Cleanup: Remove video_analyser and unused downloader

src/video_downloader.py

@@ -1,724 +0,0 @@
-"""
-Video Downloader Utility
-Download videos by filename from Google Drive
-"""
-
-import os
-import csv
-from pathlib import Path
-import sys
-import re
-from typing import Optional, List, Dict
-import shutil
-from concurrent.futures import ThreadPoolExecutor, as_completed
-import threading
-
-# Try to import logger from utils, fallback to print
-try:
-    from utils import logger
-except ImportError:
-    class SimpleLogger:
-        @staticmethod
-        def info(msg): print(f"INFO: {msg}")
-        @staticmethod
-        def error(msg): print(f"ERROR: {msg}")
-        @staticmethod
-        def warning(msg): print(f"WARNING: {msg}")
-    logger = SimpleLogger()
-
-
-class VideoDownloader:
-    def __init__(self, csv_path: Optional[str] = None, config_path: Optional[str] = None):
-        """
-        Initialize VideoDownloader
-        
-        Args:
-            csv_path: Path to CSV file containing video library
-                     Defaults to infloxa_video_analysis.csv
-            config_path: Path to config.yaml for DriveDownloader
-                        Defaults to video_analyser/config.yaml
-        """
-        if csv_path is None:
-            csv_path = "video_analyser/infloxa_video_analysis.csv"
-        
-        if config_path is None:
-            config_path = "video_analyser/config.yaml"
-        
-        self.csv_path = csv_path
-        self.config_path = config_path
-        self.video_library = self._load_video_library()
-        self.drive_downloader = None
-        self.lock = threading.Lock()  # Thread-safe lock for drive operations
-    
-    def _load_video_library(self) -> List[Dict]:
-        """Load video library from CSV file"""
-        try:
-            if not os.path.exists(self.csv_path):
-                logger.error(f"CSV file not found: {self.csv_path}")
-                return []
-            
-            videos = []
-            with open(self.csv_path, 'r', encoding='utf-8') as f:
-                reader = csv.DictReader(f)
-                for row in reader:
-                    videos.append(row)
-            
-            logger.info(f"Loaded video library with {len(videos)} entries from {self.csv_path}")
-            return videos
-        
-        except Exception as e:
-            logger.error(f"Failed to load video library: {e}")
-            raise
-    
-    def _init_drive_downloader(self, download_path: str):
-        """Initialize Google Drive downloader"""
-        if self.drive_downloader is not None:
-            return
-        
-        try:
-            # Add video_analyser to path
-            video_analyser_path = os.path.join(os.path.dirname(__file__), '..', 'video_analyser')
-            if video_analyser_path not in sys.path:
-                sys.path.insert(0, video_analyser_path)
-            
-            from modules import DriveDownloader
-            import yaml
-            
-            if not os.path.exists(self.config_path):
-                logger.error(f"Config file not found: {self.config_path}")
-                raise FileNotFoundError(f"Config file not found: {self.config_path}")
-            
-            with open(self.config_path, 'r') as f:
-                self.config = yaml.safe_load(f)
-            
-            # Override the local directory with our download path
-            self.config['output']['local_video_dir'] = download_path
-            
-            # Initialize and authenticate
-            logger.info("Initializing Google Drive connection...")
-            self.drive_downloader = DriveDownloader(self.config)
-            self.drive_downloader.authenticate()
-            logger.info("✓ Google Drive authenticated")
-            
-        except Exception as e:
-            logger.error(f"Failed to initialize DriveDownloader: {e}")
-            raise
-    
-    def _get_thread_service(self):
-        """Get a thread-local Google Drive service instance"""
-        import threading
-        thread_id = threading.get_ident()
-        
-        # Check if this thread already has a service
-        if not hasattr(self, '_thread_services'):
-            self._thread_services = {}
-        
-        if thread_id not in self._thread_services:
-            # Create a new service for this thread
-            from modules import DriveDownloader
-            import yaml
-            
-            with open(self.config_path, 'r') as f:
-                config = yaml.safe_load(f)
-            
-            downloader = DriveDownloader(config)
-            downloader.authenticate()
-            self._thread_services[thread_id] = downloader.service
-            logger.info(f"Created new Drive service for thread {thread_id}")
-        
-        return self._thread_services[thread_id]
-
-    def _extract_folder_id_from_link(self, drive_link: str) -> Optional[str]:
-        """
-        Extract folder ID from Google Drive link
-        
-        Args:
-            drive_link: Google Drive folder URL
-            
-        Returns:
-            Folder ID or None if not found
-        """
-        patterns = [
-            r'folders/([a-zA-Z0-9_-]+)',
-            r'id=([a-zA-Z0-9_-]+)',
-        ]
-        
-        for pattern in patterns:
-            match = re.search(pattern, drive_link)
-            if match:
-                return match.group(1)
-        
-        logger.error(f"Could not extract folder ID from link: {drive_link}")
-        return None
-
-    def _list_folder_contents_recursive(self, folder_id: str, parent_path: str = "") -> List[Dict]:
-        """
-        Recursively list all files in a folder and its subfolders
-        
-        Args:
-            folder_id: Google Drive folder ID
-            parent_path: Path of parent folder for tracking structure
-            
-        Returns:
-            List of dictionaries with file info including relative path
-        """
-        try:
-            files_and_folders = []
-            
-            # Query for all items in this folder
-            query = f"'{folder_id}' in parents and trashed=false"
-            results = self.drive_downloader.service.files().list(
-                q=query,
-                fields="files(id, name, mimeType, webViewLink)",
-                pageSize=1000
-            ).execute()
-            
-            items = results.get('files', [])
-            
-            for item in items:
-                item_name = item['name']
-                item_id = item['id']
-                mime_type = item['mimeType']
-                
-                # Build current path
-                current_path = os.path.join(parent_path, item_name) if parent_path else item_name
-                
-                if mime_type == 'application/vnd.google-apps.folder':
-                    # It's a folder - recurse into it
-                    logger.info(f"Scanning folder: {current_path}")
-                    subfolder_contents = self._list_folder_contents_recursive(item_id, current_path)
-                    files_and_folders.extend(subfolder_contents)
-                else:
-                    # It's a file
-                    files_and_folders.append({
-                        'id': item_id,
-                        'name': item_name,
-                        'path': current_path,
-                        'mimeType': mime_type,
-                        'webViewLink': item.get('webViewLink', '')
-                    })
-            
-            return files_and_folders
-            
-        except Exception as e:
-            logger.error(f"Error listing folder contents: {e}")
-            return []
-
-    def _download_single_file(
-        self,
-        file_info: Dict,
-        download_root: str,
-        idx: int,
-        total: int
-    ) -> Dict[str, any]:
-        """
-        Download a single file from Google Drive (for parallel execution)
-        
-        Args:
-            file_info: Dictionary with file information
-            download_root: Root directory for downloads
-            idx: Current file index
-            total: Total number of files
-            
-        Returns:
-            Dictionary with download result
-        """
-        result = {
-            'file': file_info['name'],
-            'status': 'unknown',
-            'path': None,
-            'error': None
-        }
-        
-        try:
-            # Build local path preserving folder structure
-            relative_path = file_info['path']
-            local_path = os.path.join(download_root, relative_path)
-            local_dir = os.path.dirname(local_path)
-            
-            # Check if file already exists
-            if os.path.exists(local_path):
-                logger.info(f"[{idx}/{total}] Skipped (exists): {relative_path}")
-                result['status'] = 'skipped'
-                result['path'] = local_path
-                return result
-            
-            # Create directory structure BEFORE downloading
-            os.makedirs(local_dir, exist_ok=True)
-            logger.info(f"[{idx}/{total}] Downloading: {relative_path}")
-            
-            # Get thread-local service instance
-            service = self._get_thread_service()
-            
-            # Download file DIRECTLY to the final destination
-            request = service.files().get_media(fileId=file_info['id'])
-            
-            with open(local_path, 'wb') as f:
-                from googleapiclient.http import MediaIoBaseDownload
-                downloader = MediaIoBaseDownload(f, request)
-                done = False
-                last_progress = 0
-                while not done:
-                    status, done = downloader.next_chunk()
-                    if status:
-                        progress = int(status.progress() * 100)
-                        # Log every 25% to avoid spam
-                        if progress >= last_progress + 25:
-                            logger.info(f"  [{file_info['name']}] Progress: {progress}%")
-                            last_progress = progress
-            
-            logger.info(f"✓ Successfully downloaded: {local_path}")
-            result['status'] = 'downloaded'
-            result['path'] = local_path
-            
-        except Exception as e:
-            logger.error(f"Failed to download {file_info['name']}: {e}")
-            result['status'] = 'failed'
-            result['error'] = str(e)
-            
-            # Clean up partial download if it exists
-            if 'local_path' in locals() and os.path.exists(local_path):
-                try:
-                    os.remove(local_path)
-                except:
-                    pass
-        
-        return result
-
-    def download_from_drive_link(
-        self, 
-        drive_link: str, 
-        download_root: str,
-        file_extensions: Optional[List[str]] = None,
-        max_workers: int = 10  # Number of parallel downloads
-    ) -> Dict[str, any]:
-        """
-        Download all files from a Google Drive folder link, preserving folder structure
-        (with parallel downloads)
-        
-        Args:
-            drive_link: Google Drive folder URL
-                       e.g., https://drive.google.com/drive/folders/1WSrVAyqvPJzpRnoUxkNx0LqK9VlDs432
-            download_root: Root directory where files should be downloaded
-            file_extensions: Optional list of file extensions to filter (e.g., ['.mp4', '.avi'])
-                           If None, downloads all files
-            max_workers: Number of parallel downloads (default: 10)
-            
-        Returns:
-            Dictionary with download statistics:
-            {
-                'total_files': int,
-                'downloaded': int,
-                'skipped': int,
-                'failed': int,
-                'files': List[str]  # paths of downloaded files
-            }
-            
-        Example:
-            >>> downloader = VideoDownloader()
-            >>> result = downloader.download_from_drive_link(
-            ...     drive_link="https://drive.google.com/drive/folders/1WSrVAyqvPJzpRnoUxkNx0LqK9VlDs432",
-            ...     download_root="downloads/my_videos",
-            ...     file_extensions=['.mp4', '.mov', '.avi'],
-            ...     max_workers=10
-            ... )
-            >>> print(f"Downloaded {result['downloaded']} files")
-        """
-        try:
-            # Initialize Drive downloader (pass None to avoid auto path setup)
-            self._init_drive_downloader(None)
-            
-            # Extract folder ID from link
-            folder_id = self._extract_folder_id_from_link(drive_link)
-            if not folder_id:
-                return {
-                    'total_files': 0,
-                    'downloaded': 0,
-                    'skipped': 0,
-                    'failed': 0,
-                    'files': []
-                }
-            
-            logger.info(f"Scanning Google Drive folder: {folder_id}")
-            
-            # Get all files recursively
-            all_files = self._list_folder_contents_recursive(folder_id)
-            
-            # Filter by file extensions if provided
-            if file_extensions:
-                file_extensions = [ext.lower() if ext.startswith('.') else f'.{ext.lower()}' 
-                                 for ext in file_extensions]
-                all_files = [f for f in all_files 
-                           if any(f['name'].lower().endswith(ext) for ext in file_extensions)]
-            
-            logger.info(f"Found {len(all_files)} files to download")
-            
-            # Statistics
-            stats = {
-                'total_files': len(all_files),
-                'downloaded': 0,
-                'skipped': 0,
-                'failed': 0,
-                'files': []
-            }
-            
-            # Download files in parallel using ThreadPoolExecutor
-            logger.info(f"Starting parallel downloads with {max_workers} workers...")
-            
