"""
Digital Review System (DRS) application for LBW decisions
========================================================
"""

from __future__ import annotations

import os
import shutil
import tempfile
from pathlib import Path
from typing import Any, Dict, Tuple

import gradio as gr

from drs_modules.video_processing import trim_last_seconds, save_uploaded_video
from drs_modules.detection import detect_and_track_ball
from drs_modules.trajectory import estimate_trajectory, predict_stumps_intersection
from drs_modules.lbw_decision import make_lbw_decision
from drs_modules.visualization import (
    generate_trajectory_plot,
    annotate_video_with_tracking,
)


def analyse_appeal(video_path: str, review_seconds: int = 8) -> Tuple[str, Dict[str, Any]]:
    temp_dir = tempfile.mkdtemp()
    trimmed_path = os.path.join(temp_dir, "trimmed.mp4")

    trim_last_seconds(video_path, trimmed_path, review_seconds)
    tracking_data = detect_and_track_ball(trimmed_path)
    trajectory_model = estimate_trajectory(tracking_data["centers"], tracking_data["timestamps"])
    will_hit_stumps = predict_stumps_intersection(trajectory_model)
    decision, impact_frame_idx = make_lbw_decision(
        tracking_data["centers"], trajectory_model, will_hit_stumps
    )

    total_distance_px = 0.0
    for i in range(1, len(tracking_data["centers"])):
        cx0, cy0 = tracking_data["centers"][i - 1]
        cx1, cy1 = tracking_data["centers"][i]
        total_distance_px += ((cx1 - cx0) ** 2 + (cy1 - cy0) ** 2) ** 0.5

    duration = tracking_data["timestamps"][-1] - tracking_data["timestamps"][0]
    if duration <= 0:
        speed_kmh = 0.0
    else:
        pixels_per_metre = 50.0
        speed_mps = (total_distance_px / pixels_per_metre) / duration
        speed_kmh = speed_mps * 3.6

    annotated_video_path = os.path.join(temp_dir, "annotated.mp4")
    annotate_video_with_tracking(
        trimmed_path,
        tracking_data["centers"],
        trajectory_model,
        will_hit_stumps,
        impact_frame_idx,
        annotated_video_path,
    )
    plot_path = os.path.join(temp_dir, "trajectory_plot.png")
    generate_trajectory_plot(
        tracking_data["centers"], trajectory_model, will_hit_stumps, plot_path
    )

    decision_message = f"Decision: {decision}"
    result = {
        "decision": decision,
        "ball_speed_kmh": round(speed_kmh, 2),
        "impact_frame_index": impact_frame_idx,
        "annotated_video": annotated_video_path,
        "trajectory_plot": plot_path,
    }

    return decision_message, result


def build_interface() -> gr.Blocks:
    with gr.Blocks(title="Cricket LBW DRS Demo") as demo:
        gr.Markdown(
            """# Digital Review System (LBW)
This demo lets you record or upload cricket match footage and analyse LBW appeals.
You'll get a 3D trajectory plot, annotated replay, and OUT/NOT OUT decision.
"""
        )

        with gr.Tab("Live Match Recording"):
            video_input = gr.Video(
                label="Record or upload match video",
                sources=["upload", "webcam"]
            )
            out_video_path = gr.State()

            def on_video_upload(video_file):
                if video_file is None:
                    return ""
                file_path = video_file.name if hasattr(video_file, "name") else video_file
                return save_uploaded_video(file_path, video_file)
            video_output = gr.Textbox(visible=False)
            video_input.change(
                fn=on_video_upload,
                inputs=[video_input],
                outputs=[out_video_path],
            )

            gr.Markdown(
                """
After recording or uploading a video, switch to the **LBW Review** tab and click **Analyse Appeal**.
"""
            )

        with gr.Tab("LBW Review"):
            with gr.Row():
                analyse_button = gr.Button("Analyse Appeal")
                review_seconds = gr.Number(
                    value=8, label="Seconds to review", minimum=2, maximum=20
                )

            decision_output = gr.Textbox(label="Decision", lines=1)
            ball_speed_output = gr.Textbox(label="Ball speed (km/h)", lines=1, interactive=False)
            impact_frame_output = gr.Textbox(label="Impact frame index", lines=1, interactive=False)
            annotated_video_output = gr.Video(label="Annotated replay video")
            trajectory_plot_output = gr.Image(label="3D Trajectory plot")

            def on_analyse(video_path):
                if not video_path or not os.path.exists(video_path):
                    return (
                        "Please record or upload a video in the first tab.",
                        None,
                        None,
                        None,
                        None,
                    )
                message, result = analyse_appeal(video_path, int(review_seconds.value))
                return (
                    message,
                    str(result["ball_speed_kmh"]),
                    str(result["impact_frame_index"]),
                    result["annotated_video"],
                    result["trajectory_plot"],
                )

            analyse_button.click(
                fn=on_analyse,
                inputs=[video_output],
                outputs=[
                    decision_output,
                    ball_speed_output,
                    impact_frame_output,
                    annotated_video_output,
                    trajectory_plot_output,
                ],
            )

    return demo


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