dc.contributor.author	Tala AbuSoud
dc.contributor.author	Raya Thawabe
dc.date.accessioned	2025-07-14T07:35:02Z
dc.date.available	2025-07-14T07:35:02Z
dc.date.issued	2025
dc.description.abstract	The podcast is among the strongest platforms for education, entertainment, and information. With this aim, Athena has been developed as a niche audio content platform to empower independent creators and provide a richer listening experience for the audience. Unlike the mainstream platforms, Athena caters to different user needs with a blend of localized and niche-specific features. Athena was developed via an iterative life cycle model approach, planning, and initial research to understand user and market needs, followed by UI/UX design, back-end and front-end development, and appropriate testing. The system implements Flutter in the mobile and web front-end, Node.js and PostgreSQL in the back-end, and two Python-based FastAPI microservices for content recommendation and audio fingerprint matching. Further with real-time transcription supported by OpenAI Whisper and recommendation engine powered by TF-IDF and cosine similarity, audio fingerprinting is done by Dejavu, while a GPT chatbot supports summary, translation, and QA functionalities. OpenAI tools moderate user reviews; Cloudinary handles media streaming and caching. Two FastAPI-based microservices were developed: a recommender system that uses user interactions, text similarity, and mood data, and a matcher system that uses fingerprinting to identify podcasts from short audio clips. Additional features include notifications, JWT-secured authentication, an admin dashboard to explore app and user insights, manage ads, and channel approvals, and a YouTube
dc.identifier.uri	https://hdl.handle.net/20.500.11888/20218
dc.supervisor	DR. Samer Arandi
dc.title	Athena

Athena

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