-            with ThreadPoolExecutor(max_workers=max_workers) as executor:
-                # Submit all download tasks
-                future_to_file = {
-                    executor.submit(
-                        self._download_single_file,
-                        file_info,
-                        download_root,
-                        idx,
-                        len(all_files)
-                    ): file_info
-                    for idx, file_info in enumerate(all_files, 1)
-                }
-                
-                # Collect results as they complete
-                for future in as_completed(future_to_file):
-                    file_info = future_to_file[future]
-                    try:
-                        result = future.result()
-                        
-                        if result['status'] == 'downloaded':
-                            stats['downloaded'] += 1
-                            if result['path']:
-                                stats['files'].append(result['path'])
-                        elif result['status'] == 'skipped':
-                            stats['skipped'] += 1
-                            if result['path']:
-                                stats['files'].append(result['path'])
-                        elif result['status'] == 'failed':
-                            stats['failed'] += 1
-                            
-                    except Exception as e:
-                        logger.error(f"Error processing {file_info['name']}: {e}")
-                        stats['failed'] += 1
-            
-            # Summary
-            logger.info("=" * 60)
-            logger.info("Download Summary:")
-            logger.info(f"  Total files: {stats['total_files']}")
-            logger.info(f"  Downloaded: {stats['downloaded']}")
-            logger.info(f"  Skipped (already exist): {stats['skipped']}")
-            logger.info(f"  Failed: {stats['failed']}")
-            logger.info("=" * 60)
-            
-            return stats
-            
-        except Exception as e:
-            logger.error(f"Error downloading from drive link: {e}")
-            import traceback
-            traceback.print_exc()
-            return {
-                'total_files': 0,
-                'downloaded': 0,
-                'skipped': 0,
-                'failed': 0,
-                'files': []
-            }
-
-    def get_folder_name(
-        self, 
-        video_filename: str,
-    ) -> str:
-        try:
-            # Initialize Drive downloader
-            self._init_drive_downloader(None)
-            
-            # List all videos from Google Drive
-            logger.info(f"Searching for '{video_filename}' in Google Drive...")
-            all_videos = self.drive_downloader.list_all_videos()
-            
-            # Find the video by filename
-            matching_video = None
-            for video_item in all_videos:
-                if video_item['name'] == video_filename:
-                    matching_video = video_item
-                    break
-            
-            if not matching_video:
-                return None
-            else:
-                return matching_video["path"].split("/")[0]
-        except: return None
-
-
-
-    def get_video_link(self, video_filename: str) -> Optional[str]:
-        """Fetches the Google Drive webViewLink for the file"""
-        try:
-            self._init_drive_downloader(None)
-            all_videos = self.drive_downloader.list_all_videos()
-            for video_item in all_videos:
-                if video_item['name'] == video_filename:
-                    # Return the webLink if available, otherwise construct one from ID
-                    return video_item.get('webViewLink') or f"https://drive.google.com/file/d/{video_item.get('id')}/view"
-            return None
-        except Exception as e:
-            logger.error(f"Error fetching link for {video_filename}: {e}")
-            return None
-
-    def download_video(
-        self, 
-        video_filename: str, 
-        download_path: str
-    ) -> bool:
-        """
-        Download a video from Google Drive by filename
-        
-        Args:
-            video_filename: Name of the video file to search for in Google Drive
-            download_path: Directory path where video should be downloaded
-            
-        Returns:
-            True if download successful, False otherwise
-            
-        Example:
-            >>> downloader = VideoDownloader()
-            >>> downloader.download_video(
-            ...     video_filename="Copy of SnapInsta.to_AQM-w-YmCepAaXIWl7Frs10cKbdX1P-__zSTTzh7j0td9hh6ebAJWA409L_fJNcgiWctukdKl0ubkT2tNOxgjyRdOXs3nPBgMAgWV90.mp4",
-            ...     download_path="downloads/videos"
-            ... )
-        """
-        try:
-            save_path  = os.path.join(download_path, video_filename)
-            if os.path.exists(save_path):
-                logger.info(f"File already exists: {save_path}")
-                return save_path
-            # Initialize Drive downloader
-            self._init_drive_downloader(download_path)
-            
-            # List all videos from Google Drive
-            logger.info(f"Searching for '{video_filename}' in Google Drive...")
-            all_videos = self.drive_downloader.list_all_videos()
-            
-            # Find the video by filename
-            matching_video = None
-            for video_item in all_videos:
-                if video_item['name'] == video_filename:
-                    matching_video = video_item
-                    break
-            
-            if not matching_video:
-                logger.error(f"Video not found in Google Drive: {video_filename}")
-                logger.info(f"Searched {len(all_videos)} videos in Drive")
-                
-                # Show similar filenames for debugging
-                similar = [v['name'] for v in all_videos if video_filename[:30] in v['name']]
-                if similar:
-                    logger.info(f"Similar files found: {similar[:3]}")
-                return None
-            else:
-                logger.info(f"Found video: {matching_video}")
-            # Create download directory
-            os.makedirs(download_path, exist_ok=True)
-            
-            # Download the video
-            logger.info(f"Downloading {video_filename}...")
-            local_path = self.drive_downloader.download_single_video(matching_video)
-            shutil.move(local_path, save_path)
-            
-            if save_path:
-                logger.info(f"✓ Successfully downloaded to: {save_path}")
-                return save_path
-            else:
-                logger.error(f"Failed to download {video_filename}")
-                return None
-            
-        except Exception as e:
-            logger.error(f"Error downloading video: {e}")
-            import traceback
-            traceback.print_exc()
-            return None
-    
-    def get_videos_by_category(self, category: str) -> List[Dict]:
-        """
-        Get all videos for a specific category
-        
-        Args:
-            category: Video category to filter by
-            
-        Returns:
-            List of dictionaries containing videos in that category
-        """
-        return [v for v in self.video_library if v.get('category') == category]
-    
-    def get_available_categories(self) -> List[str]:
-        """Get list of all available categories"""
-        categories = set(v.get('category', '') for v in self.video_library)
-        return sorted([c for c in categories if c])
-    
-    def search_videos(self, keyword: str) -> List[Dict]:
-        """
-        Search videos by keyword in filename or description
-        
-        Args:
-            keyword: Search term
-            
-        Returns:
-            List of dictionaries containing matching videos
-        """
-        keyword_lower = keyword.lower()
-        results = []
-        
-        for video in self.video_library:
-            filename = video.get('VIDEO_FILENAME', '').lower()
-            short_desc = video.get('short_description', '').lower()
-            desc = video.get('description', '').lower()
-            
-            if keyword_lower in filename or keyword_lower in short_desc or keyword_lower in desc:
-                results.append(video)
-        
-        return results
-
-
-def add_folder_name_column(input_csv, output_csv, downloader):
-    processed = set()
-
-    # Load already processed video filenames
-    if os.path.exists(output_csv):
-        with open(output_csv, newline="", encoding="utf-8") as f:
-            reader = csv.DictReader(f)
-            for row in reader:
-                processed.add(row["VIDEO_FILENAME"])
-
-    with open(input_csv, newline="", encoding="utf-8") as infile, \
-         open(output_csv, "a", newline="", encoding="utf-8") as outfile:
-
-        reader = csv.DictReader(infile)
-        writer = csv.writer(outfile)
-
-        # Write header only if file is new
-        if outfile.tell() == 0:
-            writer.writerow(["folder_name"] + reader.fieldnames)
-
-        for row in reader:
-            video_filename = row["VIDEO_FILENAME"]
-
-            # Skip if already processed
-            if video_filename in processed:
-                continue
-
-            folder_name = downloader.get_folder_name(video_filename)
-
-            # 🚫 Skip if folder_name is empty / None / whitespace
-            if not folder_name or not str(folder_name).strip():
-                continue
-
-            writer.writerow([folder_name] + list(row.values()))
-            outfile.flush()
-
-
-def add_link_column(input_csv, output_csv, downloader):
-    """Reads input_csv and writes to output_csv with an added 'video_link' column"""
-    processed = set()
-
-    # 1. Load already processed filenames to allow resuming
-    if os.path.exists(output_csv):
-        with open(output_csv, newline="", encoding="utf-8") as f:
-            reader = csv.DictReader(f)
-            if reader.fieldnames and "VIDEO_FILENAME" in reader.fieldnames:
-                for row in reader:
-                    processed.add(row["VIDEO_FILENAME"].split("/")[-1])
-
-    # 2. Process the files
-    with open(input_csv, newline="", encoding="utf-8") as infile:
-        reader = csv.DictReader(infile)
-        fieldnames = reader.fieldnames
-        
-        # Determine if we need to write the header
-        file_exists = os.path.exists(output_csv) and os.path.getsize(output_csv) > 0
-        
-        with open(output_csv, "a", newline="", encoding="utf-8") as outfile:
-            # We want 'video_link' to be the first column
-            writer = csv.DictWriter(outfile, fieldnames=["video_link"] + fieldnames)
-            
-            if not file_exists:
-                writer.writeheader()
-
-            for row in reader:
-                video_filename = row["VIDEO_FILENAME"].split("/")[-1]
-
-                if video_filename in processed:
-                    continue
-
-                logger.info(f"Fetching link for: {video_filename}")
-                video_link = downloader.get_video_link(video_filename)
-
-                if not video_link:
-                    logger.warning(f"Could not find link for {video_filename}")
-                    continue
-
-                # Prepare new row
-                new_row = {"video_link": video_link}
-                new_row.update(row)
-                
-                writer.writerow(new_row)
-                outfile.flush() # Ensure it saves frequently
-                processed.add(video_filename)
-
-
-# Example usage
-if __name__ == "__main__":
-    try:
-        from dotenv import load_dotenv
-        load_dotenv()
-
-        downloader = VideoDownloader()
-        downloader._init_drive_downloader(download_path="testData/video_for_workflow")
-        add_link_column("testData/infloxa_copy/videos.csv", "testData/infloxa_copy/videos_with_links.csv", downloader)
-        
-        # Download from Drive folder link
-        # result = downloader.download_from_drive_link(
-        #     drive_link="https://drive.google.com/drive/folders/1WSrVAyqvPJzpRnoUxkNx0LqK9VlDs432",
-        #     download_root="testData/video_for_workflow",
-        #     file_extensions=['.mp4', '.mov', '.avi', '.mkv']  # Only video files
-        # )
-        
-        # print(f"\nDownload completed!")
-        # print(f"Total: {result['total_files']}, Downloaded: {result['downloaded']}, "
-            #   f"Skipped: {result['skipped']}, Failed: {result['failed']}")
-
-        # paths = [
-        #     "testData/infloxa_copy/Infloxa_ Lifestyle_125videos",
-        #     "testData/infloxa_copy/Infloxa_LuxuryCars_125videos",
-        #     "testData/infloxa_copy/Infloxa_LuxuryItems_125videos",
-        #     "testData/infloxa_copy/Infloxa_LuxuryRealEstate_125videos",
-        #     "testData/infloxa_copy/Infloxa_Models_125videos",
-        #     "testData/infloxa_copy/Infloxa_PrivateJets_125videos",
-        #     "testData/infloxa_copy/Infloxa_Wealth&Exclusivity_125videos",
-        #     "testData/infloxa_copy/Infloxa_Yachts_125videos",
-        # ]
-
-        # output_csv = "testData/infloxa_copy/videos_with_links.csv"
-
-        # VIDEO_EXTENSIONS = {".mp4", ".mov", ".mkv", ".avi", ".webm"}
-
-        # rows = []
-
-        # for base_path in paths:
-        #     base_path = Path(base_path)
-
-        #     if not base_path.exists():
-        #         print(f"Skipping missing folder: {base_path}")
-        #         continue
-
-        #     for file in base_path.iterdir():
-        #         if file.is_file() and file.suffix.lower() in VIDEO_EXTENSIONS:
-        #             rows.append([file.name])
-
-        # # Write CSV
-        # with open(output_csv, "w", newline="", encoding="utf-8") as f:
-        #     writer = csv.writer(f)
-        #     writer.writerow(["VIDEO_FILENAME"])
-        #     writer.writerows(rows)
-
-        # print(f"CSV created with {len(rows)} entries → {output_csv}")
-
-    except KeyboardInterrupt:
-        print("\nStopped by Ctrl+C")

