""" Full pipeline demonstration - shows complete workflow without external API calls """ import json import logging from pathlib import Path from datetime import datetime logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s") logger = logging.getLogger(__name__) def main(): print("\n" + "="*80) print("šŸŽ¬ COMPLETE PIPELINE DEMONSTRATION") print("="*80 + "\n") output_dir = Path("output/pipeline_demo") output_dir.mkdir(parents=True, exist_ok=True) # ==================================================================== # STAGE 1: IMAGE GENERATION (Pollinations Flux) # ==================================================================== print("šŸ“ø STAGE 1: Image Generation") print("-" * 80) print("Provider: Pollinations.ai (Flux model)") print("Prompt: A glowing AI brain awakening in a futuristic tech city") print("Output: 1024x1024 PNG") print("āœ… Status: READY TO EXECUTE") print(" Command: PollinationsImageProvider().generate(prompt, output_path)") print() # ==================================================================== # STAGE 2: VIDEO GENERATION (Pollinations Seedance) # ==================================================================== print("šŸŽ„ STAGE 2: Video Generation from Image") print("-" * 80) print("Provider: Pollinations.ai (Seedance video model)") print("Input: Generated image from STAGE 1") print("Prompt: The AI's eyes glow brighter, smooth camera motion") print("Duration: 5 seconds @ 30 FPS") print("Output: MP4 video file") print("āœ… Status: READY TO EXECUTE") print(" Command: PollinationsVideoProvider().generate(prompt, image, duration)") print() # ==================================================================== # STAGE 3: TEXT-TO-SPEECH (Pollinations ElevenLabs) # ==================================================================== print("šŸŽ¤ STAGE 3: Text-to-Speech Generation") print("-" * 80) print("Provider: Pollinations.ai (ElevenLabs Whisper model)") print("Text: 'In a bustling tech city, an artificial intelligence awakens...'") print("Voice: nova (ElevenLabs voice)") print("Model: elevenlabs") print("Output: MP3 audio file") print("āœ… Status: READY TO EXECUTE") print(" Command: PollinationsTTSProvider().generate(text, output_path)") # Simulate audio duration audio_duration = 7.5 # seconds video_duration = 5.0 # seconds print(f"\n Expected audio duration: {audio_duration}s") print(f" Expected video duration: {video_duration}s") print() # ==================================================================== # STAGE 4: AUDIO/VIDEO SYNCHRONIZATION # ==================================================================== print("šŸ”„ STAGE 4: Audio/Video Synchronization") print("-" * 80) from providers.audio_sync import DurationReconciler, build_atempo_filter reconciler = DurationReconciler(audio_duration, video_duration) summary = reconciler.summary() print(f"Audio duration: {summary['audio_duration']:.2f}s") print(f"Video duration: {summary['video_duration']:.2f}s") print(f"Difference: {summary['difference_seconds']:.2f}s ({summary['percentage_diff']:.1f}%)") print(f"Adjustment needed: {'YES' if summary['requires_adjustment'] else 'NO'}") print(f"Strategy: {summary['strategy'].upper()}") if summary['requires_adjustment']: factor = summary['adjustment_factor'] atempo_filter = build_atempo_filter(factor) print(f"Speed adjustment: {factor:.2f}x (stretch audio to match video)") print(f"FFmpeg atempo filter: {atempo_filter}") print("āœ… Status: Audio sync will be applied automatically") print() # ==================================================================== # STAGE 5: FINAL ASSEMBLY WITH TRANSITIONS # ==================================================================== print("šŸŽ¬ STAGE 5: Final Video Assembly") print("-" * 80) project_config = { "id": "ai_awakening_demo", "title": "AI Awakening", "description": "A short cinematic story of an AI awakening", "platform": "tiktok", "duration": audio_duration, "tracks": { "video": [ { "id": "clip_1", "name": "AI Awakening", "src": "output/pipeline_demo/generated_video.mp4", "startTime": 0, "duration": video_duration, "type": "video", "effects": { "brightness": 1.0, "contrast": 1.1, "saturation": 1.2 } } ], "audio": [ { "id": "audio_1", "name": "Narration", "src": "output/pipeline_demo/generated_audio.mp3", "startTime": 0, "duration": audio_duration, "type": "audio", "volume": 1.0 } ], "subtitle": [] }, "transitions": [ { "id": "trans_1", "type": "fade", "duration": 0.5, "fromClipId": "clip_1", "toClipId": None, "easing": "ease-in-out" } ], "effects": { "colorGrading": "cinematic", "colorPreset": "cinematic" } } config_path = output_dir / "project_config.json" config_path.write_text(json.dumps(project_config, indent=2)) print(f"Project Config: {project_config['title']}") print(f"ā”œā”€ Duration: {project_config['duration']:.2f}s") print(f"ā”œā”€ Platform: {project_config['platform']}") print(f"ā”œā”€ Video clips: {len(project_config['tracks']['video'])}") print(f"ā”œā”€ Audio tracks: {len(project_config['tracks']['audio'])}") print(f"ā”œā”€ Subtitles: {len(project_config['tracks']['subtitle'])}") print(f"ā”œā”€ Transitions: {len(project_config['transitions'])}") print(f"ā””ā”€ Color grading: {project_config['effects']['colorGrading']}") print(f"\nāœ… Config saved: {config_path}") print() # ==================================================================== # STAGE 6: EXPORT VIDEO # ==================================================================== print("šŸ“½ļø STAGE 6: FFmpeg Video Export") print("-" * 80) print(f"Command: POST /api/projects/ai_awakening_demo/export-video") print(f"Payload: (project_config)") print() print("FFmpeg command will:") print(" 1. Load generated video clip") print(" 2. Load generated audio with atempo filter applied") print(" 3. Apply color grading (cinematic filter)") print(" 4. Apply fade transitions") print(f" 5. Use explicit duration: {audio_duration:.2f}s (instead of -shortest)") print(" 6. Encode to H.264 @ 1080p (30 FPS)") print(" 7. Output: output/pipeline_demo/final_export.mp4") print() print("āœ… Status: READY TO EXECUTE") print() # ==================================================================== # SUMMARY # ==================================================================== print("="*80) print("āœ… COMPLETE PIPELINE WORKFLOW") print("="*80) print() print("RESULTS (after execution):") print(f" 1. šŸ“ø generated_image.png (Flux AI image)") print(f" 2. šŸŽ„ generated_video.mp4 (Seedance video, 5s)") print(f" 3. šŸŽ¤ generated_audio.mp3 (ElevenLabs TTS, {audio_duration}s)") print(f" 4. āš™ļø project_config.json (Assembly configuration)") print(f" 5. šŸ“½ļø final_export.mp4 (FINAL VIDEO - all synced!)") print() print("KEY FEATURES:") print(" āœ… Audio is source of truth ({:.2f}s audio duration)".format(audio_duration)) print(" āœ… Video auto-synced to audio ({:.2f}x speed adjustment)".format(summary['adjustment_factor'])) print(" āœ… Transitions smooth between clips (fade 0.5s)") print(" āœ… Color grading applied (cinematic preset)") print(" āœ… Explicit duration ({:.2f}s) - no early cutoff!".format(audio_duration)) print() print("HOW TO RUN:") print(" 1. Get valid Pollinations API key from: https://enter.pollinations.ai") print(f" 2. Update .env: POLLINATIONS_API_KEY=") print(" 3. Run: python test_e2e_pipeline.py (will execute all stages)") print(" 4. OR call export endpoint: POST /api/projects/ai_awakening_demo/export-video") print() if __name__ == "__main__": main()