import gradio as gr
import time
from video_processing import process_video
from PIL import Image
import matplotlib
matplotlib.rcParams['figure.dpi'] = 500
matplotlib.rcParams['savefig.dpi'] = 500

def process_and_show_completion(video_input_path, anomaly_threshold_input, fps, progress=gr.Progress()):
    try:
        print("Starting video processing...")
        results = process_video(video_input_path, anomaly_threshold_input, fps, progress=progress)
        print("Video processing completed.")

        if isinstance(results[0], str) and results[0].startswith("Error"):
            print(f"Error occurred: {results[0]}")
            return [results[0]] + [None] * 18

        exec_time, results_summary, df, mse_embeddings, mse_posture, \
            mse_plot_embeddings, mse_histogram_embeddings, \
            mse_plot_posture, mse_histogram_posture, \
            mse_heatmap_embeddings, mse_heatmap_posture, \
            face_samples_frequent, face_samples_other, \
            anomaly_faces_embeddings, anomaly_frames_posture_images, \
            aligned_faces_folder, frames_folder = results

        anomaly_faces_embeddings_pil = [Image.fromarray(face) for face in anomaly_faces_embeddings]
        anomaly_frames_posture_pil = [Image.fromarray(frame) for frame in anomaly_frames_posture_images]

        face_samples_frequent = [Image.open(path) for path in face_samples_frequent]
        face_samples_other = [Image.open(path) for path in face_samples_other]

        output = [
            exec_time, results_summary,
            df, mse_embeddings, mse_posture,
            mse_plot_embeddings, mse_plot_posture,
            mse_histogram_embeddings, mse_histogram_posture,
            mse_heatmap_embeddings, mse_heatmap_posture,
            anomaly_faces_embeddings_pil, anomaly_frames_posture_pil,
            face_samples_frequent, face_samples_other,
            aligned_faces_folder, frames_folder,
            mse_embeddings, mse_posture,
        ]

        return output

    except Exception as e:
        error_message = f"An error occurred: {str(e)}"
        print(error_message)
        import traceback
        traceback.print_exc()
        return [error_message] + [None] * 18

with gr.Blocks() as iface:
    gr.Markdown("""
    # Facial Expression and Body Language Anomaly Detection

    This application analyzes videos to detect anomalies in facial features and body language. 
    It processes the video frames to extract facial embeddings and body posture, 
    then uses machine learning techniques to identify unusual patterns or deviations from the norm.

    For more information, visit: [https://github.com/reab5555/Facial-Expression-Anomaly-Detection](https://github.com/reab5555/Facial-Expression-Anomaly-Detection)
    """)

    with gr.Row():
        video_input = gr.Video()

    anomaly_threshold = gr.Slider(minimum=1, maximum=5, step=0.1, value=3, label="Anomaly Detection Threshold")
    fps_slider = gr.Slider(minimum=5, maximum=20, step=1, value=10, label="Frames Per Second")
    process_btn = gr.Button("Detect Anomalies")
    progress_bar = gr.Progress()
    execution_time = gr.Number(label="Execution Time (seconds)")

    with gr.Group(visible=False) as results_group:
        results_text = gr.TextArea(label="Anomaly Detection Results", lines=4)

        with gr.Tab("Facial Features"):
            mse_features_plot = gr.Plot(label="MSE: Facial Features")
            mse_features_hist = gr.Plot(label="MSE Distribution: Facial Features")
            mse_features_heatmap = gr.Plot(label="MSE Heatmap: Facial Features")
            anomaly_frames_features = gr.Gallery(label="Anomaly Frames (Facial Features)", columns=6, rows=2, height="auto")

        with gr.Tab("Body Posture"):
            mse_posture_plot = gr.Plot(label="MSE: Body Posture")
            mse_posture_hist = gr.Plot(label="MSE Distribution: Body Posture")
            mse_posture_heatmap = gr.Plot(label="MSE Heatmap: Body Posture")
            anomaly_frames_posture = gr.Gallery(label="Anomaly Frames (Body Posture)", columns=6, rows=2, height="auto")

        with gr.Tab("Face Samples"):
            face_samples_most_frequent = gr.Gallery(label="Most Frequent Person Samples (Target)", columns=6, rows=2, height="auto")
            face_samples_others = gr.Gallery(label="Other Persons Samples", columns=6, rows=1, height="auto")

    df_store = gr.State()
    mse_features_store = gr.State()
    mse_posture_store = gr.State()
    aligned_faces_folder_store = gr.State()
    frames_folder_store = gr.State()
    mse_heatmap_embeddings_store = gr.State()
    mse_heatmap_posture_store = gr.State()

    process_btn.click(
        process_and_show_completion,
        inputs=[video_input, anomaly_threshold, fps_slider],
        outputs=[
            execution_time, results_text, df_store,
            mse_features_store, mse_posture_store,
            mse_features_plot, mse_posture_plot,
            mse_features_hist, mse_posture_hist,
            mse_features_heatmap, mse_posture_heatmap,
            anomaly_frames_features, anomaly_frames_posture,
            face_samples_most_frequent, face_samples_others,
            aligned_faces_folder_store, frames_folder_store,
            mse_heatmap_embeddings_store, mse_heatmap_posture_store
        ]
    ).then(
        lambda: gr.Group(visible=True),
        inputs=None,
        outputs=[results_group]
    )

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