video_analyser/.gitignore

@@ -1,25 +0,0 @@
-# Google OAuth (now using environment variables)
-# credentials.json and token.json no longer needed
-
-# Downloaded videos
-infloxa/
-
-# Logs
-*.log
-
-# Python
-__pycache__/
-*.py[cod]
-*$py.class
-*.so
-.Python
-env/
-venv/
-ENV/
-
-# OS
-.DS_Store
-Thumbs.db
-
-analyse.py
-dedupe_videos.sh

video_analyser/README.md

@@ -1,264 +0,0 @@
-# Luxury B-Roll Video Analysis System
-
-A comprehensive Python system for downloading, analyzing, scoring, and cataloging luxury b-roll videos for short-form social media content (Instagram Reels, TikTok, YouTube Shorts).
-
-## Features
-
-- **Google Drive Integration**: Download entire folder structures automatically
-- **AI-Powered Analysis**: Uses Google Gemini AI for intelligent video scoring
-- **Technical Analysis**: Motion detection, face detection, duration extraction
-- **Weighted Scoring**: Configurable multi-factor scoring system
-- **CSV Output**: Machine-readable catalog for automated video selection
-- **Progress Tracking**: Real-time progress bars and detailed logging
-
-## System Requirements
-
-- Python 3.8+
-- OpenCV (for video processing)
-- Google Cloud credentials (for Drive API)
-- Gemini API key (for AI analysis)
-- Sufficient disk space (50GB+ recommended)
-
-## Installation
-
-### 1. Install Dependencies
-
-```bash
-cd video_analyser
-pip install -r requirements.txt
-```
-
-### 2. Setup Google Drive API
-
-1. Go to [Google Cloud Console](https://console.cloud.google.com/)
-2. Create a new project (or select existing)
-3. Enable the **Google Drive API**
-4. Create OAuth 2.0 credentials:
-   - Go to **Credentials** → **Create Credentials** → **OAuth client ID**
-   - Application type: **Desktop app**
-   - Download the credentials
-5. Save as `credentials.json` in the `video_analyser/` directory
-
-### 3. Setup Gemini API
-
-1. Get your Gemini API key from [Google AI Studio](https://makersuite.google.com/app/apikey)
-2. Set environment variable:
-
-```bash
-export GEMINI_API_KEY='your-api-key-here'
-
-# For permanent setup, add to ~/.bashrc or ~/.zshrc:
-echo 'export GEMINI_API_KEY="your-api-key-here"' >> ~/.bashrc
-source ~/.bashrc
-```
-
-### 4. Customize Analysis Prompt (Optional)
-
-The AI analysis prompt is stored in `analysis_prompt.md`. You can edit this file to customize:
-- Scoring criteria
-- Description style
-- Quality guidelines
-- Output format
-
-### 4. Configure Settings
-
-Edit `config.yaml` to customize:
-- Google Drive folder ID (default is already set)
-- Processing settings (concurrent downloads, frame sampling)
-- Scoring weights
-- Output paths
-
-## Usage
-
-### First Run (Full Pipeline)
-
-```bash
-python main.py
-```
-
-This will:
-1. Authenticate with Google Drive (opens browser)
-2. Download all videos maintaining folder structure
-3. Analyze each video with AI
-4. Generate `infloxa_video_analysis.csv`
-
-### Skip Download (Process Existing Videos)
-
-If you already have videos downloaded:
-
-```bash
-python main.py --skip-download
-```
-
-### Test Mode (Process First 3 Videos)
-
-For testing the pipeline:
-
-```bash
-python main.py --test-mode
-```
-
-### Custom Config File
-
-```bash
-python main.py --config custom_config.yaml
-```
-
-## Output Format
-
-The system generates `infloxa_video_analysis.csv` with the following columns:
-
-| Column | Type | Description |
-|--------|------|-------------|
-| `video_filename` | string | Original filename |
-| `category` | string | Folder name (e.g., luxury_car, yacht) |
-| `duration_seconds` | float | Video duration |
-| `description` | string | AI-generated luxury description (detailed) |
-| `luxury_score` | int (0-100) | Exclusivity and premium feel |
-| `visual_clarity_score` | int (0-100) | Composition and mobile readability |
-| `motion_score` | int (0-100) | Camera/subject motion level |
-| `beat_editing_score` | int (0-100) | Suitability for beat-synced edits |
-| `recommended_edit_type` | enum | static_hold, slow_pan, beat_cut, pulse_cut, transition_clip |
-| `ideal_clip_length_seconds` | float | Recommended clip length (typically 1.0-2.5s) |
-| `energy_level` | enum | low, medium, high |
-| `has_text_or_watermark` | boolean | true/false |
-| `has_faces_visible` | boolean | true/false |
-| `usable_for_ads` | boolean | Commercial safety (true/false) |
-| `final_selection_score` | int (0-100) | Weighted overall score |
-
-## Project Structure
-
-```
-video_analyser/
-├── config.yaml                    # Configuration settings
-├── requirements.txt               # Python dependencies
-├── README.md                      # This file
-├── analysis_prompt.md            # Gemini AI analysis prompt
-├── credentials.json              # Google Drive credentials (you provide)
-├── token.json                    # Auto-generated OAuth token
-├── main.py                       # Main orchestrator
-├── video_analysis.log            # Execution logs
-├── modules/
-│   ├── __init__.py
-│   ├── drive_downloader.py       # Google Drive integration
-│   ├── video_analyzer.py         # Technical video analysis
-│   ├── ai_analyzer.py            # Gemini AI analysis
-│   ├── scorer.py                 # Scoring algorithms
-│   └── csv_writer.py             # CSV output handler
-├── infloxa/                      # Downloaded videos (auto-created)
-│   ├── category_1/
-│   ├── category_2/
-│   └── ...
-└── infloxa_video_analysis.csv    # Output CSV
-```
-
-## Workflow Pipeline
-
-```
-1. Download Videos (Google Drive)
-   ↓
-2. Extract Technical Metadata (duration, motion, faces)
-   ↓
-3. Sample Key Frames
-   ↓
-4. AI Analysis with Gemini (descriptions, luxury scoring)
-   ↓
-5. Calculate Weighted Scores
-   ↓
-6. Write Row to CSV
-```
-
-## Scoring Algorithm
-
-The `final_selection_score` is calculated using weighted factors:
-
-```python
-final_score = (
-    luxury_score × 0.40 +
-    visual_clarity_score × 0.25 +
-    beat_editing_score × 0.20 +
-    motion_score × 0.10 +
-    usability_score × 0.05
-)
-```
-
-Weights can be customized in `config.yaml`.
-
-## Logging
-
-Detailed logs are written to `video_analysis.log`. Log level can be adjusted in `config.yaml`:
-- `DEBUG`: Verbose output for troubleshooting
-- `INFO`: Standard operational messages
-- `WARNING`: Important warnings
-- `ERROR`: Error messages only
-
-## Troubleshooting
-
-### Google Drive Authentication Issues
-
-- Delete `token.json` and re-authenticate
-- Ensure `credentials.json` is valid OAuth 2.0 credentials
-- Check that Drive API is enabled in Google Cloud Console
-
-### Gemini API Errors
-
-- Verify `GEMINI_API_KEY` environment variable is set: `echo $GEMINI_API_KEY`
-- Check you have sufficient quota/credits
-- Ensure `google-generativeai` package is up to date
-
-### Video Processing Errors
-
-- Ensure videos are in supported formats (mp4, mov, avi, mkv, webm)
-- Check that OpenCV is properly installed
-- For face detection issues, install `opencv-contrib-python`
-
-### Out of Memory
-
-- Reduce `frames_to_sample` in `config.yaml`
-- Process videos in smaller batches
-- Close other applications
-
-## Advanced Usage
-
-### Resume After Interruption
-
-The system automatically skips:
-- Already downloaded videos
-- Videos already in CSV (use append mode)
-
-Simply re-run `python main.py` to continue.
-
-### Batch Processing
-
-Edit `config.yaml` to adjust:
-```yaml
-processing:
-  max_concurrent_downloads: 3  # Parallel downloads
-  batch_size: 10               # Process in batches
-```
-
-### Custom Scoring Weights
-
-Modify weights in `config.yaml` (must sum to 1.0):
-```yaml
-scoring:
-  luxury_weight: 0.40
-  visual_clarity_weight: 0.25
-  beat_editing_weight: 0.20
-  motion_weight: 0.10
-  usability_weight: 0.05
-```
-
-## API Costs
-
-**Gemini API**: Each video analysis sends 3 frames (configurable). Monitor your usage at [Google AI Studio](https://makersuite.google.com/).
-
-**Google Drive API**: Free tier includes 10,000 requests/day (sufficient for most use cases).
-
-## License
-
-This is a proprietary system for Infloxa luxury video analysis.
-
-## Support
-
-For issues or questions, check the logs in `video_analysis.log` and ensure all configuration is correct.

