Update app.py

app.py

@@ -1,11 +1,11 @@
 import gradio as gr
-from video_processing import process_video, download_video, find_scenes, analyze_scenes, extract_best_scene, cleanup_temp_files
+from video_processing import process_video
 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
+import matplotlib.pyplot as plt
+import numpy as np
 
 class CustomTheme(Base):
     def __init__(
@@ -50,49 +50,6 @@ class CustomTheme(Base):
 
 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 {
@@ -122,10 +79,7 @@ body {
     color: #ffffff;
     border: 2px solid #ffffff;
 }
-label[for="video_url"] {
-    color: #eb5726 !important;
-}
-label[for="description"] {
+label[for="video_url"], label[for="description"], label[for="video_file"] {
     color: #eb5726 !important;
 }
 h3 {
@@ -143,50 +97,20 @@ h3 {
 }
 """
 
-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
+def display_results(video_url, video_file, description):
+    video_path = process_video(video_url or video_file, description)
+    return video_path, video_path  # This should be updated based on actual function output
 
 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")
+        video_file = gr.File(label="Upload Video File:", type="binary", interactive=True, file_types=["video"], elem_id="video_file")
         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="binary")  # Define this here
-        sentiment_plot = gr.Plot(label="Sentiment Distribution", elem_id="sentiment_plot")  # Adding elem_id for clarity
-
+        download_output = gr.File(label="Download Processed Video", elem_id="download_output")
+        sentiment_plot = gr.Plot(label="Sentiment Distribution", elem_id="sentiment_plot")
         submit_button.click(
             fn=display_results,
             inputs=[video_url, video_file, description],