AI Mood-Based Music Selector
Budget: $30 – $250 USD
I’m building a web feature that can read whatever a visitor types into the site’s chat box, gauge their emotional tone through text-analysis NLP, and immediately queue up music that matches or lifts that mood.
Here’s the flow I have in mind. The user interacts with the live-chat on our site. Every message is piped to your classifier, which returns a mood label or valence/arousal scores. Based on that output, the system picks an appropriate track or playlist from a streaming service (Spotify or a royalty-free catalogue—whichever is easier to wire up first) and starts playback without noticeable delay.
I’m set on text analysis as the detection method; no facial or voice inputs are needed right now. Likewise, I don’t need social-media or email mining—only the chat messages typed on the website itself. If the architecture leaves room to plug in additional sources later, that’s a bonus but not essential for this milestone.
Deliverables
• A trained or fine-tuned NLP model (Python preferred, Hugging Face, spaCy or similar) that outputs mood categories or sentiment scores.
• An API or lightweight microservice that exposes that model for our front-end.
• A front-end snippet (JavaScript/React welcome) that hooks into the existing chat widget, calls the API, and triggers music playback.
• Setup script / Dockerfile plus concise README so I can redeploy on our server.
Acceptance criteria
• Mood detection accuracy is demonstrably above baseline on a provided validation set.
• End-to-end latency (message → music) stays under two seconds on test hardware.
• Clean hand-off of code with clear, commented sections and instructions.
If you’ve built similar sentiment or recommendation engines, tell me how you approached the feature-selection and evaluation. Looking forward to seeing what you can create.
Here’s the flow I have in mind. The user interacts with the live-chat on our site. Every message is piped to your classifier, which returns a mood label or valence/arousal scores. Based on that output, the system picks an appropriate track or playlist from a streaming service (Spotify or a royalty-free catalogue—whichever is easier to wire up first) and starts playback without noticeable delay.
I’m set on text analysis as the detection method; no facial or voice inputs are needed right now. Likewise, I don’t need social-media or email mining—only the chat messages typed on the website itself. If the architecture leaves room to plug in additional sources later, that’s a bonus but not essential for this milestone.
Deliverables
• A trained or fine-tuned NLP model (Python preferred, Hugging Face, spaCy or similar) that outputs mood categories or sentiment scores.
• An API or lightweight microservice that exposes that model for our front-end.
• A front-end snippet (JavaScript/React welcome) that hooks into the existing chat widget, calls the API, and triggers music playback.
• Setup script / Dockerfile plus concise README so I can redeploy on our server.
Acceptance criteria
• Mood detection accuracy is demonstrably above baseline on a provided validation set.
• End-to-end latency (message → music) stays under two seconds on test hardware.
• Clean hand-off of code with clear, commented sections and instructions.
If you’ve built similar sentiment or recommendation engines, tell me how you approached the feature-selection and evaluation. Looking forward to seeing what you can create.