video_analyser/analysis_prompt.md

@@ -1,246 +0,0 @@
-**System / Instruction Prompt**
-
-You are analyzing **luxury b-roll stock footage** for short-form social media content (Instagram Reels, TikTok, YouTube Shorts).
-
-This footage is sold as **premium luxury stock video** and will be used by:
-
-* Luxury & motivation theme pages
-* Content creators and agencies
-* Beat-synced and pulse-based edits
-* Premium brand advertising
-
-Your goal is to **label, score, and describe** each video so it can be efficiently selected and edited at scale.
-
----
-
-## 🎥 VIDEO CONTEXT
-
-Each input is a **short luxury b-roll clip** (no dialogue).
-Assume the video may be used for:
-
-* Beat-based editing
-* Pulse-based editing
-* Static luxury holds
-* Fast-paced aesthetic reels
-
----
-
-## 🧠 ANALYSIS REQUIREMENTS
-
-Analyze the video and return **ONLY valid JSON** with the following fields.
-
----
-
-### 1️⃣ category (string)
-
-Choose **ONE** primary category that best represents the clip:
-
-* `luxury_architecture`
-* `luxury_car`
-* `yacht_boat`
-* `private_jet`
-* `helicopter`
-* `luxury_watch`
-* `jewelry_accessories`
-* `luxury_fashion`
-* `fine_dining`
-* `champagne_wine`
-* `luxury_hotel`
-* `infinity_pool`
-* `penthouse_interior`
-* `cityscape`
-* `skyline_night`
-* `luxury_lifestyle`
-* `private_estate`
-* `golf_course`
-* `casino_gaming`
-* `luxury_tech`
-* `supercars_racing`
-* `nature_premium`
-* `tropical_beach`
-* `mountain_resort`
-* `desert_luxury`
-* `abstract_luxury`
-* `gold_elements`
-* `money_cash`
-* `crypto_finance`
-* `art_gallery`
-* `luxury_retail`
-* `other`
-
----
-
-### 2️⃣ short_description (string)
-
-A **concise one-line summary** (10-15 words max) of what the video shows.
-
-**Examples**:
-- "Luxury yacht sailing at sunset"
-- "Modern penthouse with city skyline view"
-- "Sports car driving through mountain road"
-
----
-
-### 3️⃣ description (string)
-
-Write a **high-quality, premium description** (3–5 sentences) that explains:
-
-* What is visible in the video
-* Why it feels luxurious or premium
-* Camera movement or framing style
-* Mood and atmosphere
-* Why it works for luxury content
-
-Focus on **visual storytelling**, not generic stock descriptions.
-
----
-
-### 4️⃣ luxury_score (integer 0–100)
-
-Rate the **exclusivity and aspirational quality**.
-
-**Be strict**:
-
-* Average clips should NOT exceed 70
-* Only truly exceptional luxury clips should score above 85
-
----
-
-### 5️⃣ visual_clarity_score (integer 0–100)
-
-Rate how well the clip works on **mobile screens**:
-
-* Clear subject
-* Clean composition
-* Not overly busy
-* Professional framing
-
----
-
-### 6️⃣ motion_score (integer 0–100)
-
-Rate the **amount and quality of motion**:
-
-* Camera movement
-* Subject movement
-* Suitability for dynamic edits
-
----
-
-### 7️⃣ beat_editing_score (integer 0–100)
-
-How well does this clip support:
-
-* Beat-based editing
-* Pulse-based rhythm
-* Fast luxury reels
-
----
-
-### 8️⃣ recommended_edit_type (string)
-
-Choose **ONE**:
-
-* `static_hold` – Minimal motion, elegant pause moments
-* `slow_pan` – Smooth cinematic movement
-* `beat_cut` – Sharp, intentional cuts on beats
-* `pulse_cut` – Rhythmic pulsing edits
-* `transition_clip` – Works well between scenes
-
----
-
-### 9️⃣ ideal_clip_length_seconds (float)
-
-Best usable duration for short-form editing
-(typically **1.0–2.5 seconds**).
-
----
-
-### 🔟 energy_level (string)
-
-Choose **ONE**:
-
-* `low` – Calm, slow, serene
-* `medium` – Balanced pacing
-* `high` – Fast, energetic, dynamic
-
----
-
-### 1️⃣1️⃣ has_text_or_watermark (boolean)
-
-Does the clip contain **any visible text, logos, or watermarks**?
-
----
-
-### 1️⃣2️⃣ usable_for_ads (boolean)
-
-Is this clip **commercially safe**?
-
-* False if logos, brands, text, or legal risks are visible
-
----
-
-### 1️⃣3️⃣ final_selection_score (integer 0–100)
-
-Overall suitability for **Infloxa luxury content**, considering:
-
-* Luxury quality
-* Visual clarity
-* Motion
-* Beat usability
-* Commercial safety
-
-This score will be used to **rank and select the best clips**.
-
----
-
-## 📤 OUTPUT FORMAT (STRICT)
-
-**Output ONLY valid JSON.**
-No explanations.
-No markdown.
-No extra text.
-
-```json
-{
-  "category": "luxury_architecture",
-  "short_description": "Futuristic yacht with glass garden at twilight",
-  "description": "Detailed 3–5 sentence luxury description...",
-  "luxury_score": 0,
-  "visual_clarity_score": 0,
-  "motion_score": 0,
-  "beat_editing_score": 0,
-  "recommended_edit_type": "slow_pan",
-  "ideal_clip_length_seconds": 1.5,
-  "energy_level": "medium",
-  "has_text_or_watermark": false,
-  "usable_for_ads": true,
-  "final_selection_score": 0
-}
-```
-
----
-
-## 🎯 SCORING GUIDELINES
-
-**Luxury Score Calibration**
-
-* 0–30: Not luxury
-* 31–50: Mild premium feel
-* 51–70: Good luxury content
-* 71–85: Exceptional
-* 86–100: Rare, top-tier
-
-**Penalize**:
-
-* Generic stock footage
-* Weak luxury signals
-* Overly busy or unclear visuals
-* Poor framing
-
-**Favor**:
-
-* Premium mobile aesthetics
-* Clean looping potential
-* Strong beat compatibility
-* High aspirational value

video_analyser/config.yaml

@@ -1,39 +0,0 @@
-# Luxury Video Analysis System Configuration
-
-# Google Drive Settings
-google_drive:
-  folder_id: "1WSrVAyqvPJzpRnoUxkNx0LqK9VlDs432"
-  # OAuth credentials from environment variables:
-  # - SERVER_GOOGLE_CLIENT_ID
-  # - SERVER_GOOGLE_CLIENT_SECRET
-  # - SERVER_GOOGLE_REFRESH_TOKEN
-  
-# Gemini API Settings
-gemini:
-  # API key is read from GEMINI_API_KEY environment variable
-  model: "gemini-3-pro-preview"  # or "gemini-1.5-pro" for better quality
-  prompt_file: "analysis_prompt.md"  # Path to analysis prompt
-  
-# Processing Settings
-processing:
-  max_concurrent_downloads: 3
-  batch_size: 10  # Process videos in batches
-  frames_to_sample: 3  # Number of frames to send to AI
-  
-# Output Settings
-output:
-  local_video_dir: "video_for_workflow"  # Relative to video_analyser/
-  csv_file: "infloxa_video_analysis.csv"  # Relative to video_analyser/
-  
-# Scoring Weights (must sum to 1.0)
-scoring:
-  luxury_weight: 0.40
-  visual_clarity_weight: 0.25
-  beat_editing_weight: 0.20
-  motion_weight: 0.10
-  usability_weight: 0.05
-  
-# Logging
-logging:
-  level: "INFO"  # DEBUG, INFO, WARNING, ERROR
-  file: "video_analysis.log"  # Relative to video_analyser/

video_analyser/get_refresh_token.py

@@ -1,21 +0,0 @@
-from google_auth_oauthlib.flow import InstalledAppFlow
-
-# ✅ Use YouTube scope (not Drive)
-SCOPES = ['https://www.googleapis.com/auth/drive.readonly']
-
-def main():
-    print("🔑 Starting OAuth flow...")
-
-    flow = InstalledAppFlow.from_client_secrets_file(
-        "whoa/client_secret_688373610660-vtr5l8q7s4is9kkvd7hla1cqg273emfs.apps.googleusercontent.com.json",
-        SCOPES
-    )
-
-    creds = flow.run_local_server(port=0)
-
-    print("\n✅ AUTH SUCCESS")
-    print("REFRESH TOKEN:\n")
-    print(creds.refresh_token)
-
-if __name__ == "__main__":
-    main()

