FlaskHTMLStarter

openai-flask-starter — Flask Starter Template

By datumbrain

A chatbot starter project based on Flask and the OpenAI chat completion API, including a web interface, environment variable example, and Docker configuration.

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Recorded 2026-10-02View source

What stands out

A quick read of the recorded project description.

01

Flask backend and HTML template form a browser chat interface

02

Calls the OpenAI chat completion API to generate replies

03

Provides .env.sample to configure the OpenAI API key and HTTP_PORT

04

Includes Dockerfile and docker-compose.yml for containerized operation

05

Uses requirements.txt to manage Python dependencies

Template Details

This is a simple AI chatbot example built with Python and Flask that generates replies through OpenAI's large language model API. The project provides an input box in the browser; after the user sends a message, the backend calls the OpenAI chat completion endpoint to return content.

The repository contains the Flask service and chat logic (main.py), the frontend page template (templates/index.html), and custom styles (templates/styles/custom.css), and provides requirements.txt to manage dependencies. For configuration, .env.sample is used as an environment variable example; you need to fill in the OpenAI API key and can set HTTP_PORT (default 5899).

There are two ways to run it: locally, use Python's built-in virtual environment to install dependencies and then execute main.py; or use the Dockerfile and docker-compose.yml provided in the repository to build and start the container. The README states that Python 3.7+ and an OpenAI API key are required, and does not provide further details on model selection, error handling, or concurrency capabilities.

FlaskHTMLStarter

Is it right for your project?

Use Cases

  • Learning how to integrate the OpenAI chat completion endpoint in Flask
  • Building a simple locally running conversational bot prototype
  • As a starting point for containerized deployment of a Flask + OpenAI application

Suitable For

  • Developers who want to understand the basic integration of Flask and the OpenAI API
  • Python learners who need a locally runnable, containerizable chatbot example

Getting started & customization

Use these recorded setup notes alongside the repository instructions for your chosen version.

  1. 1

    Requires Python 3.7+ and a valid OpenAI API key

  2. 2

    Copy .env.sample to .env and fill in OPENAI_API_KEY; HTTP_PORT can be modified as needed

  3. 3

    Local run: create and activate a virtual environment, pip install -r requirements.txt, then python main.py

  4. 4

    Docker run: docker-compose build then docker-compose up

  5. 5

    Default access address is http://127.0.0.1:5899/

Read repository instructions

License & source material

Usage conditions & limitations

Free to use · keep license notice

This entry covers the free version under the MIT license in the public repository; the free scope is the code of that version and does not mean external services or third-party assets are free.

Must comply with when using

  • Free use, modification, commercial use, and distribution of this version's code are permitted.
  • When distributing copies or substantial portions of the code, retain the original author's copyright notice and the full MIT license notice.
  • The code is provided as is; warranties and limitations of liability are governed by the original license and applicable law.

Additional costs & items to verify

  • Using this project requires registering an OpenAI account and obtaining an API key, which is an external service dependency and may incur costs.
  • Before running, you must rename .env.sample to .env and fill in a valid OpenAI API key, otherwise the chat endpoint cannot be called.
  • Replies are generated by OpenAI's GPT-4 or GPT-3.5 models and depend on OpenAI service availability and the account having access to the corresponding models.
  • Demo images, fonts, icons, and other third-party assets must be checked against their respective licenses; they cannot be judged solely by the code's MIT license.
  • External services such as domains, hosting, databases, payments, or AI may incur additional charges.
View Original Text Basis

Permission is hereby granted, free of charge

Original License Text

OpenAI API key. You can get it [here](https://beta.openai.com/signup/).

Original README

Add your OpenAI API key in `.env.sample` and rename the file to `.env`

Original README

The chatbot generates human-like responses powered by GPT-4 (or GPT-3.5).

Original README
https://github.com/datumbrain/openai-flask-starter/blob/main/LICENSEhttps://github.com/datumbrain/openai-flask-starter/blob/main/README.md

Compiled by DeepSeek based on repository license and README ·2026-10-02. Results only cover the materials read; dependencies, images, fonts, and subsequent term changes are subject to their respective original texts.

