Pierpaolo Romanelli
Softwarearchitekt & KI-Content-Ersteller

# 100% Python Local Pipeline - Zero Remote API Dependencies
import torch
from local_ai import TTSLocalSynthesizer, TalkingHeadAnimator, VideoComposer

def generate_multilingual_affirmation(text: str, lang: str = 'it'):
    device = 'cuda' if torch.cuda.is_available() else 'cpu'
    print(f"[AI Core] Initializing Local Inference on {device}...")
    
    # Generate Voice Offline
    tts_engine = TTSLocalSynthesizer(model="vits-multilingual-v2", device=device)
    audio_wav = tts_engine.synthesize(text, lang=lang)
    
    # Animate Character & Synchronize Lips locally
    animator = TalkingHeadAnimator(resolution=(1080, 1920), fps=30)
    talking_frames = animator.render_lipsync(audio_wav, avatar_model="realistic_character.pt")
    
    # Final 9:16 Video Export
    composer = VideoComposer(resolution=(1080, 1920), format="mp4")
    final_clip = composer.assemble(frames=talking_frames, audio=audio_wav, background="ambient_nature.mp4")
    return final_clip.export("output_affirmations.mp4")

// PiTools Web Worker Multi-Thread Engine (0% Main Thread Freeze)
self.onmessage = function(e) {
    const { action, buffer } = e.data;
    if (action === 'parse') {
        console.time('WebWorker_Parse_100k');
        
        // Ingest 100,001 rows in background
        const workbook = XLSX.read(buffer, { type: 'array', dense: true });
        const sheet = workbook.Sheets[workbook.SheetNames[0]];
        const rawData = XLSX.utils.sheet_to_json(sheet, { header: 1 });
        
        // Stream optimized cell matrix to Luckysheet virtual canvas
        self.postMessage({
            success: true,
            rowCount: rawData.length,
            data: rawData
        });
        console.timeEnd('WebWorker_Parse_100k'); // ~680ms with 0 FPS drop
    }
};

# Sport Data Arena - ATP Match Analysis & Broadcast Engine
class TennisMatchAnalyzer:
    def __init__(self, player_a="Ben Shelton", player_b="Jenson Brooksby"):
        self.match_data = {
            "score": "2 - 0",
            "first_serve_won": "88%",
            "max_speed_kmh": 236.5,
            "break_points_saved": "5/5 (100%)"
        }
    
    def render_broadcast_overlay(self):
        print(f"🏆 Winner: {self.player_a} (Score: {self.match_data['score']})")
        return self.match_data