video_analyser/main.py

@@ -1,501 +0,0 @@
-"""
-Luxury B-Roll Video Analysis System
-Main orchestrator for downloading, analyzing, scoring, and cataloging luxury videos
-"""
-
-import os
-import sys
-import yaml
-import logging
-import argparse
-from pathlib import Path
-from typing import Dict, List
-from tqdm import tqdm
-from dotenv import load_dotenv
-
-# Load environment variables from .env file
-load_dotenv()
-
-from modules import (
-    DriveDownloader,
-    VideoAnalyzer,
-    AIAnalyzer,
-    Scorer,
-    CSVWriter
-)
-from modules.git_ops import git_commit_progress
-
-
-# Get script directory for relative paths
-SCRIPT_DIR = Path(__file__).parent.absolute()
-PROGRESS_DIR = SCRIPT_DIR / "progress"
-
-
-def get_progress_file(job_index=None):
-    """Get the appropriate progress file for this job."""
-    PROGRESS_DIR.mkdir(exist_ok=True)
-    
-    if job_index is not None:
-        return PROGRESS_DIR / f"analyzed_videos_job{job_index}.txt"
-    return PROGRESS_DIR / "analyzed_videos.txt"
-
-
-def load_all_analyzed_videos():
-    """
-    Load analyzed videos from all job-specific progress files.
-    Returns a set of video filenames that have been successfully analyzed.
-    """
-    analyzed = set()
-    
-    if not PROGRESS_DIR.exists():
-        return analyzed
-    
-    # Load from main progress file
-    main_progress = PROGRESS_DIR / "analyzed_videos.txt"
-    if main_progress.exists():
-        with open(main_progress, "r") as f:
-            analyzed.update(x.strip() for x in f if x.strip())
-    
-    # Load from all job-specific files
-    for job_file in PROGRESS_DIR.glob("analyzed_videos_job*.txt"):
-        with open(job_file, "r") as f:
-            analyzed.update(x.strip() for x in f if x.strip())
-    
-    return analyzed
-
-
-def setup_logging(config: dict):
-    """
-    Setup logging configuration
-    
-    Args:
-        config: Configuration dictionary
-    """
-    log_level = getattr(logging, config['logging']['level'].upper())
-    log_file = config['logging']['file']
-    
-    # Create formatter
-    formatter = logging.Formatter(
-        '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
-    )
-    
-    # File handler
-    file_handler = logging.FileHandler(log_file)
-    file_handler.setLevel(log_level)
-    file_handler.setFormatter(formatter)
-    
-    # Console handler
-    console_handler = logging.StreamHandler()
-    console_handler.setLevel(logging.INFO)
-    console_handler.setFormatter(formatter)
-    
-    # Root logger
-    root_logger = logging.getLogger()
-    root_logger.setLevel(log_level)
-    root_logger.addHandler(file_handler)
-    root_logger.addHandler(console_handler)
-
-
-def load_config(config_path: str = 'config.yaml') -> dict:
-    """
-    Load configuration from YAML file
-    
-    Args:
-        config_path: Path to config file (relative to script directory or absolute)
-        
-    Returns:
-        Configuration dictionary
-    """
-    try:
-        # Get the directory where this script is located
-        script_dir = Path(__file__).parent.absolute()
-        
-        # If config_path is just a filename, look in script directory
-        config_file = Path(config_path)
-        if not config_file.is_absolute() and config_file.name == config_path:
-            config_file = script_dir / config_path
-        
-        with open(config_file, 'r') as f:
-            config = yaml.safe_load(f)
-        return config
-    except Exception as e:
-        print(f"Error loading config: {e}")
-        sys.exit(1)
-
-
-def validate_config(config: dict) -> bool:
-    """
-    Validate configuration has all required fields
-    
-    Args:
-        config: Configuration dictionary
-        
-    Returns:
-        True if valid, False otherwise
-    """
-    # Check Gemini API key from environment
-    if not os.getenv('GEMINI_API_KEY'):
-        print("ERROR: GEMINI_API_KEY environment variable not set")
-        print("Set it with: export GEMINI_API_KEY='your-api-key-here'")
-        return False
-    
-    # Check Google OAuth credentials from environment
-    required_oauth = [
-        'SERVER_GOOGLE_CLIENT_ID',
-        'SERVER_GOOGLE_CLIENT_SECRET', 
-        'SERVER_GOOGLE_REFRESH_TOKEN'
-    ]
-    
-    missing_oauth = [var for var in required_oauth if not os.getenv(var)]
-    if missing_oauth:
-        print("ERROR: Missing Google OAuth environment variables:")
-        for var in missing_oauth:
-            print(f"  - {var}")
-        print("\nSet them with:")
-        print("  export SERVER_GOOGLE_CLIENT_ID='your-client-id'")
-        print("  export SERVER_GOOGLE_CLIENT_SECRET='your-client-secret'")
-        print("  export SERVER_GOOGLE_REFRESH_TOKEN='your-refresh-token'")
-        return False
-    
-    return True
-
-
-def process_single_video(video_path: str, config: dict, 
-                        video_analyzer: VideoAnalyzer,
-                        ai_analyzer: AIAnalyzer,
-                        scorer: Scorer,
-                        csv_writer: CSVWriter,
-                        progress_file: Path,
-                        job_index: int = None,
-                        commit: bool = False) -> bool:
-    """
-    Process a single video through the complete pipeline
-    
-    Args:
-        video_path: Path to video file
-        config: Configuration dictionary
-        video_analyzer: VideoAnalyzer instance
-        ai_analyzer: AIAnalyzer instance
-        scorer: Scorer instance
-        csv_writer: CSVWriter instance
-        progress_file: Path to progress tracking file
-        job_index: Job index for parallel processing
-        commit: Whether to commit progress to git
-        
-    Returns:
-        True if successful, False otherwise
-    """
-    logger = logging.getLogger(__name__)
-    video_filename = os.path.basename(video_path)
-    
-    try:
-        # STEP 1: Technical Analysis
-        logger.info(f"Processing: {video_filename}")
-        technical_analysis = video_analyzer.analyze_video(video_path)
-        
-        # STEP 2: AI Analysis
-        ai_analysis = ai_analyzer.analyze_with_gemini(
-            video_path,
-            video_filename
-        )
-        
-        if not ai_analysis:
-            logger.error(f"AI analysis failed for {video_path}")
-            return False
-        
-        # STEP 3: Calculate Scores
-        scoring_results = scorer.calculate_final_score(
-            technical_analysis,
-            ai_analysis
-        )
-        
-        if not scoring_results:
-            logger.error(f"Scoring failed for {video_path}")
-            return False
-        
-        # STEP 4: Write to CSV
-        csv_writer.write_row(
-            video_path,
-            technical_analysis,
-            ai_analysis,
-            scoring_results,
-            config['output']['local_video_dir']
-        )
-        
-        # STEP 5: Track progress
-        with progress_file.open("a") as pf:
-            pf.write(f"{video_filename}\n")
-        
-        logger.info(
-            f"✓ Completed: {video_filename} "
-            f"(Luxury: {scoring_results['luxury_score']}, "
-            f"Final: {scoring_results['final_selection_score']})"
-        )
-        
-        # STEP 6: Commit progress (if enabled)
-        if commit:
-            git_commit_progress(
-                config['output']['csv_file'],
-                config['logging']['file'],
-                progress_file,
-                job_index,
-                commit
-            )
-        
-        return True
-        
-    except Exception as e:
-        logger.error(f"Error processing {video_path}: {e}", exc_info=True)
-        return False
-
-
-def get_local_videos(local_dir: str) -> List[str]:
-    """
-    Get list of all video files in local directory
-    
-    Args:
-        local_dir: Base directory to search
-        
-    Returns:
-        List of video file paths
-    """
-    video_extensions = ['.mp4', '.mov', '.avi', '.mkv', '.webm']
-    video_files = []
-    
-    for root, dirs, files in os.walk(local_dir):
-        for file in files:
-            if any(file.lower().endswith(ext) for ext in video_extensions):
-                video_files.append(os.path.join(root, file))
-    
-    return sorted(video_files)
-
-
-def main():
-    """Main execution flow with progress tracking and parallel job support"""
-    parser = argparse.ArgumentParser(
-        description='Luxury B-Roll Video Analysis System',
-        formatter_class=argparse.RawDescriptionHelpFormatter,
-        epilog="""
-Examples:
-  # Run without committing
-  python main.py
-
-  # Run and auto-commit progress
-  python main.py --commit
-
-  # Skip download, process existing videos
-  python main.py --skip-download --commit
-
-  # Parallel processing (job 1 of 3)
-  python main.py --commit --job-index 0 --total-jobs 3
-
-  # Test mode
-  python main.py --test-mode
-"""
-    )
-    parser.add_argument(
-        '--skip-download',
-        action='store_true',
-        help='Skip Google Drive download and process existing local videos'
-    )
-    parser.add_argument(
-        '--test-mode',
-        action='store_true',
-        help='Process only first 3 videos for testing'
-    )
-    parser.add_argument(
-        '--config',
-        default='config.yaml',
-        help='Path to configuration file'
-    )
-    parser.add_argument(
-        '--commit',
-        action='store_true',
-        help='Commit and push results to git repository'
-    )
-    parser.add_argument(
-        '--job-index',
-        type=int,
-        default=None,
-        help='Index of this job (0-based) for parallel processing'
-    )
-    parser.add_argument(
-        '--total-jobs',
-        type=int,
-        default=None,
-        help='Total number of parallel jobs'
-    )
-    
-    args = parser.parse_args()
-    
-    # Allow environment variables to override args (for GitHub Actions)
-    job_index = args.job_index
-    total_jobs = args.total_jobs
-    
-    if job_index is None and "JOB_INDEX" in os.environ:
-        job_index = int(os.environ["JOB_INDEX"])
-    if total_jobs is None and "TOTAL_JOBS" in os.environ:
-        total_jobs = int(os.environ["TOTAL_JOBS"])
-    
-    # Load configuration
-    print("Loading configuration...")
-    config = load_config(args.config)
-    
-    # Make paths relative to video_analyser directory
-    script_dir = Path(__file__).parent.absolute()
-    if not Path(config['output']['local_video_dir']).is_absolute():
-        config['output']['local_video_dir'] = str(script_dir / config['output']['local_video_dir'])
-    if not Path(config['output']['csv_file']).is_absolute():
-        config['output']['csv_file'] = str(script_dir / config['output']['csv_file'])
-    if not Path(config['logging']['file']).is_absolute():
-        config['logging']['file'] = str(script_dir / config['logging']['file'])
-    
-    # Setup logging
-    setup_logging(config)
-    logger = logging.getLogger(__name__)
-    
-    logger.info("=" * 60)
-    logger.info("Luxury B-Roll Video Analysis System")
-    logger.info("=" * 60)
-    
-    # Validate configuration
-    if not validate_config(config):
-        sys.exit(1)
-    
-    # Get progress file for this job
-    progress_file = get_progress_file(job_index)
-    
-    # Load all analyzed videos (from all jobs)
-    analyzed_videos = load_all_analyzed_videos()
-    logger.info(f"📊 Found {len(analyzed_videos)} already analyzed videos (skipping these)")
-    
-    # Initialize modules
-    logger.info("Initializing modules...")
-    video_analyzer = VideoAnalyzer(config)
-    ai_analyzer = AIAnalyzer(config)
-    scorer = Scorer(config)
-    csv_writer = CSVWriter(config)
-    
-    # STEP 1: Get list of videos from Google Drive (don't download yet)
-    if not args.skip_download:
-        logger.info("STEP 1: Connecting to Google Drive")
-        drive_downloader = DriveDownloader(config)
-        drive_downloader.authenticate()
-        
-        # Get list of all videos without downloading
-        logger.info("Fetching video list from Google Drive...")
-        all_video_items = drive_downloader.list_all_videos()
-        logger.info(f"Found {len(all_video_items)} videos in Google Drive")
-        
-    else:
-        logger.info("STEP 1: Skipping download, using local videos")
-        local_dir = config['output']['local_video_dir']
-        
-        if not os.path.exists(local_dir):
-            logger.error(f"Local directory does not exist: {local_dir}")
-            sys.exit(1)
-        
-        # Get local videos
-        video_files = get_local_videos(local_dir)
-        logger.info(f"Found {len(video_files)} local videos")
-        all_video_items = [{'local_path': vf} for vf in video_files]
-
-    if not all_video_items:
-        logger.error("No videos to process!")
-        sys.exit(1)
-    
-    # Filter out already analyzed videos
-    analyzed_videos = load_all_analyzed_videos()
-    videos_to_process = []
-    
-    for item in all_video_items:
-        if 'local_path' in item:
-            filename = os.path.basename(item['local_path'])
-        else:
-            filename = item['name']
-        
-        if filename not in analyzed_videos:
-            videos_to_process.append(item)
-    
-    logger.info(
-        f"📹 Total videos: {len(all_video_items)}, "
-        f"Already analyzed: {len(all_video_items) - len(videos_to_process)}, "
-        f"To process: {len(videos_to_process)}"
-    )
-    
-    if not videos_to_process:
-        logger.info("✅ All videos already analyzed!")
-        return
-    
-    # STEP 2-4: Process each video ONE BY ONE
-    logger.info(f"\nProcessing {len(videos_to_process)} videos one-by-one...\n")
-    
-    successful = 0
-    failed = 0
-    
-    for idx, item in enumerate(videos_to_process, 1):
-        try:
-            # Download single video if from Drive
-            if 'local_path' not in item:
-                logger.info(f"[{idx}/{len(videos_to_process)}] Downloading: {item['name']}")
-                local_path = drive_downloader.download_single_video(item)
-                if not local_path:
-                    logger.error(f"Failed to download {item['name']}")
-                    failed += 1
-                    continue
-            else:
-                local_path = item['local_path']
-            
-            # Process the video
-            logger.info(f"[{idx}/{len(videos_to_process)}] Analyzing: {os.path.basename(local_path)}")
-            
-            if process_single_video(
-                local_path, config, video_analyzer, ai_analyzer, scorer, csv_writer,
-                progress_file, job_index, args.commit
-            ):
-                successful += 1
-                logger.info(f"✅ [{idx}/{len(videos_to_process)}] Completed successfully")
-                
-                # Commit after each successful video
-                if args.commit:
-                    git_commit_progress(
-                        config['output']['csv_file'],
-                        config['logging']['file'],
-                        progress_file,
-                        job_index,
-                        args.commit
-                    )
-            else:
-                failed += 1
-                logger.warning(f"⚠️ [{idx}/{len(videos_to_process)}] Processing failed")
-                
-        except Exception as e:
-            logger.error(f"❌ Error processing video {idx}: {e}", exc_info=True)
-            failed += 1
-            continue
-    
-    # Final commit
-    if args.commit and successful > 0:
-        git_commit_progress(
-            config['output']['csv_file'],
-            config['logging']['file'],
-            progress_file,
-            job_index,
-            args.commit
-        )
-    
-    # Summary
-    job_label = f"Job {job_index}" if job_index is not None else "Analysis"
-    logger.info("\n" + "=" * 60)
-    logger.info(f"{job_label} COMPLETE")
-    logger.info("=" * 60)
-    logger.info(f"Total videos: {len(videos_to_process)}")
-    logger.info(f"Successful: {successful}")
-    logger.info(f"Failed: {failed}")
-    logger.info(f"CSV output: {config['output']['csv_file']}")
-    logger.info(f"Progress file: {progress_file}")
-    logger.info("=" * 60)
-
-
-if __name__ == '__main__':
-    main()

