AI-Assistant

A full-stack intelligent agent combining machine learning models (data processing + inference) with a modern, type-safe web interface (FastAPI backend + Next.js frontend). The repository contains separate backend and frontend modules and is built with Python, TypeScript, and common data libraries. ([GitHub][1])

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Table of contents

• Overview

• Features

• Tech stack

• Architecture & repo layout

• Prerequisites

• Installation & run (local)

  • Backend
  • Frontend

• Environment variables (example)

• Development workflow

• Testing

• Contributing

• License & contact

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Overview

AI-Assistant is an intelligent agent project that demonstrates a production-oriented layout: asynchronous FastAPI backend for serving inference and data pipelines, and a Next.js + TypeScript frontend for a responsive UI. It’s intended as a starter template or reference for deploying a small ML-enabled web service. ([GitHub][1])

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Features

• Low-latency AI inference served from FastAPI (async). ([GitHub][1])
• Data processing pipelines using NumPy / Pandas. ([GitHub][1])
• Type-safe frontend using Next.js + TypeScript and responsive UI components. ([GitHub][1])
• Clear separation between backend and frontend for independent development and deployment.

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Tech stack

• Backend: FastAPI (Python), Uvicorn. ([GitHub][1])
• Frontend: Next.js with TypeScript (Bootstrap for UI). ([GitHub][1])
• Data & ML: NumPy, Pandas (example libs shown in repo). ([GitHub][1])

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Architecture & repo layout

Typical structure (reflects this repository):

AI-Assistant/
├─ Backend_assistantai/       # FastAPI backend code
├─ Frontend_AssistantAI/      # Next.js frontend code
├─ .gitignore
├─ readme.md

Adjust paths if your local folder names differ. The repo contains backend and frontend folders that should be started independently. ([GitHub][1])

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Prerequisites

• Python 3.10+ (or compatible 3.x line)
• Node.js 18+ and npm/yarn (for Next.js dev server)
• Git (to clone the repo)

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Installation & run (local)

Note: Commands below assume you are at the repository root.

Backend

1. Change into backend directory:

cd Backend_assistantai

2. Create & activate a virtual environment:

python -m venv venv
# macOS / Linux
source venv/bin/activate
# Windows (PowerShell)
venv\Scripts\Activate.ps1

3. Install dependencies:

pip install -r requirements.txt

4. Run the backend (development):

uvicorn main:app --reload

This should start FastAPI (Uvicorn) on the default port (8000). Adjust module path if your main app file has a different name. ([GitHub][1])

Frontend

1. Change into frontend directory:

cd ../Frontend_AssistantAI

2. Install packages:

npm install
# or
yarn

3. Run dev server:

npm run dev
# or
yarn dev

This will start the Next.js dev server (commonly on http://localhost:3000). Update package.json scripts if different.

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Environment variables (example)

Create a .env file at the root of backend/frontend as required by your code. Example placeholders:

# Backend
PORT=8000
DATABASE_URL=postgresql://user:pass@localhost:5432/dbname
SECRET_KEY=your-secret-key
# Optional ML/3rd-party keys
OPENAI_API_KEY=sk-...

Adjust names & values to match the actual code that reads environment values. If you want, I can scan the repo to enumerate exact env vars used (I will read Backend_assistantai source files to list them).

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Development workflow

• Work on backend API endpoints inside Backend_assistantai/.
• Work on UI components & pages inside Frontend_AssistantAI/.
• Keep API contracts (paths, request/response JSON) documented and stable while iterating on UI.
• Use type hints + Pydantic models in backend and TypeScript interfaces in frontend to maintain type safety.

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Testing

• Add/extend unit tests in the backend (e.g., using pytest) and frontend (e.g., jest / testing-library) as needed.
• For API testing, tools like httpie, curl, or Postman are useful:

# Example: check health endpoint
curl http://localhost:8000/health

(Replace /health with whatever endpoint the repo exposes.)

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Contributing

1. Fork the repo.
2. Create a feature branch: git checkout -b feat/your-feature
3. Commit changes & open a PR describing the change and motivation.
4. Follow consistent code style: lint Python (e.g., black, ruff), and TypeScript linting for the frontend.

If you want, I can prepare a CONTRIBUTING.md template and a recommended pre-commit configuration.

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Next improvements & suggestions (quick wins)

• Add example API documentation (OpenAPI/Swagger is available with FastAPI — link it in README).
• Provide docker-compose for local full-stack run (Postgres + backend + frontend).
• Add CI (GitHub Actions) for linting/testing on push/PR.
• Add a short demo GIF or screenshots showing the UI in action.