Nepali Xvideyocom Better Portable ⚡ Pro

| Component | Tech Stack (suggested) | Key Steps | |-----------|-----------------------|-----------| | | Python + langdetect , pydub , pre‑trained Whisper model for speech‑to‑text. | • Run detection on upload. • Store language tag in video metadata. | | Automatic Subtitles | Whisper (OpenAI) → Translate via Google Cloud Translation API → Store as VTT/WEBVTT. | • Generate subtitles on the fly (or batch‑process). • Offer “Download Subtitles” button. | | AI Content Classification | TensorFlow / PyTorch model fine‑tuned on a labelled dataset (e.g., SafeSearch, NSFW). | • Score each video (0–1). • Use threshold to decide “Safe”, “Review”, “Restricted”. | | Community Flagging System | Backend: Node.js + Express + MongoDB (or PostgreSQL). Frontend: React/Vue. | • UI button “Report”. • Store reports, aggregate, auto‑escalate after N flags. | | Recommendation Engine | Apache Spark MLlib or Scikit‑Learn for collaborative filtering; Elasticsearch for fast look‑ups. | • Build user‑item matrix (watch, likes, skips). • Combine with content‑based scores (language, genre). | | Geo‑Aware Trend Tracker | Redis for real‑time counters; IP‑based location lookup (MaxMind GeoIP). | • Increment counters per video per region. • Refresh “Trending in Kathmandu” list every hour. | | Localization Layer | i18next (React) or ngx‑translate (Angular). Translation files in Nepali (UTF‑8). | • Externalise all UI strings. • Provide fallback to English. | | Privacy‑First Data Handling | GDPR‑style consent banner; anonymise IPs; allow opt‑out of personalized recommendations. | • Store only hashed user IDs for recommendation models. • Offer “Safe‑Mode” toggle. |

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