Project checks & description sources

Template Information

Source platforms
GitHub
Documentation and maintenance reference score
Partly documented · 61 / 100
Technologies
Flask · HTML
Website type
Starter
How this score is calculated

Our heuristic uses license records (up to 30), a demo link (12), a preview (10), stars (15), recorded maintenance (15), description completeness (8), feature records (5) and classification data (5). It is not a code, security or performance test, or a Google score. A link does not establish tested functionality.

What to verify for this project

Repository update recorded
2024-09-13
Screenshot last checked
2026-10-02

No demo link is recorded. Use the repository instructions to evaluate the project locally; the screenshot alone cannot establish interactive behavior.

The catalog identifies Flask as the primary stack. Check the actual dependency manifest and lockfile for versions and hosting requirements. These notes use recorded metadata, not a code audit or a deployment test.

Use a selection guide →

How this description was prepared

AI-assisted description based on catalog records and repository material. Repository authors are not automatically the reviewers of this description.

Automatically checked against source and license rules; no manual editorial review.

Inspect the recorded source material

SHA-256: c6b463a7b43ad3bf7b68aebbda73480212483d4b2282a0149b0f9edfb542e05b

2026-10-02T22:49:06.441Z

Source file / repository
# OpenAI Flask Starter

![Banner Page Picture](./banner.png)

This project demonstrates how to create a simple AI chatbot using Python, Flask, and OpenAI's Large Language Model (LLM) API. The chatbot generates human-like responses powered by GPT-4 (or GPT-3.5).

## Features

- **Chatbot**: Conversational AI powered by OpenAI's GPT models.
- **Web Interface**: Built using Flask to interact with the bot via a browser.
- **OpenAI Integration**: Utilizes the latest OpenAI API for chat completions.
- **Docker Support**: Ready for containerization with a `Dockerfile` and `docker-compose` setup.
- **Easy Setup**: Uses Python's built-in virtual environment.

## Prerequisites

- Python 3.7+
- OpenAI API key. You can get it [here](https://beta.openai.com/signup/).

## Installation

1. **Clone the Repository**:

   ```bash
   git clone https://github.com/datumbrain/openai-flask-starter.git
   cd openai-flask-starter
   ```

2. **Set Up Virtual Environment**:

   Create a virtual environment and activate it:

   ```bash
   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`
   ```

3. **Install Dependencies**:

   Install the required packages using `requirements.txt`:

   ```bash
   pip install -r requirements.txt
   ```

4. **Set Up Your OpenAI API Key**:

   Add your OpenAI API key in `.env.sample` and rename the file to `.env`:

   ```bash
   OPENAI_API_KEY=your-api-key-here
   HTTP_PORT=5899    # change it as you like
   ```

5. **Run the Flask Application**:

   Start the Flask app by running:

   ```bash
   python main.py
   ```

   The app will be available at `http://127.0.0.1:5899/`.

## Docker Setup (Optional)

You can run the project in a Docker container using the following steps:

1. **Build the Docker Image**:

   ```bash
   docker-compose build
   ```

2. **Run the Application**:

   ```bash
   docker-compose up
   ```

   The app will be available at `http://127.0.0.1:5899/`.

## Project Structure

```raw
.
├── .dockerignore           # Files to ignore in Docker context
├── .env.sample             # Environment variable example file
├── .gitignore              # Files to ignore in git
├── Dockerfile              # Docker image setup
├── LICENSE                 # License information
├── README.md               # Project documentation
├── docker-compose.yml      # Docker Compose configuration
├── main.py                 # Flask server and chatbot logic
├── requirements.txt        # Project dependencies
└── templates/
    ├── index.html          # Frontend HTML for interacting with the chatbot
    └── styles/
        └── custom.css      # custom CSS styling
```

## Usage

Once the app is running, open your browser and go to `http://127.0.0.1:5899/`. Type a message in the input box, and the chatbot will respond using the GPT-4 model.

## License

This project is licensed under the MIT License.
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