danieljwilson / LLM-Poster-Recommender

🕵️ Poster Finder

Overview

This project comprises a Jupyter notebook for generating embeddings from poster descriptions (poster_embeddings.ipynb) and a Streamlit application (app_streamlit.py) for visualizing these embeddings. It aims to provide an interactive platform to explore semantic similarities between posters based on their descriptions using OpenAI's embedding models.

Features

• Embedding Generation: Calculate embeddings for poster descriptions using OpenAI's API.
• Dimensionality Reduction: Apply PCA and t-SNE for embedding visualization.
• Streamlit Web App: Interactive UI to search and visualize posters by similarity.

Prerequisites

• Python 3.6 or higher
• Streamlit
• OpenAI
• Pandas, NumPy, Scikit-learn, Plotly

Installation

1. Clone the repository:

   git clone https://github.com/danieljwilson/LLM-Poster-Recommender.git
   cd LLM-Poster-Recommender
   

2. Create and activate a virtual environment:

   python -m venv venv
   # For Windows
   venv\Scripts\activate
   
   # For Unix or MacOS
   source venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

4. Set the OpenAI API key:

   • Locally: Set an environment variable OPENAI_API_KEY with your OpenAI API key.

     # For Windows
     setx OPENAI_API_KEY "your_openai_api_key_here"
     # For Unix or MacOS
     export OPENAI_API_KEY="your_openai_api_key_here"

   • Streamlit Sharing/Deployment: Add your OpenAI API key as a secret. In Streamlit Sharing, navigate to your app settings, find the Secrets section, and add your OpenAI API key like so:

     OPENAI_API_KEY: "your_openai_api_key_here"

Running the App Locally

1. Generate embeddings:

   Use the poster_embeddings.ipynb notebooks to processes the poster descriptions, generates embeddings, and saves them along with dimensional reductions for visualization.

2. Start the Streamlit app:

   streamlit run app_streamlit.py

   The Streamlit app provides an interface to visualize the posters' embeddings and explore their similarities.

Deployment

To deploy the Streamlit app, you can use services like Streamlit Sharing, Heroku, or any platform supporting Python apps. Ensure you add your OPENAI_API_KEY as a secret or environment variable according to the platform's guidelines.

Streamlit Sharing

1. Push your code to a GitHub repository.
2. Sign up for Streamlit Sharing and connect your GitHub account.
3. Select your repository and branch, then click on deploy.
4. Add your OPENAI_API_KEY in the app's settings under Secrets.

Heroku

1. Create a Procfile and add:

   web: sh setup.sh && streamlit run app_streamlit.py
   

2. Create a setup.sh script to set environment variables (including OPENAI_API_KEY) and start the app.
3. Follow Heroku's documentation to deploy the app.

Contributing

Contributions are welcome. Please fork the repository and submit pull requests with your changes.

License

MIT.