Customer Support ML

A FastAPI-based customer support ticketing system with machine learning integration for ticket classification and summarization.
Includes Celery for background AI tasks, PostgreSQL for storage, Redis for task brokering, and Docker Compose for easy setup.

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Features

• REST API for ticket creation, retrieval, and listing
• Celery for asynchronous AI processing (classification & summarization)
• PostgreSQL database (via SQLAlchemy ORM)
• Redis as Celery broker and result backend
• ML Integration using HuggingFace Transformers
• Docker Compose for local development
• Testing with pytest and FastAPI TestClient

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Setup Instructions

1. Clone the Repository

git clone https://github.com/yourusername/customer-support-ml.git
cd customer-support-ml

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2. Environment Variables

Copy .env.example to .env and adjust as needed, or use the provided .env:

DATABASE_URL=postgresql://admin:password@localhost/support_db
REDIS_URL=redis://redis:6379/0 # For Docker

# For local development, you can use:
REDIS_URL=redis://localhost:6379/0

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3. Start Database and Redis (Docker Compose)

Make sure Docker is running, then:

docker-compose up -d db csml_redis

This will start:

• PostgreSQL on port 5432
• Redis on port 6379

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4. Database Migration

Run Alembic migrations to create tables:

alembic upgrade head

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5. Install Python Dependencies

It’s recommended to use a virtual environment:

#### Using `venv` + `pip`

```sh
python -m venv venv
venv\Scripts\activate  # On Windows
# or
source venv/bin/activate  # On Linux/Mac

pip install -r requirements.txt

Or using pipenv

pip install pipenv
pipenv install --dev
pipenv shell

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### 6. Start the FastAPI Server

```sh
uvicorn main:app --reload

The API will be available at http://localhost:8000.

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7. Start Celery Worker

In a new terminal (with the virtual environment activated):

celery -A app.routes.ticket.celery_app worker --pool=threads --loglevel=info

OR you can also run the dockerized Celery worker:

docker-compose up -d csml_worker

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8. Run Tests

pytest

• Make sure your environment variables are set and the database/Redis are running.
• Tests include core logic, ML integration (mocked), API endpoints, and dataset accuracy.

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Project Structure

Path/Directory	Description
app/models/	SQLAlchemy DB models
app/schemas/	Pydantic validation models
app/services/	AI pipelines (classification & summarization)
app/routes/	API endpoints (POST /requests, GET /requests)
app/utils/	Utility functions (e.g., logger)
scripts/	Dataset ingestion script
tests/	Pytest test cases
alembic/	DB migration files
logs/	Logs directory for AI/DB errors
main.py	FastAPI app entry
Dockerfile	Docker build instructions for the app
docker-compose.yml	Redis, Postgres, Celery, FastAPI stack
.env	DB + secret config
requirements.txt	Dependencies

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Useful Commands

• Run migrations: alembic upgrade head
• Start API server: uvicorn main:app --reload
• Start Celery worker: celery -A app.routes.ticket.celery_app worker --pool=threads --loglevel=info
• Run tests: pytest

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Notes

• For local development, ensure PYTHONPATH is set to the project root if you encounter import errors:

  set PYTHONPATH=%CD%  # Windows
  export PYTHONPATH=$(pwd)  # Linux/Mac

• Adjust .env variables if running services in Docker vs. locally.
• For production, configure secure credentials and proper environment separation.

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License

MIT License

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