🧠 Smart Conversational Assistant with Memory

A Django + Django REST Framework-based AI chatbot that integrates HuggingFace models to hold memory-aware conversations. Frontend is designed to impress.

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📦 Project Stack

• Backend: Django, Django REST Framework
• Frontend: HTML/CSS + Bootstrap (or Gradio/React optional)
• AI Model: HuggingFace Transformers (e.g., DialoGPT, Falcon)
• Database: PostgreSQL (recommended for Render)
• Deployment: Render.com

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🚀 Getting Started (Local Development)

1. Clone the repo

git clone https://github.com/yourusername/smart-assistant.git
cd smart-assistant

2. Create virtual environment & install requirements

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

3. Set up environment variables

Create a .env file:

SECRET_KEY=your-secret-key
DEBUG=True
ALLOWED_HOSTS=localhost,127.0.0.1
HUGGINGFACE_MODEL=gpt2

4. Apply migrations and run

python manage.py migrate
python manage.py runserver

Visit: http://127.0.0.1:8000

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🔌 API Endpoints (DRF)

Endpoint	Method	Description
/api/start-session/	POST	Start a new chat session
/api/chat/	POST	Send a message and receive AI response
/api/history/	GET	View full message history

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🧠 AI Model (Transformers)

In transformers_model/model.py, you can load your model like this:

from transformers import pipeline
chatbot = pipeline("text-generation", model="gpt2")

Replace with DialoGPT, Falcon, or any supported conversational model.

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🌐 Deployment to Render.com

1. Push code to GitHub

Make sure your code is pushed to a public or private GitHub repo.

2. Create a Render Web Service

• Go to Render.com
• Click New Web Service
• Connect to your GitHub repo
• Use the following build settings:

Build Command:

pip install -r requirements.txt && python manage.py collectstatic --noinput && python manage.py migrate

Start Command:

gunicorn config.wsgi:application

Environment:

• Add environment variables in the Render dashboard (SECRET_KEY, DEBUG=False, DATABASE_URL, etc.)

PostgreSQL DB:

• Add a Render PostgreSQL service and connect DATABASE_URL to your Django settings.

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📁 Folder Structure

smart_assistant/
├── manage.py
├── requirements.txt
├── README.md

├── config/                        # Django project config
│   ├── __init__.py
│   ├── settings.py               # Main settings
│   ├── urls.py                   # Project-level routing
│   └── wsgi.py / asgi.py

├── chatbot/                      # Main app
│   ├── __init__.py
│   ├── apps.py
│   ├── models.py                # DB models: Session, Message, UserMemory
│   ├── views.py                 # Django views
│   ├── api_views.py             # Chat API endpoint
│   ├── urls.py                  # App-level URLs
│   ├── forms.py                 # Input forms (if using Django forms)

├── templates/                   # HTML templates
│   ├── base.html                # Layout base
│   └── chat.html                # Chat interface

├── static/                      # Static files (CSS, JS, images)
│   ├── css/
│   ├── js/
│   └── img/

├── transformers_model/          # AI Model logic
│   ├── __init__.py
│   ├── model.py                 # Load + respond using HuggingFace

├── tests/                       # Unit and integration tests
│   ├── __init__.py
│   ├── test_models.py
│   ├── test_views.py
│   └── test_chatbot.py

├── .env                         # Secrets for local dev
└── .gitignore

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✅ To Do Checklist for Students

• Implement models and API endpoints
• Add HuggingFace model integration
• Design sleek chat UI (Tailwind or Gradio)
• Deploy to Render with PostgreSQL
• Submit GitHub + live demo + README + short video

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👏 Mentorship Tip

Make the assistant charming, like a product you want to show off. Think beyond basic Q&A: memory, personality, even jokes or Easter eggs!