Dear *Giannis Prokopiou *and *Thanos Aidinis*, I hope you both are doing well. I apologize for mailing you multiple times, but I wanted to share my thoughts on our current approach and seek your guidance. I’ve been working on the mood detection model and opted for a local approach instead of using an API. Considering the rate limits and potential costs associated with APIs—especially since we’re already using one and might need another for song recommendation—I felt that managing everything locally would be more efficient. Currently, I'm using a model based on the *"**facebook/bart-large-mnli**"* pipeline for mood detection. By mood, I mean identifying emotions like *sad*, *happy*, *angry*, etc from the user’s input. As for intent detection, we aim to classify whether the input is a *mood expression*, *direct song request*, or *mixed*. For instance, if a user says, *"I’m feeling low, play something upbeat,"* it would be classified as *mixed*, while *"Play something by The Weeknd"* would be a *direct request*. For the song recommendation model, I’m currently exploring options like *RecBole, Reccobeats* and *LightFM*. Since scalability and data availability are key considerations, I would greatly value your input on which of these models—or any other alternative—you think would be more suitable for our use case. I’m confident that I can do more research on this, but I’ve already spent significant time on this, and I don’t want to waste any more if it’s not the right path altogether. Your thoughts and suggestions would really help us make informed decisions. Thank you once again for your guidance and support! Best regards, Sai Sri Lekha kslekha02 [ at ] gmail [ dot ] com
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