QuickFlow

QuickFlow is a web application that enables easy processing of text and images through a sequence of predefined Large Language Model (LLM) tasks. It provides a simple interface for chaining various data extraction and analysis operations.

Features

• Text Processing: Process text input through various LLM-powered tools
• Image Processing: Upload or capture images directly from your smartphone camera
• Tool Chaining: Send results from one tool as input to another tool
• Predefined Tools: Built-in tools for common tasks like:
  • Item List extraction
  • Link extraction
  • IOC (Indicators of Compromise) extraction
  • Contact information extraction
  • Date extraction
  • Named entity extraction
  • Text summarization
  • Item grouping/categorization

Screenshots

Setup

Prerequisites

• Docker and Docker Compose
• OpenRouter API key (sign up at openrouter.ai)

Installation

1. Clone the repository:

git clone <repository-url>
cd QuickFlow

2. Set up your environment variables by editing the docker-compose.yml file:

environment:
  - OPENROUTER_API_KEY=your_api_key_here
  - OPENROUTER_MODEL=google/gemma-3-27b-it:free

3. Build and run the application:

docker-compose up --build

4. Access the application at http://localhost:5000

Manual Setup (without Docker)

1. Install Python dependencies:

pip install flask requests werkzeug

2. Set environment variables:

export OPENROUTER_API_KEY=your_api_key_here
export OPENROUTER_MODEL=google/gemma-3-27b-it:free

3. Run the application:

python app.py

Usage

Web Interface

1. Select a Tool: Choose from the available processing tools on the main page
2. Input Data:
   • Enter text in the textarea, or
   • Upload an image file, or
   • Take a photo directly (on mobile devices)
3. Process: Click the Submit button to process your input
4. Chain Tools: Use the result as input for another tool by clicking on the tool buttons below the result

Mobile Usage

QuickFlow is optimized for mobile devices:

• Use the camera capture feature to take photos directly
• Responsive design works well on smartphones and tablets
• Touch-friendly interface for easy navigation

Available Tools

• Item List: Extracts a list of items from text or images
• Group Items: Categorizes items into logical groups
• Extract Links: Finds and extracts URLs and hyperlinks
• Extract IOCs: Identifies security indicators like IP addresses, domains, hashes
• Extract Contact Info: Finds names, emails, phone numbers
• Extract Dates: Identifies and extracts date information
• Extract Entities: Finds named entities (people, organizations, locations)
• Summarize Text: Creates concise summaries of longer text

API Endpoints

• GET / - Serves the main web interface
• GET /tools.json - Returns the available tools configuration
• POST /process - Processes text or image input through the selected tool

Configuration

Adding Custom Tools

You can add custom tools by editing the tools.json file:

{
  "name": "Your Tool Name",
  "description": "Description of what your tool does",
  "prompt": "Your prompt template with {input} placeholder",
  "structured": true
}

Environment Variables

• OPENROUTER_API_KEY: Your OpenRouter API key (required)
• OPENROUTER_MODEL: The LLM model to use (default: google/gemma-3-27b-it:free)

Technical Details

Architecture

• Frontend: React-based SPA with Tailwind CSS
• Backend: Flask web server
• LLM Integration: OpenRouter API for model access
• Image Processing: Base64 encoding for image data

File Structure

QuickFlow/
├── app.py              # Flask backend application
├── index.html          # Frontend React application
├── tools.json          # Tool definitions and prompts
├── Dockerfile          # Docker container configuration
├── docker-compose.yml  # Docker Compose setup
└── README.md          # This file

Development

Running in Development Mode

1. Enable debug mode by setting FLASK_ENV=development
2. Use volume mounting in docker-compose for live code reloading
3. Check the console logs for debugging information

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

License

MIT

Support

For issues and questions, please create an issue on the repository.