https://github.com/parvvaresh/deepspeek
This is a simple chatbot application built using Flask and DeepSeek AI. The application includes user authentication, a chat interface, and a deep learning model for generating chatbot responses.
https://github.com/parvvaresh/deepspeek
chatbot deepseek flask ollama sqllite
Last synced: 6 months ago
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This is a simple chatbot application built using Flask and DeepSeek AI. The application includes user authentication, a chat interface, and a deep learning model for generating chatbot responses.
- Host: GitHub
- URL: https://github.com/parvvaresh/deepspeek
- Owner: parvvaresh
- Created: 2025-02-03T19:55:20.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-03-04T05:48:56.000Z (7 months ago)
- Last Synced: 2025-03-04T06:30:48.516Z (7 months ago)
- Topics: chatbot, deepseek, flask, ollama, sqllite
- Language: HTML
- Homepage:
- Size: 8.79 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Flask Chatbot Application
## Overview
This is a simple chatbot application built using Flask and DeepSeek AI. The application includes user authentication, a chat interface, and a deep learning model for generating chatbot responses.
## Features
- User registration and authentication
- Chat interface with AI-generated responses
- SQLite database for user management
- Dockerized for easy deployment
## Prerequisites
Make sure you have the following installed before running the application:
- Python 3.9+
- Docker
- pip
## Installation
1. Clone the repository:
```sh
git clone https://github.com/parvvaresh/deepspeek
cd deepspeek
```
2. Install dependencies:
```sh
pip install -r requirements.txt
```
3. Download the deep learning model:
```sh
chmod +x dl_model.sh
./dl_model.sh
```
4. Initialize the database:
```sh
python app.py
```
## Running the Application
### Using Python
```sh
python app.py
```
### Using Docker
1. Build the Docker image:
```sh
docker build -t flask-chatbot .
```
2. Run the container:
```sh
docker run -p 9090:9090 flask-chatbot
```
## API Endpoints
- `GET /` - Home page
- `GET /register` - Registration page
- `POST /register` - Handle user registration
- `GET /login` - Login page
- `POST /login` - Handle user login
- `GET /chat` - Chat interface
- `POST /chat` - Get AI-generated chatbot response
## Technologies Used
- Flask
- SQLite
- Bcrypt
- Ollama AI
- Docker
## License
This project is open-source and available under the MIT License.