AI-powered Insights Platform

A simple AI insights platform that allows users to upload files, extract text, and generate insights using DeepSeek Model via Openrouter.ai.

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Features

• Document upload and text extraction
• AI-powered insights generation
• User-friendly interface (Flutter Web)
• Backend built with FastAPI

Architectural Decisions

For a detailed overview of the architectural decisions made during the development of this project, please refer to the Architectural Decisions document. However, here are some key decisions taken into consideration of this project:

• FastAPI: Chosen for its performance and ease of use in building APIs.
• Flutter Web: Used for the frontend to provide a responsive and interactive user interface.
• MCP Server/Client: The platform is connected to a MCP server via Openrouter.ai, ensuring LLM (Large Language Model) capabilities are available for generating insights.
• File Upload and Processing: Implemented using FastAPI's file handling capabilities, allowing users to upload various document formats.
• Text Extraction: Text extraction is done via python-docx for Word Docuemnt files, and custom functions for TXT files, ensuring compatibility with different document types.

Aside: Added support for Word Docuement files (.docx) and TXT files for text extraction, to stress-test the platform's capabilities with different file formats.

Installation

At the moment, It is highly recommended to run the project locally, within each project folders (web and backend). Ensure all dependencies are installed by following the instructions below.

cd into web, and run:

flutter pub get    # to install Flutter dependencies
flutter build web    # to build the Flutter web app
flutter run -d chrome    # to run the Flutter web app in Chrome

cd into backend, and run:

pip install -U pip    # to upgrade pip
python3 -m venv .venv
source .venv/bin/activate    # to activate the virtual environment
pip install -r requirements.txt    # to install Python dependencies

then still within backend, run:

cd src
uvicorn main:app --reload

For local development, you can set up the project using Docker and Docker Compose. This will allow you to run both the Flutter web app and the FastAPI backend seamlessly.

Clone the repository and run:

make web    # to build the Flutter web app
make backend    # to start the FastAPI backend

Prerequisites

Docker

Deployment

Please ensure you have Docker installed and running before deploying the application. Follow these steps to deploy the application:

Run the following commands to build and start the Docker containers:

docker-compose build    # to build the Docker images
docker-compose up -d    # on your server or VM

Contributing

We welcome contributions to this project! Please follow these steps to contribute:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and commit them with clear messages.
4. Push your changes to your forked repository.
5. Create a pull request to the main repository.

License

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