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README.md

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+# 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
+
+```text
+.
+├── 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**
+```json
+{
+  "text": "Long source text...",
+  "max_length": 80
+}
+```
+
+**Response body**
+```json
+{
+  "summary": "Short summary text..."
+}
+```
+
+### `POST /analyze-sentiment`
+Analyzes sentiment and returns typed output.
+
+**Request body**
+```json
+{
+  "text": "I loved how smooth this release felt!"
+}
+```
+
+**Response body**
+```json
+{
+  "sentiment": "positive",
+  "confidence": 0.93,
+  "explanation": "The text expresses clear satisfaction and positive emotion."
+}
+```
+
+## Local Setup
+
+1. Clone and enter project
+```bash
+git clone https://github.com/zainabahmed4626-lab/AIEngineeringW1V2.git
+cd AIEngineeringW1V2
+```
+
+2. Create virtual environment
+```bash
+python -m venv .venv
+# Windows PowerShell
+.venv\Scripts\Activate.ps1
+```
+
+3. Install dependencies
+```bash
+pip install -r requirements.txt
+```
+
+4. Configure environment variables
+```env
+OPENAI_API_KEY=your_openai_key
+# Optional
+OPENAI_MODEL=gpt-4.1-mini
+```
+
+5. Run API
+```bash
+uvicorn app.main:app --reload
+```
+
+Alternative host-style run:
+```bash
+uvicorn main:app --reload
+```
+
+6. Open interactive docs
+- Swagger UI: `http://127.0.0.1:8000/docs`
+
+## Example cURL Commands
+
+```bash
+curl -X GET "http://127.0.0.1:8000/health"
+```
+
+```bash
+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}'
+```
+
+```bash
+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
+
+---
+
+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).