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Project: “Social Media Sentiment Analysis” (Colab → UI + Deployment)

  • 2 notebooks:

    • model development

    • playground/testing (.pkl, .h5)

  • Models:

    • Logistic Regression

    • Naive Bayes

    • ANN

  • Future:

    • multiple model versions

    • different optimizers/vectorizers/techniques

    • showcase all models in deployment

  • Stack:

    • Next.js

    • frontend/ + backend/

    • FastAPI backend

    • simple API flow

    • frontend fetch requests

  • Later additions:

    • Clerk auth

    • Supabase DB

    • user history storage

  • Goal:

    • scalable

    • clean architecture

    • conventional/best practices

  • Planned architecture:

    • (inner files/folders may expand later)

social-sentiment/

├── frontend/ # Next.js App │ ├── app/ │ │ ├── page.tsx # Landing/Home │ │ ├── analyze/page.tsx # Main analysis UI │ │ ├── compare/page.tsx # Model comparison view │ │ ├── history/page.tsx # User history (after auth) │ │ └── api/ # Optional: lightweight proxy │ ├── components/ │ ├── lib/ │ └── package.json ├── backend/ # FastAPI │ ├── app/ │ │ ├── main.py # FastAPI app entry │ │ ├── models/ # Pydantic schemas │ │ ├── routers/ # API routes │ │ └── services/ # Business logic │ ├── ml/ # ML models & utils │ │ ├── models/ # .pkl, .h5 files (versioned) │ │ └── preprocessors/ # Vectorizers, etc. │ ├── requirements.txt │ └── Dockerfile # For containerization ├── docker-compose.yml # Local dev orchestration ├── .env.example # Environment template └── README.md

[NOTE: Lets go only one step at a time, don't give entire code. give even the terminal commands and working on external services.] Its Windows Device. What's done: Directory skeleton + git init. nothing else.