Zainab4626/AIEngineeringW1V2

Summarize & Sentiment API

Production-style FastAPI service that exposes LLM-powered text summarization and sentiment analysis endpoints using the OpenAI Responses API.

Built as an AI engineering portfolio project to demonstrate clean API design, schema validation, resilient model-output parsing, and deployment-ready structure.

Why This Project

This repository showcases practical applied-AI backend skills recruiters and hiring managers look for:

• Designing typed, testable API contracts with Pydantic
• Integrating LLMs into backend services (not just notebooks)
• Hardening model outputs with parsing/validation logic
• Implementing structured JSON logging (structlog) for observability
• Organizing code into routes, services, and schemas for maintainability

Features

• Health endpoint for service uptime checks
• Summarization endpoint with length control
• Sentiment endpoint returning structured JSON (sentiment, confidence, explanation)
• Strict validation and normalization of model sentiment labels
• Environment-driven config (OPENAI_API_KEY, optional OPENAI_MODEL)
• Render-friendly entrypoint via main.py (uvicorn main:app)

Tech Stack

• Python
• FastAPI
• OpenAI Python SDK (responses.create)
• Pydantic v2
• Structlog
• Uvicorn
• python-dotenv

Project Structure

.
├── app/
│   ├── main.py                 # FastAPI app factory, logging setup
│   ├── routes/
│   │   ├── health.py           # GET /health
│   │   ├── summarize.py        # POST /summarize
│   │   └── sentiment.py        # POST /analyze-sentiment
│   ├── schemas/
│   │   └── models.py           # Request/response models
│   └── services/
│       ├── summarize.py        # LLM summarization logic
│       └── sentiment.py        # LLM sentiment + robust JSON parsing
├── main.py                     # host entrypoint (uvicorn main:app)
└── requirements.txt

API Endpoints

GET /health

Returns status and timestamp.

POST /summarize

Generates a concise summary.

Request body

{
  "text": "Long source text...",
  "max_length": 80
}

Response body

{
  "summary": "Short summary text..."
}

POST /analyze-sentiment

Analyzes sentiment and returns typed output.

Request body

{
  "text": "I loved how smooth this release felt!"
}

Response body

{
  "sentiment": "positive",
  "confidence": 0.93,
  "explanation": "The text expresses clear satisfaction and positive emotion."
}

Local Setup

1. Clone and enter project

git clone https://github.com/zainabahmed4626-lab/AIEngineeringW1V2.git
cd AIEngineeringW1V2

2. Create virtual environment

python -m venv .venv
# Windows PowerShell
.venv\Scripts\Activate.ps1

3. Install dependencies

pip install -r requirements.txt

4. Configure environment variables

OPENAI_API_KEY=your_openai_key
# Optional
OPENAI_MODEL=gpt-4.1-mini

5. Run API

uvicorn app.main:app --reload

Alternative host-style run:

uvicorn main:app --reload

6. Open interactive docs

• Swagger UI: http://127.0.0.1:8000/docs

Example cURL Commands

curl -X GET "http://127.0.0.1:8000/health"

curl -X POST "http://127.0.0.1:8000/summarize" \
  -H "Content-Type: application/json" \
  -d '{"text":"FastAPI makes building APIs fast and maintainable.","max_length":20}'

curl -X POST "http://127.0.0.1:8000/analyze-sentiment" \
  -H "Content-Type: application/json" \
  -d '{"text":"The onboarding flow is confusing and frustrating."}'

Engineering Notes

• Sentiment service includes normalization for common label variants (pos, mixed, etc.) before mapping to strict enum values.
• Services fail fast when required env vars are missing.
• Route layer catches exceptions and returns controlled HTTP 500 errors while logging structured failure context.

Recruiter Snapshot

This project demonstrates readiness for roles involving:

• AI/LLM backend engineering
• API productization of GenAI features
• Reliable model-in-the-loop service development
• Deployable Python service architecture

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If you are reviewing this repository for a role, I can also provide a short architecture walkthrough and trade-off discussion (latency, cost, and model reliability choices).