from pathlib import Path

import gradio as gr
import yaml


PROJECT_ROOT = Path(__file__).resolve().parent


def list_configs() -> list[str]:
    return sorted(str(path.relative_to(PROJECT_ROOT)).replace("\\", "/") for path in PROJECT_ROOT.glob("configs/**/*.yaml"))


def show_config(config_name: str) -> str:
    if not config_name:
        return "Select a config."
    path = PROJECT_ROOT / config_name
    data = yaml.safe_load(path.read_text(encoding="utf-8"))
    return yaml.safe_dump(data, sort_keys=False)


with gr.Blocks(title="DIPAug Project Hub") as demo:
    gr.Markdown(
        """
        # DIPAug Project Hub

        **Project Title**

        Realistic Digital Image Processing-Driven Data Augmentation for Robust Wheat Leaf Disease Classification and Severity Scoring in Field Conditions

        **Short Titles**

        - `DIPAug-Net`
        - `DIPAug-SeverNet`

        This Hugging Face app is a lightweight dashboard for the project scaffold. It helps inspect the experiment configs and repository structure before training on a proper GPU machine.
        """
    )

    with gr.Row():
        with gr.Column(scale=2):
            config_input = gr.Dropdown(label="Experiment Config", choices=list_configs(), value="configs/phase1/e6_full.yaml")
            config_output = gr.Code(label="YAML", language="yaml", value=show_config("configs/phase1/e6_full.yaml"))
        with gr.Column(scale=2):
            gr.Markdown(
                """
                ## Included Modules

                - `dipauglib.transforms`: 8 physics-aware augmentations
                - `dipauglib.schedulers`: adaptive augmentation scheduler
                - `dipauglib.sampling`: class-imbalance-aware sampling
                - `dipaugnet`: phase 1 classification pipeline
                - `dipaugsevernet`: phase 2 segmentation and severity scaffold

                ## Status

                - repository scaffold: ready
                - configs: ready
                - tests: included
                - full training runs: not executed in this dashboard
                """
            )

    config_input.change(fn=show_config, inputs=[config_input], outputs=[config_output])


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