Sentiment Analysis App
Part of the AI Engineer Roadmap
In short
A model in a notebook helps only you. Wrap it in a small web app and anyone can paste a review and see the mood instantly. This project takes you from model to working product.
A small deployed app that classifies user-submitted text sentiment in real time — your first full NLP project shipped end-to-end.
Think of it like this
The feedback machine at the counter
Some shops have a little machine at the exit with smiley and frowny buttons. Press one and the manager's screen updates instantly. Your app is a smarter version: people type what they think, and the model decides which button they meant.
Watch it happen
How the parts talk
types a review
1/6The user pastes 'Delivery was late but the food was great' and clicks Analyse.
The big ideas
What you need to know
Load the model once
Loading takes seconds. Do it at startup, not inside the request handler, or every request will crawl.
A clean API
One POST /predict endpoint that takes text and returns label and score. FastAPI gives you docs at /docs for free.
A simple front end
A plain HTML form, or Streamlit or Gradio for a quick Python-only UI. Show the score, not just the label.
Deploy it
Hugging Face Spaces can host small Gradio or Streamlit demos. Check the current free limits before you rely on them.
See it in code
Walk through the code
1from fastapi import FastAPI2from pydantic import BaseModel3from transformers import pipeline4 5app = FastAPI()6clf = pipeline("sentiment-analysis",7 model="distilbert/distilbert-base-uncased-finetuned-sst-2-english") # load once8 9class Review(BaseModel):10 text: str11 12@app.post("/predict")13def predict(review: Review):14 result = clf(review.text, truncation=True)[0] # long text is cut to the model's limit15 return {"label": result["label"], "score": round(result["score"], 3)}16# run: uvicorn app:app --reload1/3Create the app and load the model once at startup.
The heart of the app. Save it as app.py, pip install fastapi uvicorn transformers torch, and open /docs to test it.
Save yourself the pain
Common mistakes
Oops
Loading the model inside the predict function.
Do this instead
Every request would reload hundreds of megabytes. Load it once at module level or in a startup hook.
Oops
Showing only 'POSITIVE' even when the score is 0.51.
Do this instead
Show the confidence, and label low-confidence results as 'unsure' so users don't over-trust the app.
Your mission
Milestone 1: a working API
Quick check
Why load the sentiment model at startup instead of inside each request?
Go deeper
- 1Fine-tune a transformer sentiment model and wrap it in a simple API (FastAPI).
- 2Build a minimal frontend (or Streamlit UI) so anyone can try it live.
- 3Deploy it publicly and add a short write-up of your approach.
More projects at this stop
Text Summarizer
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