)

    temperature = 2.0
    batch_size = 1

    weights_path = model_dir / "weights.ckpt"
    syms_path = model_dir / "syms.txt"
    language_model_params = {"language_model_weight": 1.0}
    use_language_model = (model_dir / "tokens.txt").exists()
    if use_language_model:
        language_model_params.update(
            {
                "language_model_path": str(model_dir / "language_model.arpa.gz"),
                "lexicon_path": str(model_dir / "lexicon.txt"),
                "tokens_path": str(model_dir / "tokens.txt"),
            }
        )

    common_args = CommonArgs(
        checkpoint=str(weights_path.relative_to(model_dir)),
        train_path=str(model_dir),
        experiment_dirname="",
    )
    data_args = DataArgs(batch_size=batch_size, color_mode="L")
    trainer_args = TrainerArgs(
        # Disable progress bar else it messes with frontend display
        progress_bar_refresh_rate=0
    )
    decode_args = DecodeArgs(
        include_img_ids=True,
        join_string="",
        convert_spaces=True,
        print_line_confidence_scores=True,
        print_word_confidence_scores=False,
        temperature=temperature,
        use_language_model=use_language_model,
        **language_model_params,
    )

    with NamedTemporaryFile() as pred_stdout, NamedTemporaryFile() as img_list:
        image_id = uuid4()
        # Resize image to 128 if bigger/smaller
        input_img = input_img.resize((int(get_width(input_img)), DEFAULT_HEIGHT))
        input_img.save(str(images / f"{image_id}.jpg"))
        # Export image list
        Path(img_list.name).write_text("\n".join([str(image_id)]))

        # Capture stdout as that's where PyLaia outputs predictions
        with redirect_stdout(open(pred_stdout.name, mode="w")):
            decode(
                syms=str(syms_path),
                img_list=img_list.name,
                img_dirs=[str(images)],
                common=common_args,
                data=data_args,
                trainer=trainer_args,
                decode=decode_args,
                num_workers=1,
            )
            # Flush stdout to avoid output buffering
            sys.stdout.flush()
        predictions = Path(pred_stdout.name).read_text().strip().splitlines()
    assert len(predictions) == 1
    _, score, text = LINE_PREDICTION.match(predictions[0]).groups()
    return input_img, {"text": text, "score": score}


gradio_app = gr.Interface(
    predict,
    inputs=[
        gr.Dropdown(models_name, value=models_name[0], label="Models"),
        gr.Image(
            label="Upload an image of a line",
            sources=["upload", "clipboard"],
            type="pil",
            height=DEFAULT_HEIGHT,
            width=2000,
            image_mode="L",
        ),
    ],
    outputs=[
        gr.Image(label="Processed Image"),
        gr.JSON(label="Decoded text"),
    ],
    examples=[
        ["Teklia/pylaia-rimes", str(filename)]
        for filename in Path("examples").iterdir()
    ],
    title="Decode the transcription of an image using a PyLaia model",
    cache_examples=True,
)

if __name__ == "__main__":
    gradio_app.launch()