ImageIQ

ImageIQ is a FastAPI-based application designed to leverage machine learning for analyzing and generating predictions from text and image inputs. It is optimized for scalability and efficiency, making it ideal for real-time data processing applications.

Key Features

• FastAPI Framework: Leverages the high performance and scalability of FastAPI.
• Machine Learning Integration: Utilizes advanced machine learning models for comprehensive analysis of text and images.
• Docker Support: Fully containerized using Docker to simplify deployment and ensure consistency across different environments.

Technologies Used

• FastAPI: A modern, fast web framework for building APIs with Python 3.7+ based on standard Python type hints.
• Python: The primary programming language used.
• Docker: Used for containerizing the application to ensure easy deployment and consistent runtime environments across different systems.
• Uvicorn: An ASGI server for Python, used to run the FastAPI application.
• Pillow (PIL): Python Imaging Library, used for image processing tasks.

ImageIQ.mov

Getting Started

Follow these instructions to set up the project locally for development and testing purposes.

Prerequisites

Ensure you have the following installed:

• Python 3.8 or higher
• Docker (optional, for containerization)

Installation

Execute the following steps to get your development environment up and running:

1. Clone the repository:

   git clone https://github.com/PrinceTp/ImageIQ

2. Install dependencies:

   pip install -r requirements.txt

3. Run the application:

   uvicorn main:app --reload

Docker Usage

For running the application within a Docker container, perform the following:

1. Build the Docker image:

   docker build -t ml_api .

2. Run the Docker container:

   docker run -p 8000:8000 ml_api

API Endpoints

• GET /: Returns a simple "Hello World" message.
• POST /ask: Accepts text and an image file, returns processed results.