import argparse
import sys
import cv2
import numpy as np
from rich.console import Console
from rich.panel import Panel
from rich.align import Align
from rich.layout import Layout
from pyfiglet import Figlet
import mediapipe as mp
from PoseClassification.pose_embedding import FullBodyPoseEmbedding
from PoseClassification.pose_classifier import PoseClassifier
from PoseClassification.utils import EMADictSmoothing
from PoseClassification.visualize import PoseClassificationVisualizer

# For cross-platform compatibility
try:
    import msvcrt  # Windows
except ImportError:
    import termios  # Unix-like
    import tty


def getch():
    if sys.platform == "win32":
        return msvcrt.getch().decode("utf-8")
    else:
        fd = sys.stdin.fileno()
        old_settings = termios.tcgetattr(fd)
        try:
            tty.setraw(sys.stdin.fileno())
            ch = sys.stdin.read(1)
        finally:
            termios.tcsetattr(fd, termios.TCSADRAIN, old_settings)
        return ch


def create_ascii_title(text):
    f = Figlet(font="isometric2")
    return f.renderText(text)


def main(input_source, display=False, output_file=None):
    console = Console()
    layout = Layout()

    # Create ASCII title
    ascii_title = create_ascii_title("YOGAI")

    # Create the layout
    layout.split(
        Layout(Panel(Align.center(ascii_title), border_style="bold blue"), size=15),
        Layout(name="main"),
    )

    is_live = input_source == "live"
    if is_live:
        layout["main"].update(
            Panel(
                "Processing live video from camera",
                title="Video Classification",
                border_style="bold blue",
            )
        )
    else:
        layout["main"].update(
            Panel(
                f"Processing video: {input_source}",
                title="Video Classification",
                border_style="bold blue",
            )
        )

    console.print(layout)

    # Initialize pose tracker, embedder, and classifier
    mp_pose = mp.solutions.pose
    pose_tracker = mp_pose.Pose()
    pose_embedder = FullBodyPoseEmbedding()
    pose_classifier = PoseClassifier(
        pose_samples_folder="data/yoga_poses_csvs_out",
        pose_embedder=pose_embedder,
        top_n_by_max_distance=30,
        top_n_by_mean_distance=10,
    )
    pose_classification_filter = EMADictSmoothing(window_size=10, alpha=0.2)

    # Open the video source
    if is_live:
        video = cv2.VideoCapture(0)
        fps = 30  # Assume 30 fps for live video
        total_frames = float("inf")  # Infinite frames for live video
    else:
        video = cv2.VideoCapture(input_source)
        fps = video.get(cv2.CAP_PROP_FPS)
        total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))

    # Initialize pose timings (use lowercase for keys)
    pose_timings = {
        "chair": 0,
        "cobra": 0,
        "dog": 0,
        "plank": 0,
        "goddess": 0,
        "tree": 0,
        "warrior": 0,
        "no pose detected": 0,
        "fallen": 0,
    }

    frame_count = 0
    while True:
        ret, frame = video.read()
        if not ret:
            if is_live:
                console.print(
                    "[bold red]Error reading from camera. Exiting...[/bold red]"
                )
            break

        # Process the frame
        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        result = pose_tracker.process(image=frame_rgb)

        if result.pose_landmarks is not None:
            # Draw landmarks on the frame
            mp.solutions.drawing_utils.draw_landmarks(
                frame, result.pose_landmarks, mp_pose.POSE_CONNECTIONS
            )

            frame_height, frame_width = frame.shape[0], frame.shape[1]
            pose_landmarks = np.array(
                [
                    [lmk.x * frame_width, lmk.y * frame_height, lmk.z * frame_width]
                    for lmk in result.pose_landmarks.landmark
                ],
                dtype=np.float32,
            )

            # Classify the pose
            pose_classification = pose_classifier(pose_landmarks)
            pose_classification_filtered = pose_classification_filter(
                pose_classification
            )

            # Update pose timings (only for the pose with highest confidence)
            max_pose = max(
                pose_classification_filtered, key=pose_classification_filtered.get
            ).lower()
            pose_timings[max_pose] += 1 / fps
        else:
            pose_timings["no pose detected"] += 1 / fps

        frame_count += 1
        if frame_count % 30 == 0:  # Update every 30 frames
            panel_content = (
                f"[bold]Chair:[/bold] {pose_timings['chair']:.2f}s\n"
                f"[bold]Cobra:[/bold] {pose_timings['cobra']:.2f}s\n"
                f"[bold]Dog:[/bold] {pose_timings['dog']:.2f}s\n"
                f"[bold]Plank:[/bold] {pose_timings['plank']:.2f}s\n"
                f"[bold]Goddess:[/bold] {pose_timings['goddess']:.2f}s\n"
                f"[bold]Tree:[/bold] {pose_timings['tree']:.2f}s\n"
                f"[bold]Warrior:[/bold] {pose_timings['warrior']:.2f}s\n"
                f"---\n"
                f"[bold]No pose detected:[/bold] {pose_timings['no pose detected']:.2f}s\n"
                f"[bold]Fallen:[/bold] {pose_timings['fallen']:.2f}s"
            )
            if not is_live:
                panel_content += f"\n\nProcessed {frame_count}/{total_frames} frames"

            layout["main"].update(
                Panel(
                    panel_content,
                    title="Classification Results",
                    border_style="bold green",
                )
            )
            console.print(layout)

        if display:
            cv2.imshow("Video", frame)
            if cv2.waitKey(1) & 0xFF == ord("q"):
                break

    video.release()
    if display:
        cv2.destroyAllWindows()

    # Final results
    final_panel_content = (
        f"[bold]Chair:[/bold] {pose_timings['chair']:.2f}s\n"
        f"[bold]Cobra:[/bold] {pose_timings['cobra']:.2f}s\n"
        f"[bold]Dog:[/bold] {pose_timings['dog']:.2f}s\n"
        f"[bold]Plank:[/bold] {pose_timings['plank']:.2f}s\n"
        f"[bold]Goddess:[/bold] {pose_timings['goddess']:.2f}s\n"
        f"[bold]Tree:[/bold] {pose_timings['tree']:.2f}s\n"
        f"[bold]Warrior:[/bold] {pose_timings['warrior']:.2f}s\n"
        f"---\n"
        f"[bold]No pose detected:[/bold] {pose_timings['no pose detected']:.2f}s\n"
        f"[bold]Fallen:[/bold] {pose_timings['fallen']:.2f}s"
    )
    layout["main"].update(
        Panel(
            final_panel_content,
            title="Final Classification Results",
            border_style="bold green",
        )
    )
    console.print(layout)

    if output_file:
        console.print(f"[green]Output saved to: {output_file}[/green]")


if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description="Classify poses in a video file or from live camera."
    )
    parser.add_argument("input", help="Input video file or 'live' for camera feed")
    parser.add_argument(
        "--display", action="store_true", help="Display the video with detected poses"
    )
    parser.add_argument("--output", help="Output video file")

    if len(sys.argv) == 1:
        parser.print_help(sys.stderr)
        sys.exit(1)

    args = parser.parse_args()

    main(args.input, args.display, args.output)