import re
import subprocess
import yaml

import gradio as gr
import requests
from huggingface_hub import HfApi


CMD = ["python" ,"run.py"]

with open("README.md") as f:
    METADATA = yaml.safe_load(f.read().split("---\n")[1])
TITLE = METADATA["title"]
EMOJI = METADATA["emoji"]

try:
    process = subprocess.run(CMD + ["--help"], stdout=subprocess.PIPE, stderr=subprocess.PIPE)
    HELP = not process.returncode and (process.stdout or process.stderr).decode()
except Exception:
    HELP = False

DRY_RUN = bool(HELP) and bool(m :=re.search("--dry(-|_)run", HELP)) and m.group(0)

def update_pbars(pbars: dict[str, float], line: str):
    if (percent_match := re.search("\\d+(?:\\.\\d+)?%", line)) and any(c in line.split("%")[1][:10] for c in "|█▌"):
        [pbars.pop(desc) for desc, percent in pbars.items() if percent == 1.]
        percent = float(percent_match.group(0)[:-1]) / 100
        desc = line[:percent_match.start()].strip() or "Progress"
        pbars[desc] = percent

def dry_run(src, config, split, dst, query):
    if not all([src, config, split, dst, query]):
        raise gr.Error("Please fill source, destination and query.")
    process = subprocess.Popen(CMD + ["--src", src, "--config", config, "--split", split, "--dst", dst, "--query", query, DRY_RUN], stdout=subprocess.PIPE)
    logs = ""
    for line in iter(process.stdout.readline, b""):
        logs += line.decode()
        yield {output_markdown: logs, progress_labels: gr.Label(visible=False)}

def run(src, config, split, dst, query):
    if not all([src, config, split, dst, query]):
        raise gr.Error("Please fill source, destination and query.")
    raise gr.Error("NotImplemented")

READ_FUNCTIONS = ("pl.read_parquet", "pl.read_csv", "pl.read_json")
NUM_TRENDING_DATASETS = 10

with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column(scale=10):
            gr.Markdown(f"# {TITLE} {EMOJI}")
        with gr.Column():
            gr.LoginButton(scale=0.1)
    with gr.Row():
        with gr.Column():
            with gr.Row():
                loading_codes_json = gr.JSON([], visible=False)
                dataset_dropdown = gr.Dropdown(label="Source Dataset", allow_custom_value=True, scale=10)
                subset_dropdown = gr.Dropdown(info="Subset", allow_custom_value=True, show_label=False, visible=False)
                split_dropdown = gr.Dropdown(info="Split", allow_custom_value=True, show_label=False, visible=False)
        with gr.Column(scale=0.1, min_width=60):
            gr.HTML("<div style='font-size: 4em;'>→</div>")
        with gr.Column():
            dst_dropdown = gr.Dropdown(label="Destination Dataset", allow_custom_value=True)
    query_textarea = gr.TextArea(label="SQL Query", placeholder="SELECT * FROM src;", value="SELECT * FROM src;", container=False, show_label=False)
    with gr.Row():
        run_button = gr.Button("Run", scale=10, variant="primary")
        if DRY_RUN:
            dry_run_button = gr.Button("Dry-Run")
    progress_labels= gr.Label(visible=False, label="Progress")
    output_markdown = gr.Markdown(label="Output logs")
    run_button.click(run, inputs=[dataset_dropdown, subset_dropdown, split_dropdown, dst_dropdown, query_textarea], outputs=[progress_labels, output_markdown])
    if DRY_RUN:
        dry_run_button.click(dry_run, inputs=[dataset_dropdown, subset_dropdown, split_dropdown, dst_dropdown, query_textarea], outputs=[progress_labels, output_markdown])

    def show_subset_dropdown(dataset: str):
        if dataset and "/" not in dataset.strip().strip("/"):
            return []
        resp = requests.get(f"https://datasets-server.huggingface.co/compatible-libraries?dataset={dataset}", timeout=3).json()
        loading_codes = ([lib["loading_codes"] for lib in resp.get("libraries", []) if lib["function"] in READ_FUNCTIONS] or [[]])[0] or []
        subsets = [loading_code["config_name"] for loading_code in loading_codes]
        subset = (subsets or [""])[0]
        return dict(choices=subsets, value=subset, visible=len(subsets) > 1, key=hash(str(loading_codes))), loading_codes

    def show_split_dropdown(subset: str, loading_codes: list[dict]):
        splits = ([list(loading_code["arguments"]["splits"]) for loading_code in loading_codes if loading_code["config_name"] == subset] or [[]])[0]
        split = (splits or [""])[0]
        return dict(choices=splits, value=split, visible=len(splits) > 1, key=hash(str(loading_codes) + subset))

    @demo.load(outputs=[dataset_dropdown, loading_codes_json, subset_dropdown, split_dropdown])
    def _fetch_datasets(request: gr.Request):
        dataset = "CohereForAI/Global-MMLU"
        datasets = [dataset] + [ds.id for ds in HfApi().list_datasets(limit=NUM_TRENDING_DATASETS, sort="trendingScore", direction=-1) if ds.id != dataset]
        subsets, loading_codes = show_subset_dropdown(dataset)
        splits = show_split_dropdown(subsets["value"], loading_codes)
        return {
            dataset_dropdown: gr.Dropdown(choices=datasets, value=dataset),
            loading_codes_json: loading_codes,
            subset_dropdown: gr.Dropdown(**subsets),
            split_dropdown: gr.Dropdown(**splits),
        }
    
    @dataset_dropdown.select(inputs=[dataset_dropdown], outputs=[subset_dropdown, split_dropdown])
    def _show_subset_dropdown(dataset: str):
        subsets, loading_codes = show_subset_dropdown(dataset)
        splits = show_split_dropdown(subsets["value"], loading_codes)
        return {
            subset_dropdown: gr.Dropdown(**subsets),
            split_dropdown: gr.Dropdown(**splits),
        }
    
    @subset_dropdown.select(inputs=[dataset_dropdown, subset_dropdown, loading_codes_json], outputs=[split_dropdown])
    def _show_split_dropdown(dataset: str, subset: str, loading_codes: list[dict]):
        splits = show_split_dropdown(subset, loading_codes)
        return {
            split_dropdown: gr.Dropdown(**splits),
        }

if HELP:
    with demo.route("Help", "/help"):
        gr.Markdown(f"# Help\n\n```\n{HELP}\n```")

with demo.route("Jobs", "/jobs"):
    gr.Markdown("# Jobs")

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0")