import gradio as gr
import gradio.themes as Soft
import pixeltable as pxt
from pixeltable.iterators import FrameIterator
from pixeltable.ext.functions.yolox import yolox
import PIL.Image
import PIL.ImageDraw

# Creating a UDF to draw bounding boxes

@pxt.udf
def draw_boxes(
    img: PIL.Image.Image, boxes: list[list[float]]
) -> PIL.Image.Image:
    result = img.copy()  # Create a copy of `img`
    d = PIL.ImageDraw.Draw(result)
    for box in boxes:
        # Draw bounding box rectangles on the copied image
        d.rectangle(box, width=3)
    return result

# Gradio Application
def process_video(video_file, model_id, threshold, progress=gr.Progress()):
    progress(0, desc="Initializing...")

    # Ensure a clean slate for the demo
    pxt.drop_dir('video_tutorial', force=True)
    pxt.create_dir('video_tutorial')

    # Create the `videos` table
    videos_table = pxt.create_table(
        'video_tutorial.videos',
        {'video': pxt.VideoType()}
    )

    # Create a view for video frames
    frames_view = pxt.create_view(
        'video_tutorial.frames',
        videos_table,
        iterator=FrameIterator.create(video=videos_table.video, fps=5)
    )
    
    # Insert video into Pixeltable table
    videos_table.insert([
        {
            'video': video_file.name
        }
    ])

    progress(0.3, desc="Running Model...")

    # Perform object detection
    frames_view[f'detect_{model_id}'] = yolox(
        frames_view.frame, model_id=model_id, threshold=threshold
    )

    progress(0.6, desc="Object detection completed...")
    
    # Prepare frame gallery
    frame_gallery = frames_view.select(frames_view.frame).where(frames_view.pos % 2 == 0).limit(10).collect()['frame']

    progress(0.8, desc="Outputs generated, retrieving video...")    

    # Generate output video with bounding boxes
    output_video = frames_view.group_by(videos_table).select(
        pxt.functions.video.make_video(
            frames_view.pos,
            draw_boxes(
                frames_view.frame,
                frames_view[f'detect_{model_id}'].bboxes
            )
        )
    ).collect()['col_0'][0]

    return output_video, frame_gallery

# Gradio interface
with gr.Blocks(theme=Soft) as demo:
    gr.Markdown(
        """
        <div max-width: 800px; margin: 0 auto;">
            <img src="https://raw.githubusercontent.com/pixeltable/pixeltable/main/docs/source/data/pixeltable-logo-large.png" alt="Pixeltable" style="max-width: 200px; margin-bottom: 20px;" />
            <h1 style="margin-bottom: 0.5em;">Object Detection in Videos</h1>
        </div>
        """
    )
    gr.HTML(
        """
        <p>
            <a href="https://github.com/pixeltable/pixeltable" target="_blank" style="color: #00A4EF; text-decoration: none; font-weight: bold;">Pixeltable</a> is a declarative interface for working with text, images, embeddings, and even video, enabling you to store, transform, index, and iterate on data.
        </p>
        """
    )

    # Add the disclaimer
    gr.HTML(
        """
        <div style="background-color: #000000; border: 1px solid #e9ecef; color: #FFFFFF; border-radius: 8px; padding: 15px; margin-bottom: 20px;">
            <strong style="color: #FFFFFF">Disclaimer:</strong> This app is best run on your own hardware with a GPU for optimal performance. This Hugging Face Space uses the free tier (2vCPU, 16GB RAM), which may result in slower processing times, especially for large video files. If you wish to use this app with your own hardware for improved performance, you can <a href="https://huggingface.co/spaces/Pixeltable/Multi-LLM-RAG-with-Groundtruth-Comparison/duplicate" target="_blank" style="color: #00A4EF; text-decoration: none; font-weight: bold;">duplicate this Hugging Face Space</a> and run it locally, or use Google Colab with the Free limited GPU support.
        </div>
        """
    )

    with gr.Row():
      with gr.Column():
          with gr.Accordion("What This Demo Does", open=True):
              gr.Markdown("""
                1. **Ingests Videos**: Uploads your Video.
                2. **Process and Retrieve Data**: Store, version, chunk, and retrieve video and frames.
                3. **Detects Objects**: Leverages Pixeltable's YOLOX integration to produce object detection results.
                4. **Visualizes Output**: Displays the processed video alongside a sample of the original frames.
                """)
      
      with gr.Column():
        gr.Examples(
          examples=[
              ["https://raw.github.com/pixeltable/pixeltable/release/docs/source/data/bangkok.mp4", "yolox_tiny", 0.25],
              ["https://raw.github.com/pixeltable/pixeltable/release/docs/source/data/bangkok.mp4", "yolox_m", 0.3],
          ],
          inputs=[video_file, model_id, threshold],
          outputs=[output_video, frame_gallery],
          fn=process_video,
      )

    # File upload components for ground truth and PDF documents
    with gr.Row():
        video_file = gr.File(label="Upload Video", file_count="single")

  # Add controls for chunking parameters
    with gr.Row():
          model_id = gr.Dropdown(
            choices=['yolox_tiny', 'yolox_m', 'yolox_x'],
            value='yolox_tiny',
            label="YOLOX Model"
      )
    
          threshold = gr.Slider(minimum=0.1, maximum=0.9, value=0.25, step=0.05, label="Threshold")

    # Button to trigger file processing
    process_button = gr.Button("Process Video")

    with gr.Row():
        output_video = gr.Video(label="Processed Video with Detections")
    
    with gr.Row():
        frame_gallery = gr.Gallery(label="Frame Gallery", show_label=True, elem_id="gallery")

    process_button.click(process_video,
                         inputs=[video_file,
                                 model_id,
                                 threshold],
                         outputs=[output_video, frame_gallery])

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