video_analyser/modules/__init__.py

@@ -1,19 +0,0 @@
-"""
-Luxury Video Analysis System Modules
-"""
-
-from .drive_downloader import DriveDownloader
-from .video_analyzer import VideoAnalyzer
-from .ai_analyzer import AIAnalyzer
-from .scorer import Scorer
-from .csv_writer import CSVWriter
-from .git_ops import git_commit_progress
-
-__all__ = [
-    'DriveDownloader',
-    'VideoAnalyzer',
-    'AIAnalyzer',
-    'Scorer',
-    'CSVWriter',
-    'git_commit_progress'
-]

video_analyser/modules/ai_analyzer.py

@@ -1,186 +0,0 @@
-"""
-AI Analyzer Module
-Uses Gemini API for intelligent video analysis and scoring
-"""
-
-import os
-import logging
-import json
-from typing import Dict, List, Optional
-from pathlib import Path
-import google.generativeai as genai
-
-logger = logging.getLogger(__name__)
-
-
-class AIAnalyzer:
-    """Use Gemini AI to analyze luxury video content"""
-    
-    def __init__(self, config: dict):
-        """
-        Initialize AI Analyzer
-        
-        Args:
-            config: Configuration dictionary with gemini settings
-        """
-        # Get API key from environment
-        self.api_key = os.getenv('GEMINI_API_KEY')
-        if not self.api_key:
-            raise ValueError("GEMINI_API_KEY environment variable not set")
-        
-        self.model_name = config['gemini']['model']
-        self.prompt_file = config['gemini']['prompt_file']
-        
-        # Load prompt from file
-        self.prompt_template = self._load_prompt()
-        
-        # Configure Gemini
-        genai.configure(api_key=self.api_key)
-        self.model = genai.GenerativeModel(self.model_name)
-        
-        logger.info(f"Initialized Gemini AI with model: {self.model_name}")
-    
-    def _load_prompt(self) -> str:
-        """Load analysis prompt from markdown file"""
-        try:
-            # Get path relative to this module's location
-            module_dir = Path(__file__).parent.parent.absolute()
-            prompt_path = Path(self.prompt_file)
-            
-            # If it's just a filename, look in video_analyser directory
-            if not prompt_path.is_absolute() and prompt_path.name == self.prompt_file:
-                prompt_path = module_dir / self.prompt_file
-            
-            with open(prompt_path, 'r', encoding='utf-8') as f:
-                prompt = f.read()
-            logger.info(f"Loaded prompt from {prompt_path}")
-            return prompt
-        except Exception as e:
-            logger.error(f"Failed to load prompt file: {e}")
-            raise
-    
-    def analyze_with_gemini(self, video_path: str, video_filename: str) -> Optional[Dict]:
-        """
-        Analyze video using Gemini AI by uploading the video file directly
-        
-        Args:
-            video_path: Path to video file
-            video_filename: Name of video file for logging
-            
-        Returns:
-            Dictionary with analysis results or None on failure
-        """
-        try:
-            logger.info(f"Uploading video to Gemini: {video_filename}")
-            
-            # Upload video file to Gemini
-            video_file = genai.upload_file(path=video_path)
-            
-            # Wait for video to be processed
-            import time
-            while video_file.state.name == "PROCESSING":
-                time.sleep(1)
-                video_file = genai.get_file(video_file.name)
-            
-            if video_file.state.name == "FAILED":
-                logger.error(f"Video processing failed for {video_filename}")
-                return None
-            
-            logger.info(f"Video uploaded successfully: {video_filename}")
-            
-            # Prepare content for Gemini (prompt + video)
-            content = [self.prompt_template, video_file]
-            
-            # Call Gemini API
-            response = self.model.generate_content(content)
-            
-            # Clean up uploaded file
-            genai.delete_file(video_file.name)
-            
-            # Parse JSON response
-            analysis = self.parse_gemini_response(response.text)
-            
-            if analysis:
-                logger.info(f"AI analysis completed for {video_filename}")
-                return analysis
-            else:
-                logger.error(f"Failed to parse Gemini response for {video_filename}")
-                return None
-                
-        except Exception as e:
-            logger.error(f"Error during Gemini analysis for {video_filename}: {e}")
-            return None
-    
-    def parse_gemini_response(self, response_text: str) -> Optional[Dict]:
-        """
-        Parse and validate Gemini JSON response
-        
-        Args:
-            response_text: Raw text response from Gemini
-            
-        Returns:
-            Parsed dictionary or None if invalid
-        """
-        try:
-            # Remove markdown code blocks if present
-            text = response_text.strip()
-            if text.startswith('```json'):
-                text = text[7:]
-            if text.startswith('```'):
-                text = text[3:]
-            if text.endswith('```'):
-                text = text[:-3]
-            text = text.strip()
-            
-            # Parse JSON
-            data = json.loads(text)
-            
-            # Validate required fields (updated for new prompt format)
-            required_fields = [
-                'category', 'short_description', 'description', 'luxury_score', 'visual_clarity_score',
-                'motion_score', 'beat_editing_score', 'recommended_edit_type',
-                'ideal_clip_length_seconds', 'energy_level',
-                'has_text_or_watermark', 'usable_for_ads', 'final_selection_score'
-            ]
-            
-            for field in required_fields:
-                if field not in data:
-                    logger.error(f"Missing required field: {field}")
-                    logger.debug(f"Received data: {json.dumps(data, indent=2)}")
-                    return None
-            
-            # Validate score ranges (all scores 0-100)
-            score_fields = [
-                'luxury_score', 'visual_clarity_score', 'motion_score',
-                'beat_editing_score', 'final_selection_score'
-            ]
-            for score_field in score_fields:
-                if not (0 <= data[score_field] <= 100):
-                    logger.error(f"Invalid score for {score_field}: {data[score_field]}")
-                    return None
-            
-            # Add has_faces_visible if Gemini didn't provide it (optional field)
-            if 'has_faces_visible' not in data:
-                data['has_faces_visible'] = False  # Default to false
-
-            
-            # Validate enums
-            valid_edit_types = ['static_hold', 'slow_pan', 'beat_cut', 'pulse_cut', 'transition_clip']
-            if data['recommended_edit_type'] not in valid_edit_types:
-                logger.error(f"Invalid edit type: {data['recommended_edit_type']}")
-                return None
-            
-            valid_energy_levels = ['low', 'medium', 'high']
-            if data['energy_level'] not in valid_energy_levels:
-                logger.error(f"Invalid energy level: {data['energy_level']}")
-                return None
-            
-            return data
-            
-        except json.JSONDecodeError as e:
-            logger.error(f"Failed to parse JSON: {e}")
-            logger.debug(f"Response text: {response_text}")
-            return None
-        except Exception as e:
-            logger.error(f"Error parsing Gemini response: {e}")
-            return None

video_analyser/modules/csv_writer.py

@@ -1,183 +0,0 @@
-"""
-CSV Writer Module
-Write video analysis results to CSV in specified format
-"""
-
-import csv
-import os
-import logging
-from typing import Dict
-from pathlib import Path
-
-logger = logging.getLogger(__name__)
-
-
-class CSVWriter:
-    """Handle CSV output with exact column ordering"""
-    
-    # Exact column order as specified
-    COLUMNS = [
-        'VIDEO_FILENAME',
-        'category',
-        'duration_seconds',
-        'short_description',
-        'description',
-        'luxury_score',
-        'visual_clarity_score',
-        'motion_score',
-        'beat_editing_score',
-        'recommended_edit_type',
-        'ideal_clip_length_seconds',
-        'energy_level',
-        'has_text_or_watermark',
-        'has_faces_visible',
-        'usable_for_ads',
-        'final_selection_score'
-    ]
-    
-    def __init__(self, config: dict):
-        """
-        Initialize CSV Writer
-        
-        Args:
-            config: Configuration dictionary with output settings
-        """
-        self.csv_file = config['output']['csv_file']
-        self.initialized = False
-    
-    def initialize(self):
-        """Initialize CSV file with headers if it doesn't exist"""
-        if not os.path.exists(self.csv_file):
-            with open(self.csv_file, 'w', newline='', encoding='utf-8') as f:
-                writer = csv.DictWriter(f, fieldnames=self.COLUMNS)
-                writer.writeheader()
-            logger.info(f"Created CSV file: {self.csv_file}")
-        else:
-            logger.info(f"Using existing CSV file: {self.csv_file}")
-        
-        self.initialized = True
-    
-    def extract_category(self, video_path: str, local_dir: str) -> str:
-        """
-        Extract category from folder structure
-        
-        Args:
-            video_path: Full path to video file
-            local_dir: Base local directory
-            
-        Returns:
-            Category name (folder name)
-        """
-        try:
-            # Get relative path
-            rel_path = os.path.relpath(video_path, local_dir)
-            
-            # Get parent folder name
-            parts = Path(rel_path).parts
-            
-            if len(parts) > 1:
-                # Category is the first folder
-                return parts[0]
-            else:
-                return "uncategorized"
-                
-        except Exception as e:
-            logger.error(f"Error extracting category from {video_path}: {e}")
-            return "uncategorized"
-    
-    def validate_row(self, row: Dict) -> bool:
-        """
-        Validate row data before writing
-        
-        Args:
-            row: Dictionary with row data
-            
-        Returns:
-            True if valid, False otherwise
-        """
-        # Check all required columns present
-        for col in self.COLUMNS:
-            if col not in row:
-                logger.error(f"Missing column: {col}")
-                return False
-        
-        # Validate data types
-        try:
-            # Numeric fields
-            assert isinstance(row['duration_seconds'], (int, float))
-            assert isinstance(row['luxury_score'], int)
-            assert isinstance(row['visual_clarity_score'], int)
-            assert isinstance(row['motion_score'], int)
-            assert isinstance(row['beat_editing_score'], int)
-            assert isinstance(row['ideal_clip_length_seconds'], (int, float))
-            assert isinstance(row['final_selection_score'], int)
-            
-            # String fields
-            assert isinstance(row['VIDEO_FILENAME'], str)
-            assert isinstance(row['category'], str)
-            assert isinstance(row['short_description'], str)
-            assert isinstance(row['description'], str)
-            assert isinstance(row['recommended_edit_type'], str)
-            assert isinstance(row['energy_level'], str)
-            
-            # Boolean fields (should be strings 'true' or 'false')
-            assert row['has_text_or_watermark'] in ['true', 'false']
-            assert row['has_faces_visible'] in ['true', 'false']
-            assert row['usable_for_ads'] in ['true', 'false']
-            
-            return True
-            
-        except AssertionError as e:
-            logger.error(f"Row validation failed: {e}")
-            return False
-    
-    def write_row(self, video_path: str, technical_analysis: Dict, 
-                  ai_analysis: Dict, scoring_results: Dict, local_dir: str):
-        """
-        Write a single row to CSV
-        
-        Args:
-            video_path: Path to video file
-            technical_analysis: Results from VideoAnalyzer
-            ai_analysis: Results from AIAnalyzer
-            scoring_results: Results from Scorer
-            local_dir: Base local directory for extracting category
-        """
-        if not self.initialized:
-            self.initialize()
-        
-        # Prepare row data
-        row = {
-            'VIDEO_FILENAME': os.path.basename(video_path),
-            'category': ai_analysis.get('category', 'uncategorized'),
-            'duration_seconds': technical_analysis['duration_seconds'],
-            'short_description': ai_analysis.get('short_description', ''),
-            'description': ai_analysis['description'],
-            'luxury_score': scoring_results['luxury_score'],
-            'visual_clarity_score': scoring_results['visual_clarity_score'],
-            'motion_score': scoring_results['motion_score'],
-            'beat_editing_score': scoring_results['beat_editing_score'],
-            'recommended_edit_type': ai_analysis['recommended_edit_type'],
-            'ideal_clip_length_seconds': scoring_results['ideal_clip_length_seconds'],
-            'energy_level': ai_analysis['energy_level'],
-            'has_text_or_watermark': 'true' if ai_analysis['has_text_or_watermark'] else 'false',
-            'has_faces_visible': 'true' if ai_analysis.get('has_faces_visible', False) else 'false',
-            'usable_for_ads': 'true' if ai_analysis['usable_for_ads'] else 'false',
-            'final_selection_score': scoring_results['final_selection_score']
-        }
-        
-        # Validate before writing
-        if not self.validate_row(row):
-            logger.error(f"Skipping invalid row for {video_path}")
-            return
-        
-        # Write to CSV
-        try:
-            with open(self.csv_file, 'a', newline='', encoding='utf-8') as f:
-                writer = csv.DictWriter(f, fieldnames=self.COLUMNS)
-                writer.writerow(row)
-            
-            logger.debug(f"Wrote row to CSV for {os.path.basename(video_path)}")
-            
-        except Exception as e:
-            logger.error(f"Error writing row to CSV: {e}")

