A photo sharing social networking app built with Python and Flask. The example application for the book Python Web Development with Flask (2nd edition) (《Flask Web 开发实战（第 2 版）》).
Demo: http://moments.helloflask.com
This project enhances the Moments application with ML-powered features, including:
- Alternative Text Generation: Uses Azure Computer Vision API to generate image descriptions.
- Image Search with Object Detection: Automatically tags images based on detected objects, enabling search functionality.
Clone the repo:
$ git clone https://github.com/greyli/moments
$ cd moments
Install dependencies with PDM:
$ pdm install
Tip
If you don't have PDM installed, you can create a virtual environment with venv and install dependencies with pip install -r requirements.txt.
Install python-dotenv (code loads API keys from a .env file): pdm add python-dotenv
Set Up Azure API Credentials
- Go to Azure Portal
- Create a "Computer Vision Resource" (if you haven’t already).
- Navigate to "Keys and Endpoint" and copy your API key.
- Set Up Environment Variables- Create a
.envfile in the project root and add the following credentials:AZURE_CV_KEY=your_azure_computer_vision_key AZURE_CV_ENDPOINT=https://your-region.api.cognitive.microsoft.com/
To initialize the app, run the flask init-app command:
$ pdm run flask init-app
If you just want to try it out, generate fake data with flask lorem command then run the app:
$ pdm run flask lorem
It will create a test account:
- email:
admin@helloflask.com - password:
moments
Now you can run the app:
$ pdm run flask run
* Running on http://127.0.0.1:5000/
Using the Features
-
Upload Images for Alt Text Generation: Navigate to /upload. Upload an image, and the system will automatically generate alt text.
-
Search Images by Detected Objects: Go to /search?q=your_keyword. The system will retrieve images with matching detected objects.
- Implement user feedback loops to refine ML-generated descriptions.
- Optimize database queries for large-scale image search.
- Explore self-hosted models to reduce dependency on cloud APIs.
This project is licensed under the MIT License (see the LICENSE file for details).