linkedinPostGenerator

Files and Directories

• contentGenerationForm/: Contains the logic for querying GPT to generate content.
  • queryGPT.py: Contains the query function used to generate content.
• modelForm/: Contains the logic for model content retrieval and generation.
  • modelContentRetrieval.py: Handles retrieval of model content.
  • modelGeneration.py: Handles generation of model content.
• ui.py: The main application file that sets up the Streamlit interface.
• environment.yml: Conda environment configuration file.
• .env: Environment variables file.
• .gitignore: Git ignore file.
• README.md: Project documentation.

Installation

1. Clone the repository:

   git clone <repository-url>
   cd linkedinPostGenerator

2. Create and activate a Conda environment:

   conda env create -f environment.yml
   conda activate linkedinPostGenerator

3. Install the required packages:

   pip install -r requirements.txt

Usage

1. Run the Streamlit application:

   streamlit run ui.py

2. Open your web browser and navigate to the provided local URL (usually http://localhost:8501).

3. Use the sidebar forms to input the necessary information:

   • Model Form: Specify the model influencer.
   • Content Generation Form: Provide the temperature, max tokens, context, and content for the post.

4. Click the "Write" button to generate the LinkedIn post.

Contributing

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes.
4. Commit your changes (git commit -m 'Add some feature').
5. Push to the branch (git push origin feature-branch).
6. Open a pull request.

License

This project is licensed under the MIT License.