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MoodSense AI (ML and FastAPI and Gradio, Deployed on Hugging Face)

MoodSense AI (ML and FastAPI and Gradio, Deployed on Hugging Face)

by aman179102·Apr 11, 2026·1 point·1 comment
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AI Analysis

●MidShip It

Text sentiment analysis when IBM Watson and Google NLP already dominate this space.

Strengths
  • •FastAPI backend with Gradio UI means both programmatic and interactive access.
  • •Probability distribution output shows model confidence, not just labels.
Weaknesses
  • •Standard scikit-learn + LightGBM pipeline with no novel architecture or fine-tuning approach.
  • •No differentiation from established sentiment analysis APIs with better accuracy.
Category
AI/ML
Target Audience

Developers learning NLP, hobbyists building mood tracking apps

Similar To

IBM Watson Tone Analyzer · Google Cloud Natural Language · Azure Text Analytics

Post Description

I built MoodSense AI, an end-to-end NLP project that detects mood from text and provides basic recommendations.

It classifies text into multiple moods (happy, sad, anxious, etc.), shows confidence and probability distribution, and exposes both an API (FastAPI) and a UI (Gradio).

The goal was to go beyond just training a model and make something actually usable.

Live demo: https://huggingface.co/spaces/aman179102/moodsense-ai

Source: https://github.com/aman179102/moodsense-ai

Tech stack: Python, scikit-learn, LightGBM, spaCy, FastAPI, Gradio

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