app.py

import gradio as gr
from video_processing import process_video, download_video, find_scenes, analyze_scenes, extract_best_scene, cleanup_temp_files
from gradio.themes.base import Base
from gradio.themes.utils import colors, fonts, sizes
from typing import Iterable
import uuid
import os
import matplotlib as plt

class CustomTheme(Base):
    def __init__(
        self,
        *,
        primary_hue: colors.Color | str = colors.orange,
        secondary_hue: colors.Color | str = colors.orange,
        neutral_hue: colors.Color | str = colors.gray,
        spacing_size: sizes.Size | str = sizes.spacing_md,
        radius_size: sizes.Size | str = sizes.radius_md,
        text_size: sizes.Size | str = sizes.text_md,
        font: fonts.Font | str | Iterable[fonts.Font | str] = (
            fonts.GoogleFont("Sora"),
            "ui-sans-serif",
            "sans-serif",
        ),
        font_mono: fonts.Font | str | Iterable[fonts.Font | str] = (
            fonts.GoogleFont("Sora"),
            "ui-monospace",
            "monospace",
        ),
    ):
        super().__init__(
            primary_hue=primary_hue,
            secondary_hue=secondary_hue,
            neutral_hue=neutral_hue,
            spacing_size=spacing_size,
            radius_size=radius_size,
            text_size=text_size,
            font=font,
            font_mono=font_mono,
        )
        super().set(
            body_background_fill="radial-gradient(circle at center, rgba(235, 87, 38, 1) 0%, rgba(235, 87, 38, 0) 70%), radial-gradient(#eb5726 1px, transparent 1px)",
            body_text_color="#282828",
            block_background_fill="#ffffff",
            block_title_text_color="#eb5726",
            block_label_text_color="#eb5726",
            button_primary_background_fill="#eb5726",
            button_primary_text_color="#ffffff",
        )

custom_theme = CustomTheme()

def save_uploaded_file(uploaded_file):
    upload_dir = "uploaded_videos"
    os.makedirs(upload_dir, exist_ok=True)
    file_path = os.path.join(upload_dir, f"{uuid.uuid4()}.mp4")
    with open(file_path, "wb") as f:
        f.write(uploaded_file)
    return file_path
    
def display_results(video_url, video_file, description):
    if video_url:
        video_path = download_video(video_url)
    elif video_file:
        video_path = save_uploaded_file(video_file)
    else:
        return "No video provided", None, None

    scenes = find_scenes(video_path)
    if not scenes:
        return "No scenes detected", None, None

    best_scene, sentiment_distribution = analyze_scenes(video_path, scenes, description)
    if best_scene:
        final_clip = extract_best_scene(video_path, best_scene)
        if final_clip:
            output_dir = "output"
            os.makedirs(output_dir, exist_ok=True)
            final_clip_path = os.path.join(output_dir, f"{uuid.uuid4()}_final_clip.mp4")
            final_clip.write_videofile(final_clip_path, codec='libx264', audio_codec='aac')
            cleanup_temp_files()

            # Create the radial plot using sentiment_distribution
            if sentiment_distribution:
                plot = create_radial_plot(sentiment_distribution)
                return final_clip_path, plot
            else:
                return final_clip_path, "No sentiment data available"
        else:
            return "No matching scene found", None
    else:
        return "No suitable scenes found", None

        

# Custom CSS for additional styling
css = """
body {
    background-color: #ffffff;
    background-image: radial-gradient(#eb5726 1px, transparent 1px);
    background-size: 10px 10px;
    background-repeat: repeat;
    background-attachment: fixed;
}
#video_url {
    background-color: #ffffff;
    color: #282828;
    border: 2px solid #eb5726;
}
#description {
    background-color: #ffffff;
    color: #282828;
    border: 2px solid #eb5726;
}
#submit_button {
    background-color: #eb5726;
    color: #ffffff;
    border: 2px solid #ffffff;
}
#submit_button:hover {
    background-color: #f5986e;
    color: #ffffff;
    border: 2px solid #ffffff;
}
label[for="video_url"] {
    color: #eb5726 !important;
}
label[for="description"] {
    color: #eb5726 !important;
}
h3 {
    color: #eb5726;
}
.centered-markdown {
    text-align: center;
    background-color: #ffffff;
    padding: 10px;
}
#sickstadium-title {
    font-size: 3em !important;
    font-weight: bold;
    text-transform: uppercase;
}
"""

def save_uploaded_file(uploaded_file):
    upload_dir = "uploaded_videos"
    os.makedirs(upload_dir, exist_ok=True)
    file_path = os.path.join(upload_dir, f"{uuid.uuid4()}.mp4")
    with open(file_path, "wb") as f:
        f.write(uploaded_file)
    return file_path

import matplotlib.pyplot as plt


def create_radial_plot(sentiments):
    labels = list(sentiments.keys())
    stats = list(sentiments.values())
    num_vars = len(labels)

    angles = np.linspace(0, 2 * np.pi, num_vars, endpoint=False).tolist()
    stats += stats[:1]
    angles += angles[:1]

    fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(polar=True))
    ax.fill(angles, stats, color='red', alpha=0.25)
    ax.plot(angles, stats, color='red', linewidth=2)
    ax.set_yticklabels([])
    ax.set_xticks(angles[:-1])
    ax.set_xticklabels(labels)

    plt.show()

    return fig

with gr.Blocks(theme=custom_theme, css=css) as demo:
    with gr.Column():
        gr.Markdown("# **Sickstadium AI**", elem_classes="centered-markdown", elem_id="sickstadium-title")
        gr.Markdown("### Upload your videos. Find sick clips. Tell your truth.", elem_classes="centered-markdown")
        gr.Markdown("**Welcome to Sickstadium AI. Our goal is to empower content creators with the ability to tell their stories without the friction of traditional video editing software. Skip the timeline, and don't worry about your video editing skills. Upload your video, describe the clip you want, and let our AI video editor do the work for you. Get more info about the Sickstadium project at [Strongholdlabs.io](https://strongholdlabs.io/)**", elem_classes="centered-markdown")
        video_url = gr.Textbox(label="Video URL:", elem_id="video_url")
        video_file = gr.File(label="Upload Video File:", interactive=True, file_types=["video"], type="binary")
        description = gr.Textbox(label="Describe your clip:", elem_id="description")
        submit_button = gr.Button("Process Video", elem_id="submit_button")
        video_output = gr.Video(label="Processed Video", elem_id="video_output")
        download_output = gr.File(label="Download Processed Video", elem_id="download_output", type="file")  # Define this here
        sentiment_plot = gr.Plot(label="Sentiment Distribution", elem_id="sentiment_plot")  # Adding elem_id for clarity

        submit_button.click(
            fn=display_results,
            inputs=[video_url, video_file, description],
            outputs=[video_output, download_output, sentiment_plot]
        )

demo.launch()