Thumbnail Generation Service for KB Entities

This service utilizes generative AI to create visually appealing thumbnails for entities within the KB domain. By leveraging advanced language models and image generation techniques, this service provides a quick and efficient way to generate representative thumbnails for various KB entities.

Prerequisites

Before you begin, ensure you have the following installed on your system:

• Python 3.10+
• Poetry
• Docker (optional, for running the application in a container)

Table of Contents

• Installation
• Usage
• Docker
• Contributing

Installation

1. Clone and Change the directory to the project root.

   git clone https://github.com/KB-iGOT/Thumbnail-Generation.git
   cd Thumbnail-Generation

2. Create a virtual environment (optional but recommended):

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

3. Install the required dependencies:

   poetry install
   

4. Copy .env.example file, paste it into the same location, and rename to .env and update the values in that file.

   Environment Variable	Description
   SERVICE_ENVIRONMENT	Specifies the environment in which the service is running. Can be "DEV", "STAGING", or "PROD".
   LOG_LEVEL	Defines the level of logging. Common values include "DEBUG", "INFO", "WARN", "ERROR".
   KB_API_HOST=""	Host URL for the KarmaYogi portal's API.
   OPENAI	OpenAI API Configuration
   OPENAI_API_KEY	API key used to authenticate with OpenAI services.
   GCP Storage	Google Cloud Platform (GCP) Storage Configuration
   GCP_STORAGE_CREDENTIALS	Path to a JSON file containing GCP service account credentials for accessing Google Cloud Storage
   GCP_BUCKET_NAME	Name of the GCP bucket where data is stored.
   STORAGE_THUMBNAIL_FOLDER	Subfolder within the GCP bucket to store thumbnails of generated images. (e.g., "thumbnail_images")
   STORAGE_PROXY_PATH	Proxy path for accessing stored files in the GCP bucket through a proxy url. (e.g., "thumbnails/generate")
   GCP Vertex AI	Google Cloud Platform (GCP) Vertex AI Configuration
   GCP_GEMINI_CREDENTIALS	Path to the GCP Gemini credentials JSON file used for authentication with Vertex AI.
   GCP_GEMINI_PROJECT_ID	ID of the GCP project where Vertex AI models are deployed.
   GEMINI_MODEL_PRO	Name of the Gemini text-to-image model to be used. (e.g., "gemini-1.5-flash-001")
   VISION_MODEL	Identifier for the vision model version to be used for image generation in Vertex AI. (e.g., "imagen-3.0-fast-generate-001")
   NUMBER_OF_IMAGES	Defines the number of images to generate during an image processing task. (e.g., "2")

Usage

• Run the FastAPI application: uvicorn app.main:app --reload

• Access the API at http://localhost:8000/docs.

Use the /v1/image/course/{course_id} endpoint to generate image.

Docker

To run the application using Docker, follow these steps:

• Build the Docker image: docker build -t kb_image .

• Run the Docker container: docker run -d -p 8000:8000 --name kb_image_api kb_image

Access the Application: You can access your FastAPI application by navigating to http://localhost:8000/docs in your web browser.

Contributing

Contributions are welcome! If you have suggestions for improvements or new features, please open an issue or submit a pull request.

• Fork the repository.
• Create a new branch (e.g., git checkout -b feature-branch).
• Make your changes and commit them (e.g., git commit -m 'Add new feature').
• Push to the branch (e.g., git push origin feature-branch).
• Open a pull request.