Policy Phantom: Escape the Demonetization Graveyard
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine What I Built I initially designed this application with the intention of entering the Week 3 challenge. However, since I found out about the event late and missed the deadline, I adapted it to perfectly align with the Week 4 theme: Ghost in the Machine . The application is called Policy Phantom , an…
Policy Phantom is a free, open-source AI-based tool designed to scan YouTube video scripts or transcripts for potential monetization policy violations before a video is uploaded. It aims to prevent videos from being caught in the "Demonetization Graveyard." This application takes its name from the analogy of the YouTube algorithm being a "ghost" that silently judges content, and Policy Phantom acts as a tool to read this curse so creators can save their videos.
The application is built on open-source AI models, including Google's Gemma 2 (2B) running locally via Ollama and OpenAI Whisper for audio transcription. Gradio is used for its retro/terminal-themed interface, which orchestrates the entire flow from video upload to visualizing risk scores. An optional Gemini 2.5 Flash API pulls a summary of the latest YouTube policy guidelines and saves them statically in a local policy.json file.
The core intelligence of Policy Phantom, the Gemma 2 model, evaluates the transcript text and provides a risk score and revision suggestions.
Open innovation matters in digital content creation due to privacy, security, and cost concerns. By processing data locally through open-weight models, content remains private, and there are no costs associated with monthly API quotas. The open-weight approach allows for absolute assurance of content privacy, eliminates monthly API costs, and prevents reliance on closed API services.
The developer found the most memorable moment was testing the application using old videos that had been demonetized, as the risk meter lit up red and accurately highlighted the exact phrases, giving the developer a sense of horror reminiscent of the anxiety creators feel when facing YouTube's mysterious strike algorithm.
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