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app.py

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+"""Video Processing Space - Gradio Interface for AI Video Analysis."""
+
+import gradio as gr
+import cv2
+import numpy as np
+import json
+import tempfile
+import os
+
+
+def get_metadata(video_file):
+    """Extract video metadata."""
+    if video_file is None:
+        return "No video uploaded"
+    
+    cap = cv2.VideoCapture(video_file)
+    
+    if not cap.isOpened():
+        return "Error: Could not open video"
+    
+    metadata = {
+        "frame_count": int(cap.get(cv2.CAP_PROP_FRAME_COUNT)),
+        "fps": round(cap.get(cv2.CAP_PROP_FPS), 2),
+        "width": int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
+        "height": int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)),
+    }
+    
+    if metadata["fps"] > 0:
+        metadata["duration_sec"] = round(metadata["frame_count"] / metadata["fps"], 2)
+    else:
+        metadata["duration_sec"] = 0
+    
+    codec_int = int(cap.get(cv2.CAP_PROP_FOURCC))
+    metadata["codec"] = "".join([chr((codec_int >> 8 * i) & 0xFF) for i in range(4)])
+    
+    cap.release()
+    return json.dumps(metadata, indent=2)
+
+
+def create_contact_sheet(video_file, grid_size):
+    """Generate contact sheet grid from video."""
+    if video_file is None:
+        return None, "No video uploaded"
+    
+    grid_size = int(grid_size)
+    cap = cv2.VideoCapture(video_file)
+    
+    if not cap.isOpened():
+        return None, "Error: Could not open video"
+    
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    duration = total_frames / fps if fps > 0 else 0
+    
+    total_cells = grid_size * grid_size
+    step = max(1, total_frames // total_cells)
+    
+    thumb_width = 200
+    thumb_height = int(height * (thumb_width / width))
+    
+    frames = []
+    
+    for i in range(total_cells):
+        frame_idx = i * step
+        cap.set(cv2.CAP_PROP_POS_FRAMES, frame_idx)
+        ret, frame = cap.read()
+        
+        if ret:
+            thumb = cv2.resize(frame, (thumb_width, thumb_height))
+            cv2.putText(thumb, str(i), (5, 20), 
+                       cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
+            frames.append(thumb)
+        else:
+            blank = np.zeros((thumb_height, thumb_width, 3), dtype=np.uint8)
+            frames.append(blank)
+    
+    cap.release()
+    
+    rows = []
+    for r in range(grid_size):
+        start_idx = r * grid_size
+        end_idx = start_idx + grid_size
+        row_frames = frames[start_idx:end_idx]
+        if row_frames:
+            rows.append(np.hstack(row_frames))
+    
+    if rows:
+        grid_image = np.vstack(rows)
+        grid_image_rgb = cv2.cvtColor(grid_image, cv2.COLOR_BGR2RGB)
+        
+        info = {
+            "grid_size": grid_size,
+            "total_cells": total_cells,
+            "video_duration_sec": round(duration, 2),
+            "seconds_per_cell": round(duration / total_cells, 2),
+        }
+        
+        return grid_image_rgb, json.dumps(info, indent=2)
+    
+    return None, "Error: Could not generate contact sheet"
+
+
+def extract_clip(video_file, start_sec, end_sec):
+    """Extract video segment by timestamps."""
+    if video_file is None:
+        return None, "No video uploaded"
+    
+    cap = cv2.VideoCapture(video_file)
+    
+    if not cap.isOpened():
+        return None, "Error: Could not open video"
+    
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    duration = total_frames / fps if fps > 0 else 0
+    
+    if start_sec < 0:
+        start_sec = 0
+    if end_sec > duration:
+        end_sec = duration
+    if start_sec >= end_sec:
+        cap.release()
+        return None, f"Invalid time range: {start_sec} to {end_sec}"
+    
+    start_frame = int(start_sec * fps)
+    end_frame = int(end_sec * fps)
+    
+    # Create temp output file
+    output_path = tempfile.mktemp(suffix='.mp4')
+    
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
+    
+    cap.set(cv2.CAP_PROP_POS_FRAMES, start_frame)
+    
+    frames_written = 0
+    current_frame = start_frame
+    
+    while current_frame < end_frame:
+        ret, frame = cap.read()
+        if not ret:
+            break
+        out.write(frame)
+        frames_written += 1
+        current_frame += 1
+    
+    cap.release()
+    out.release()
+    
+    info = {
+        "start_sec": start_sec,
+        "end_sec": end_sec,
+        "duration_sec": round(end_sec - start_sec, 2),
+        "frames_written": frames_written,
+    }
+    
+    return output_path, json.dumps(info, indent=2)
+
+
+# Gradio Interface
+with gr.Blocks(title="Video Process - AI Video Analysis", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # 🎬 Video Process
+    **AI-powered video analysis and editing**
+    
+    Upload a video to inspect, analyze, or extract clips.
+    """)
+    
+    video_input = gr.Video(label="Upload Video")
+    
+    with gr.Tabs():
+        # Tab 1: Metadata
+        with gr.TabItem("📊 Metadata"):
+            meta_btn = gr.Button("Get Metadata", variant="primary")
+            meta_output = gr.Code(language="json", label="Video Properties")
+            meta_btn.click(get_metadata, inputs=video_input, outputs=meta_output)
+        
+        # Tab 2: Contact Sheet
+        with gr.TabItem("👁️ Inspect (Contact Sheet)"):
+            gr.Markdown("Generate a visual grid to 'see' the entire video at once")
+            grid_slider = gr.Slider(minimum=2, maximum=10, value=6, step=1, label="Grid Size")
+            inspect_btn = gr.Button("Generate Contact Sheet", variant="primary")
+            contact_sheet_output = gr.Image(label="Contact Sheet")
+            inspect_info = gr.Code(language="json", label="Info")
+            inspect_btn.click(create_contact_sheet, 
+                            inputs=[video_input, grid_slider], 
+                            outputs=[contact_sheet_output, inspect_info])
+        
+        # Tab 3: Extract Clip
+        with gr.TabItem("✂️ Extract Clip"):
+            gr.Markdown("Cut a segment from the video by timestamps")
+            with gr.Row():
+                start_input = gr.Number(value=0, label="Start (seconds)")
+                end_input = gr.Number(value=5, label="End (seconds)")
+            extract_btn = gr.Button("Extract Clip", variant="primary")
+            clip_output = gr.Video(label="Extracted Clip")
+            extract_info = gr.Code(language="json", label="Info")
+            extract_btn.click(extract_clip,
+                            inputs=[video_input, start_input, end_input],
+                            outputs=[clip_output, extract_info])
+    
+    gr.Markdown("""
+    ---
+    *Built with OpenCV + Gradio | VAM-Seek inspired contact sheet approach*
+    """)
+
+
+if __name__ == "__main__":
+    demo.launch()