Welcome to the Image Detection (Write Metadata) Streamlit App!

With this Streamlit app, you can easily:

-Upload images

-Detect objects within them

-Generate text descriptions

-Add metadata to the images

The models employed in this app are sourced from Hugging Face and include "facebook/detr-resnet-50" for object detection and "Salesforce/blip-image-captioning-base" for text generation.

Follow these steps to set up and run the Streamlit app on your local machine.

• Python (>=3.9)
• pip (Python package manager)

1. Clone this repository to your local machine:

   git clone https://github.com/timooo-thy/image-detection.git
   cd image-detection
   

2. Create a virtual environment (recommended):

   python -m venv venv
   source venv/bin/activate # On Windows: venv\Scripts\activate
   

3. Install the required packages:

   pip install -r requirements.txt
   

1. Create a .env file in the root directory of the project.

2. Add your huggingface token information to the .env file in the following format: HUGGINGFACEHUB_API_TOKEN = your_api_key_here

3. Make sure to include .env in your .gitignore file to keep your sensitive information secure.

1. Open a terminal and navigate to the project directory.

2. Activate the virtual environment if you created one:

   source venv/bin/activate # On Windows: venv\Scripts\activate
   

3. Run the Streamlit app:

   streamlit run app.py
   

4. The app should open in your default web browser.

For deploying the app to a production environment, consider deploying from Streamlit. Be sure to adjust configurations and follow deployment instructions for your chosen platform.

©Timothy Lee