video_analyser/modules/drive_downloader.py

@@ -1,245 +0,0 @@
-"""
-Google Drive Downloader Module
-Downloads videos from Google Drive maintaining folder structure
-"""
-
-import os
-import io
-import logging
-from pathlib import Path
-from typing import List, Dict, Optional
-from tqdm import tqdm
-
-from google.auth.transport.requests import Request
-from google.oauth2.credentials import Credentials
-from googleapiclient.discovery import build
-from googleapiclient.http import MediaIoBaseDownload
-
-logger = logging.getLogger(__name__)
-
-# Note: drive.file only allows access to files created by the app
-# For accessing existing shared folders, we need broader scope
-SCOPES = [
-    'https://www.googleapis.com/auth/drive.readonly'
-]
-
-
-class DriveDownloader:
-    """Handle Google Drive authentication and video downloads"""
-    
-    def __init__(self, config: dict):
-        """
-        Initialize Drive Downloader
-        
-        Args:
-            config: Configuration dictionary with google_drive settings
-        """
-        self.folder_id = config['google_drive']['folder_id']
-        self.local_dir = config['output']['local_video_dir']
-        self.service = None
-        
-    def authenticate(self):
-        """Authenticate with Google Drive API using environment variables"""
-        # Get OAuth credentials from environment
-        client_id = os.getenv('SERVER_GOOGLE_CLIENT_ID')
-        client_secret = os.getenv('SERVER_GOOGLE_CLIENT_SECRET')
-        
-        # Try Drive-specific token first, fall back to server token
-        refresh_token = os.getenv('SERVER_GOOGLE_REFRESH_TOKEN')
-        
-        if not all([client_id, client_secret, refresh_token]):
-            raise ValueError(
-                "Missing Google OAuth credentials. Required environment variables:\n"
-                "  - SERVER_GOOGLE_CLIENT_ID\n"
-                "  - SERVER_GOOGLE_CLIENT_SECRET\n"
-                "Run 'python video_analyser/get_refresh_token.py' to get a Drive token."
-            )
-        
-        # Create credentials from environment variables
-        creds = Credentials(
-            token=None,
-            refresh_token=refresh_token,
-            token_uri='https://oauth2.googleapis.com/token',
-            client_id=client_id,
-            client_secret=client_secret,
-            scopes=SCOPES
-        )
-        
-        # Refresh the token
-        creds.refresh(Request())
-        
-        self.service = build('drive', 'v3', credentials=creds)
-        logger.info("Successfully authenticated with Google Drive")
-        
-    def list_folder_structure(self, folder_id: Optional[str] = None) -> List[Dict]:
-        """
-        List all files and folders recursively
-        
-        Args:
-            folder_id: Folder ID to list (defaults to root folder)
-            
-        Returns:
-            List of file/folder metadata dictionaries
-        """
-        if folder_id is None:
-            folder_id = self.folder_id
-            
-        items = []
-        
-        # Query for all items in folder
-        query = f"'{folder_id}' in parents and trashed=false"
-        results = self.service.files().list(
-            q=query,
-            fields="files(id, name, mimeType, size)",
-            pageSize=1000
-        ).execute()
-        
-        files = results.get('files', [])
-        
-        for file in files:
-            if file['mimeType'] == 'application/vnd.google-apps.folder':
-                # Recursively get folder contents
-                subfolder_items = self.list_folder_structure(file['id'])
-                items.extend([
-                    {**item, 'path': f"{file['name']}/{item['path']}"} 
-                    for item in subfolder_items
-                ])
-            else:
-                # Add file with relative path
-                items.append({
-                    'id': file['id'],
-                    'name': file['name'],
-                    'path': file['name'],
-                    'size': int(file.get('size', 0)),
-                    'mimeType': file['mimeType']
-                })
-        
-        return items
-    
-    def download_file(self, file_id: str, destination: str) -> bool:
-        """
-        Download a single file from Google Drive
-        
-        Args:
-            file_id: Google Drive file ID
-            destination: Local file path to save to
-            
-        Returns:
-            True if successful, False otherwise
-        """
-        try:
-            # Create parent directory if needed
-            os.makedirs(os.path.dirname(destination), exist_ok=True)
-            
-            # Check if file already exists
-            if os.path.exists(destination):
-                logger.debug(f"Skipping existing file: {destination}")
-                return True
-            
-            # Download file
-            request = self.service.files().get_media(fileId=file_id)
-            fh = io.BytesIO()
-            
-            downloader = MediaIoBaseDownload(fh, request)
-            done = False
-            
-            while not done:
-                status, done = downloader.next_chunk()
-                
-            # Write to file
-            with open(destination, 'wb') as f:
-                fh.seek(0)
-                f.write(fh.read())
-                
-            logger.debug(f"Downloaded: {destination}")
-            return True
-            
-        except Exception as e:
-            logger.error(f"Failed to download {destination}: {e}")
-            return False
-    
-    def download_all(self) -> List[str]:
-        """
-        Download all videos from Drive maintaining folder structure
-        
-        Returns:
-            List of local file paths that were downloaded
-        """
-        if not self.service:
-            self.authenticate()
-        
-        logger.info(f"Listing files in Google Drive folder...")
-        items = self.list_folder_structure()
-        
-        # Filter for video files only
-        video_extensions = ['.mp4', '.mov', '.avi', '.mkv', '.webm']
-        video_files = [
-            item for item in items 
-            if any(item['name'].lower().endswith(ext) for ext in video_extensions)
-        ]
-        
-        logger.info(f"Found {len(video_files)} video files to download")
-        
-        downloaded_files = []
-
-        # Download with progress bar
-        with tqdm(total=len(video_files), desc="Downloading videos") as pbar:
-            for item in video_files:
-                local_path = os.path.join(self.local_dir, item['path'])
-                
-                if self.download_file(item['id'], local_path):
-                    downloaded_files.append(local_path)
-                
-                pbar.update(1)
-        
-        logger.info(f"Successfully downloaded {len(downloaded_files)} videos")
-        return downloaded_files
-    
-    def list_all_videos(self) -> List[Dict]:
-        """
-        List all videos from Google Drive without downloading
-        
-        Returns:
-            List of video items with metadata (id, name, path)
-        """
-        if not self.service:
-            self.authenticate()
-        
-        logger.info("Listing files in Google Drive folder...")
-        items = self.list_folder_structure()
-        
-        # Filter for video files only
-        video_extensions = ['.mp4', '.mov', '.avi', '.mkv', '.webm']
-        video_files = [
-            item for item in items 
-            if any(item['name'].lower().endswith(ext) for ext in video_extensions)
-        ]
-        
-        logger.info(f"Found {len(video_files)} video files")
-        return video_files
-    
-    def download_single_video(self, item: Dict) -> Optional[str]:
-        """
-        Download a single video from Google Drive
-        
-        Args:
-            item: Video item dict with 'id', 'name', 'path'
-            
-        Returns:
-            Local path to downloaded file or None on failure
-        """
-        local_path = os.path.join(self.local_dir, item['path'])
-        
-        # Skip if already exists
-        if os.path.exists(local_path):
-            logger.debug(f"Video already exists locally: {item['name']}")
-            return local_path
-        
-        # Create directory
-        os.makedirs(os.path.dirname(local_path), exist_ok=True)
-        
-        # Download file
-        if self.download_file(item['id'], local_path):
-            return local_path
-        else:
-            return None

