Complexica-Server

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Overview

Complexica-Server is a Flask-based application designed to utilize machine learning models for image colorization. Leveraging Intel OpenVINO for optimized inference, the server takes grayscale images as input and outputs fully colorized images, enabling seamless integration of AI-powered image enhancement.

Features

Colorization Engine: Trained using the Flickr30k dataset based fine-tuned model for high-quality results.
Optimized Deployment: Enhanced wsgi based build powered by OpenVINO for efficient inference on Intel hardware.
Ready for Deployment: Easily deployable on local machines with docker or Heroku with minimal configuration.

Getting Started

Prerequisites

Python 3.8+
Flask
Intel OpenVINO Toolkit (optional for local inference)

Installation

Run the docker image directly:

   docker run -p 9090:9090 vrushankpatel5/complexica-server:1.0

OR

Docker compose

version: '3.8'

services:
  complexica-server:
    image: vrushankpatel5/complexica-server:1.0
    container_name: complexica-server
    ports:
      - "9090:9090"
    restart: always

OR

Clone the Repository:

git clone https://github.com/VrushankPatel/Complexica-Server.git
cd Complexica-Server

Set Up Virtual Environment:

python -m venv venv
source venv/bin/activate  # For Linux/Mac
venv\Scripts\activate    # For Windows

Install Requirements:
```
pip install -r requirements.txt
```
Download the Pretrained Model:
- The pre-trained Caffe model can be downloaded from this link.
- Place the model file in the src/model directory.
Run the Application:
```
flask run
```

Deployment on Heroku

Refer to the deploy-instructions.txt file for step-by-step deployment on Heroku.

API Endpoints

Colorization Endpoint

POST /api/upload_image

Request: Upload a grayscale image in .jpg or .png format.
Response: Returns a colorized image in the same format.

Directory Structure

Complexica-Server/
├── src/
│   ├── model/           # Pretrained models
│   ├── static/          # Static assets
│   ├── templates/       # HTML templates (if any)
│   └── app.py           # Main Flask application
├── requirements.txt     # Python dependencies
├── deploy-instructions.txt # Heroku deployment steps
└── README.md            # Project documentation

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments

Intel OpenVINO Toolkit for model optimization.
Flickr30k Dataset for training the colorization model.
Community contributors and developers.

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