Expelexia Lab

Expelexia Lab is an AI-powered data analysis platform designed to automatically analyze data and generate intelligent, human-friendly recommendations to support better decision-making.

Why Expelexia Lab

• Enables fast AI-assisted analysis of CSV and PDF files.
• Generates clear summaries, dashboards, and visual insights.
• Produces professional PDF reports with actionable recommendations.
• Supports better decision-making through explainable, human-readable outputs.

Core Capabilities

• Data Upload & Processing: Supports multiple dataset types and document inputs.
• AI Analysis: Generates narrative insights from processed data.
• Recommendation Engine: Provides practical, decision-oriented suggestions.
• Dashboard Data API: Supplies data for front-end charts and summary views.
• Report Generation: Creates polished PDF reports.

Tech Stack

Frontend

• Next.js
• React
• TypeScript
• Tailwind CSS

Backend

• FastAPI
• Python

AI & Cloud Services

• Azure OpenAI
• Azure Cognitive Services
• Azure Storage

Project Structure

File Structure

LICENSE
README.md
backend/
    main.py
    requirements.txt
    smoke_check.py
    models/
        schemas.py
    routes/
        analyze.py
        dashboard.py
        report.py
        upload.py
    services/
        ai_service.py
        azure_blob_service.py
        data_service.py
        report_pdf_service.py
        report_service.py
        safety.py
    utils/
        helpers.py
data/
    processed/
        *.csv_summary.json
    raw/
        *.csv
    reports/
    temp/
docs/
    presentation file and video link
frontend/
    components/
    pages/
    public/
    styles/
    utils/

Ignored Files

The following files and directories are excluded from version control:

• .env files: Contain sensitive environment variables.
• node_modules/: Contains dependencies installed via npm.
• .venv/: Python virtual environment files.
• .next/: Build artifacts for Next.js.
• __pycache__/: Python cache files.

Setup Instructions

1. Clone the repository:

   git clone https://github.com/Adam-7th/Expelexia.git

2. Navigate to the project directory:

   cd Expelexia

3. Configure the backend:

   cd backend
   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\Activate.ps1
   pip install -r requirements.txt

4. Configure the frontend:

   cd ../frontend
   npm install

5. Start the project:

   • Backend: uvicorn main:app --reload
   • Frontend: npm run dev

Notes

• Python 3.9+ and Node.js must be installed.
• Replace .env files with appropriate environment variables.
• Large files (e.g., .venv, node_modules) are excluded to reduce repository size.

Quick Start

Clone the Repository

git clone https://github.com/Adam-7th/Expelexia.git
cd Expelexia

Backend Configuration

cd backend
python -m venv .venv
.venv\Scripts\activate   # Windows PowerShell
pip install -r requirements.txt

Start the backend:

.venv\Scripts\python.exe -m uvicorn main:app --reload --port 8000

Frontend Configuration

Open a separate terminal and execute the following commands:

cd frontend
npm install
npm run dev

The frontend is accessible at http://localhost:3000. The backend is accessible locally at http://127.0.0.1:8000.

Hosted Backend

The backend API is also hosted on Render and can be accessed at: https://backend-update-1-uloa.onrender.com/api

You can use the hosted backend for testing or switch to the local setup as needed.

API Endpoints (Common)

• POST /api/upload
• POST /api/analyze?file_name=<name>
• GET /api/report?file_name=<name>
• GET /api/report/<name>

Environment Variables (Secure Setup)

Create a .env file inside backend/ using backend/.env.example as a template.

# Azure OpenAI
AZURE_OPENAI_KEY=User_api_key_here
AZURE_OPENAI_ENDPOINT=User_endpoint_here

# Azure Cognitive Services
AZURE_COGNITIVE_KEY=User_key_here
AZURE_COGNITIVE_ENDPOINT=User_endpoint_here

# Azure Storage
AZURE_STORAGE_ACCOUNT_NAME=User_storage_account
AZURE_STORAGE_ACCOUNT_KEY=User_storage_key
AZURE_CONTAINER_NAME=User_container

Important Security Notes

• Never commit .env to GitHub.
• Keep real API keys and endpoints out of source files.
• Regenerate keys immediately if exposure occurs.
• Use Azure Key Vault for production secret management.

Security Best Practices

All sensitive credentials are stored securely using environment variables and are not exposed in the source code.

• API keys are loaded from environment variables.
• No hardcoded secrets in committed source files.
• Sensitive configuration is excluded by .gitignore.
• Azure service credentials are treated as private runtime secrets.

Future Security Enhancements

• Azure Key Vault integration for secret retrieval.
• Token-based authentication for API access.
• Role-based access control for dashboard/report operations.

Hackathon Readiness

• Professional architecture with separate frontend/backend layers.
• Production-aware documentation and secure environment handling.
• AI-driven value: actionable recommendations, not just raw analytics.

Team Information

Our team, NeuraForge, brings together highly skilled and diverse professionals with a strong background in AI, cloud computing, full-stack development, and data analytics. Each member has hands-on experience from internships, research, and projects in both academic and professional settings. Our combined technical proficiency, logical problem-solving, and collaborative mindset make us capable of solving complex AI challenges effectively. We are committed to delivering innovative solutions during the challenge.

Team Members

Henok Tariku

• Role: Junior Data Analyst & Software Engineer
• Education:
  • WorldQuant University (Financial Engineering, Feb 2026 - Present)
  • MIPT (Computer Science, Nov 2025 - Present)
  • University of the People (Computer Science, Dec 2023 - Jan 2026, CGPA 3.95)
• Focus: Practical software engineering, API integrations, and reliable production rollouts.
• LinkedIn: Henok Tariku
• Location: Russia,Moscow

Rocio Herrera

• Role: Cybersecurity & AI Enthusiast
• Skills: Cybersecurity, Artificial Intelligence, Problem Solving, Data Analysis, Risk Assessment
• Location: USA,LA

Built for Microsoft Hackathon

Design

• Clean, modern, company-style AI product narrative with strong motion design, research, and user focus.

Core Values

• Responsible AI by design.
• Clear and auditable next-step reasoning.
• Modular architecture for fast expansion.
• Research-first user experience.

Video Presentation

This video presentation showcases our project for the AI Innovation Challenge 2026, organized by Microsoft.

Video Parts

Part 1: https://youtu.be/V1Q58-B9So8 Part 2: https://www.youtube.com/watch?v=FOWmYaab1_M

The video explains the problem, our solution, key features, and the overall impact of the project.

Project Website

The Expelexia Lab project website is live and accessible at: Expelexia AI Lab

This website provides an interactive interface for users to explore the features of Expelexia Lab, including:

• Uploading and analyzing data files.
• Generating AI-driven insights and reports.
• Visualizing data through dashboards and charts.

Visit the website to experience the full functionality of the Expelexia Lab platform.