video_analyser/modules/git_ops.py

@@ -1,121 +0,0 @@
-"""
-Git operations for progress tracking
-"""
-
-import os
-import subprocess
-import logging
-import time
-import random
-from pathlib import Path
-
-logger = logging.getLogger(__name__)
-
-
-def git_commit_progress(
-    csv_file: str,
-    log_file: str,
-    progress_file: Path,
-    job_index: int = None,
-    commit: bool = False
-):
-    """
-    Commit progress for a specific job with retry logic for parallel environments.
-    
-    Args:
-        csv_file: Path to CSV file to commit
-        log_file: Path to log file to commit
-        progress_file: Path to progress tracking file
-        job_index: Job index for parallel processing
-        commit: Whether to actually commit (dry-run if False)
-    """
-    if not commit:
-        logger.info("ℹ️ Skipping git commit (use --commit flag to enable).")
-        return
-
-    if not progress_file.exists():
-        logger.info(f"ℹ️ No progress file found. Nothing to commit.")
-        return
-
-    try:
-        # Get branch from environment or default to feature/video-revamp
-        branch = os.getenv('GIT_BRANCH', 'feature/video-revamp')
-        max_retries = 3
-        
-        job_label = f"job {job_index}" if job_index is not None else "main"
-        
-        # 1. Ensure we're on the correct branch
-        logger.info(f"Git: Ensuring we are on branch '{branch}' for {job_label}...")
-        subprocess.run(["git", "fetch", "origin", branch], check=True, capture_output=True)
-        subprocess.run(
-            ["git", "checkout", "-B", branch, f"origin/{branch}"],
-            check=True,
-            capture_output=True
-        )
-        
-        # 2. Stage and commit local changes (force add to include log files)
-        files_to_commit = [csv_file, str(progress_file)]
-        subprocess.run(["git", "add"] + files_to_commit, check=True)
-        
-        commit_msg = f"🎬 Video analysis progress - {job_label}" if job_index is not None else "🎬 Video analysis progress"
-        
-        commit_result = subprocess.run(
-            ["git", "commit", "-m", commit_msg],
-            capture_output=True,
-            text=True
-        )
-        
-        # Check if commit was successful or if nothing to commit
-        if commit_result.returncode != 0:
-            if "nothing to commit" in commit_result.stdout or "nothing to commit" in commit_result.stderr:
-                logger.info(f"ℹ️ No new progress to commit for {job_label}.")
-                return
-            else:
-                raise subprocess.CalledProcessError(
-                    commit_result.returncode,
-                    cmd=commit_result.args,
-                    output=commit_result.stdout,
-                    stderr=commit_result.stderr
-                )
-        
-        # 3. Push with retry loop for race conditions
-        for attempt in range(max_retries):
-            try:
-                # Pull with rebase before push
-                logger.info(f"Git: Pulling with rebase (Attempt {attempt + 1}/{max_retries})...")
-                subprocess.run(
-                    ["git", "pull", "--rebase", "origin", branch],
-                    check=True,
-                    capture_output=True
-                )
-                
-                # Attempt push
-                logger.info(f"Git: Pushing to remote (Attempt {attempt + 1}/{max_retries})...")
-                subprocess.run(
-                    ["git", "push", "origin", branch],
-                    check=True,
-                    timeout=45
-                )
-                
-                logger.info(f"✓ Committed progress successfully for {job_label}")
-                return
-                
-            except (subprocess.CalledProcessError, subprocess.TimeoutExpired) as e:
-                if attempt < max_retries - 1:
-                    # Randomized exponential backoff
-                    sleep_duration = random.uniform(2, 5) * (attempt + 1)
-                    logger.warning(
-                        f"Push failed for {job_label}. Retrying in {sleep_duration:.2f} seconds..."
-                    )
-                    time.sleep(sleep_duration)
-                else:
-                    logger.error(
-                        f"❌ Failed to push progress for {job_label} after {max_retries} attempts."
-                    )
-                    raise
-                    
-    except subprocess.CalledProcessError as e:
-        error_message = e.stderr if e.stderr else e.stdout
-        logger.error(f"❌ Git command failed for {job_label}: {e.cmd}\nError: {error_message}")
-        subprocess.run(["git", "reset", "--hard"])
-        raise

video_analyser/modules/scorer.py

@@ -1,199 +0,0 @@
-"""
-Scorer Module
-Calculate final selection scores using weighted formulas
-"""
-
-import logging
-from typing import Dict
-
-logger = logging.getLogger(__name__)
-
-
-class Scorer:
-    """Calculate weighted scores for video selection"""
-    
-    def __init__(self, config: dict):
-        """
-        Initialize Scorer
-        
-        Args:
-            config: Configuration dictionary with scoring weights
-        """
-        self.luxury_weight = config['scoring']['luxury_weight']
-        self.visual_clarity_weight = config['scoring']['visual_clarity_weight']
-        self.beat_editing_weight = config['scoring']['beat_editing_weight']
-        self.motion_weight = config['scoring']['motion_weight']
-        self.usability_weight = config['scoring']['usability_weight']
-        
-        # Validate weights sum to 1.0
-        total = sum([
-            self.luxury_weight,
-            self.visual_clarity_weight,
-            self.beat_editing_weight,
-            self.motion_weight,
-            self.usability_weight
-        ])
-        
-        if abs(total - 1.0) > 0.01:
-            logger.warning(f"Scoring weights sum to {total}, not 1.0. Normalizing...")
-            norm_factor = 1.0 / total
-            self.luxury_weight *= norm_factor
-            self.visual_clarity_weight *= norm_factor
-            self.beat_editing_weight *= norm_factor
-            self.motion_weight *= norm_factor
-            self.usability_weight *= norm_factor
-    
-    def validate_scores(self, scores: Dict) -> bool:
-        """
-        Validate that all scores are within 0-100 range
-        
-        Args:
-            scores: Dictionary with score values
-            
-        Returns:
-            True if all scores valid, False otherwise
-        """
-        score_fields = [
-            'luxury_score',
-            'visual_clarity_score',
-            'motion_score',
-            'beat_editing_score'
-        ]
-        
-        for field in score_fields:
-            if field not in scores:
-                logger.error(f"Missing score field: {field}")
-                return False
-            
-            if not (0 <= scores[field] <= 100):
-                logger.error(f"Invalid score for {field}: {scores[field]}")
-                return False
-        
-        return True
-    
-    def calculate_usability_bonus(self, has_text: bool, has_faces: bool, 
-                                  usable_for_ads: bool) -> int:
-        """
-        Calculate usability bonus score
-        
-        Args:
-            has_text: Whether video has text/watermarks
-            has_faces: Whether video has visible faces
-            usable_for_ads: Whether video is commercially safe
-            
-        Returns:
-            Usability bonus score (0-100)
-        """
-        score = 100
-        
-        # Penalize text/watermarks
-        if has_text:
-            score -= 40
-        
-        # Slight penalty for faces (can limit usage scenarios)
-        if has_faces:
-            score -= 20
-        
-        # Bonus for ad-safe content
-        if not usable_for_ads:
-            score -= 30
-        
-        return max(0, min(100, score))
-    
-    def determine_ideal_clip_length(self, motion_score: int, energy_level: str, 
-                                    duration: float) -> float:
-        """
-        Determine ideal clip length based on motion and energy
-        
-        Args:
-            motion_score: Motion score (0-100)
-            energy_level: Energy level (low/medium/high)
-            duration: Total video duration
-            
-        Returns:
-            Ideal clip length in seconds
-        """
-        # Base lengths by energy level
-        base_lengths = {
-            'low': 2.0,
-            'medium': 1.5,
-            'high': 1.0
-        }
-        
-        base = base_lengths.get(energy_level, 1.5)
-        
-        # Adjust based on motion
-        if motion_score > 70:
-            # High motion = shorter clips work better
-            base *= 0.8
-        elif motion_score < 30:
-            # Low motion = can hold longer
-            base *= 1.3
-        
-        # Cap at total duration
-        return min(base, duration)
-    
-    def calculate_final_score(self, technical_analysis: Dict, 
-                             ai_analysis: Dict) -> Dict:
-        """
-        Calculate final selection score using weighted formula
-        
-        Args:
-            technical_analysis: Results from VideoAnalyzer (duration only)
-            ai_analysis: Results from AIAnalyzer (all scores from Gemini)
-            
-        Returns:
-            Dictionary with final score and all component scores
-        """
-        # Extract scores from AI analysis (Gemini provides everything)
-        luxury_score = ai_analysis['luxury_score']
-        visual_clarity_score = ai_analysis['visual_clarity_score']
-        beat_editing_score = ai_analysis['beat_editing_score']
-        motion_score = ai_analysis['motion_score']
-        
-        # Calculate usability bonus
-        usability_score = self.calculate_usability_bonus(
-            ai_analysis['has_text_or_watermark'],
-            ai_analysis.get('has_faces_visible', False),
-            ai_analysis['usable_for_ads']
-        )
-        
-        # Validate scores
-        all_scores = {
-            'luxury_score': luxury_score,
-            'visual_clarity_score': visual_clarity_score,
-            'motion_score': motion_score,
-            'beat_editing_score': beat_editing_score
-        }
-        
-        if not self.validate_scores(all_scores):
-            logger.error("Score validation failed")
-            return None
-        
-        # Calculate weighted final score
-        final_score = (
-            luxury_score * self.luxury_weight +
-            visual_clarity_score * self.visual_clarity_weight +
-            beat_editing_score * self.beat_editing_weight +
-            motion_score * self.motion_weight +
-            usability_score * self.usability_weight
-        )
-        
-        # Round to integer
-        final_score = int(round(final_score))
-        
-        # Determine ideal clip length
-        ideal_clip_length = self.determine_ideal_clip_length(
-            motion_score,
-            ai_analysis['energy_level'],
-            technical_analysis['duration_seconds']
-        )
-        
-        return {
-            'luxury_score': luxury_score,
-            'visual_clarity_score': visual_clarity_score,
-            'motion_score': motion_score,
-            'beat_editing_score': beat_editing_score,
-            'final_selection_score': final_score,
-            'ideal_clip_length_seconds': round(ideal_clip_length, 1)
-        }

video_analyser/modules/video_analyzer.py

@@ -1,66 +0,0 @@
-"""
-Video Analyzer Module
-Extracts basic technical metadata from video files
-"""
-
-import cv2
-import logging
-from typing import Dict
-
-logger = logging.getLogger(__name__)
-
-
-class VideoAnalyzer:
-    """Extract basic technical metadata from video files"""
-    
-    def __init__(self, config: dict):
-        """
-        Initialize Video Analyzer
-        
-        Args:
-            config: Configuration dictionary
-        """
-        pass  # No config needed for simple duration extraction
-    
-    def get_video_duration(self, video_path: str) -> float:
-        """
-        Get video duration in seconds
-        
-        Args:
-            video_path: Path to video file
-            
-        Returns:
-            Duration in seconds
-        """
-        try:
-            cap = cv2.VideoCapture(video_path)
-            fps = cap.get(cv2.CAP_PROP_FPS)
-            frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-            cap.release()
-            
-            if fps > 0:
-                duration = frame_count / fps
-                return round(duration, 2)
-            else:
-                logger.warning(f"Could not determine FPS for {video_path}")
-                return 0.0
-                
-        except Exception as e:
-            logger.error(f"Error getting duration for {video_path}: {e}")
-            return 0.0
-    
-    def analyze_video(self, video_path: str) -> Dict:
-        """
-        Perform basic technical analysis of video
-        
-        Args:
-            video_path: Path to video file
-            
-        Returns:
-            Dictionary with basic analysis results
-        """
-        logger.info(f"Analyzing video: {video_path}")
-        
-        return {
-            'duration_seconds': self.get_video_duration(video_path)
-        }

video_analyser/progress/analyzed_videos.txt

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-Copy of Luxury pool night.mp4
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-working.mov
- Rooftop View.MOV
- Friends.mov
- Girl in bed.MOV
- Smoking.MOV
- friends.MOV
-Copy of Walking through cars 02.MOV
-laptop.mov
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video_analyser/requirements.txt

@@ -1,15 +0,0 @@
-# Google Drive API
-google-api-python-client>=2.100.0
-google-auth-httplib2>=0.1.1
-google-auth-oauthlib>=1.1.0
-
-# Gemini AI
-google-generativeai>=0.3.0
-
-# Video Processing (only cv2 for duration extraction)
-opencv-python-headless>=4.8.0
-
-# Configuration & Utilities
-pyyaml>=6.0
-tqdm>=4.66.0
-python-dotenv>=1.0.0