DataXpert

AI-Powered Business Analytics Platform

Features • Quick Start • Documentation • Technology

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📋 Overview

DataXpert is a modern business analytics platform that leverages AI to provide actionable insights from your business data. Built with Python Flask backend and vanilla JavaScript frontend, it offers real-time analytics, AI-powered chat interface, and comprehensive data visualization.

✨ What Makes DataXpert Special

• 🔐 Dual Authentication - Email/password OR Google OAuth for seamless access
• 📊 Live Dashboard - Real-time metrics with interactive Chart.js visualizations
• 🤖 AI Chat Analysis - Natural language queries for immediate insights
• 📈 Anomaly Detection - Automated Z-score based outlier identification
• 📁 Excel Import - Bulk upload and process business data from spreadsheets
• 👤 Smart Profiles - Google profile integration with completion prompts
• ⚡ Optimized Performance - Streamlined codebase for faster response times

🚀 Quick Start

Prerequisites

• Python 3.8 or higher
• pip (Python package manager)
• Supabase account (for database)
• Google OAuth credentials (optional, for Google login)

Installation

1. Clone the repository

   git clone https://github.com/yourusername/DataXpert.git
   cd DataXpert

2. Setup Backend

   cd backend
   pip install -r requirements.txt

3. Configure Environment Variables

   cp .env.example .env

   Edit .env file with your credentials:

   SECRET_KEY=your-secret-key
   JWT_SECRET_KEY=your-jwt-secret
   SUPABASE_URL=your-supabase-url
   SUPABASE_KEY=your-supabase-key
   GOOGLE_CLIENT_ID=your-google-client-id
   

4. Setup Database

   Run the simplified SQL setup script in your Supabase SQL Editor:

   # Copy contents of database.sql and run in Supabase

   Or manually create tables (see database.sql for complete schema):

   • users - User accounts with Google OAuth support
   • business_data - Business metrics and records
   • chats - AI chat conversation history
   • analysis_results - AI-generated insights
   • teams - Team management

5. Run Backend

   python app.py

   Server will start at http://localhost:5000

6. Setup Frontend

   cd ../frontend

   Edit js/config.js and update:

   const GOOGLE_CLIENT_ID = 'your-google-client-id';

7. Run Frontend

   Option 1: Use Python HTTP Server

   python -m http.server 5500

   Option 2: Use VS Code Live Server extension

   Option 3: Open index.html directly in browser

8. Access Application

   • Frontend: http://localhost:5500 or http://127.0.0.1:5500
   • Backend API: http://localhost:5000
   • Documentation: Open documentation/index.html

📁 Project Structure (Optimized)

DataXpert/
├── assets/              # Static assets (logo, images)
├── backend/             # Flask backend application
│   ├── ai/             # AI analysis modules
│   │   ├── analysis_engine.py    # Business data analysis
│   │   ├── anomaly_detector.py   # Anomaly detection
│   │   ├── data_processor.py     # Data processing
│   │   └── nlp_processor.py      # NLP for chat
│   ├── auth/           # Authentication service
│   │   └── auth_service.py       # Auth logic
│   ├── database/       # Database operations
│   │   └── supabase_client.py    # DB client
│   ├── app.py          # Main Flask application
│   ├── requirements.txt # Python dependencies
│   ├── .env            # Environment variables (create from .env.example)
│   └── .env.example    # Environment template
├── frontend/           # Frontend application
│   ├── css/           # Stylesheets
│   │   ├── style.css
│   │   ├── dashboard.css
│   │   └── analysis.css
│   ├── js/            # JavaScript modules
│   │   ├── auth.js            # Authentication
│   │   ├── config.js          # Configuration
│   │   ├── dashboard.js       # Dashboard logic
│   │   ├── analysis.js        # AI analysis
│   │   ├── theme.js           # Theme switcher
│   │   ├── header-profile.js  # Profile component
│   │   ├── data-import-export.js  # Data operations
│   │   └── comparison-analysis.js # Comparisons
│   ├── docs/          # Documentation
│   │   ├── api/       # API documentation
│   │   │   └── swagger.yaml
│   │   ├── css/       # Docs styles
│   │   ├── js/        # Docs scripts
│   │   ├── index.html # Docs home
│   │   ├── setup.html # Setup guide
│   │   ├── workflow.html # Workflow diagram
│   │   ├── er-diagram.html # DB schema
│   │   └── folder-structure.html # Structure
│   ├── index.html     # Login/signup page
│   ├── dashboard.html # Main dashboard
│   ├── analysis.html  # AI analysis
│   ├── history.html   # Activity history
│   └── profile.html   # User profile
├── database.sql       # Complete database setup
└── README.md          # This file

🔧 Optimization Changes

This version includes the following optimizations:

Backend Optimization

• ✅ Removed duplicate code - Fixed duplicate complete_profile function
• ✅ Streamlined environment - Consolidated multiple .env files into one
• ✅ Unified requirements - Merged development and production dependencies
• ✅ Removed cache files - Cleaned all __pycache__ directories

Frontend Optimization

• ✅ Consolidated config - Removed redundant config.development.js and config.production.js
• ✅ Merged documentation - Combined docs folder into frontend/docs
• ✅ Organized structure - Better file organization and naming

Database Optimization

• ✅ Single SQL file - Combined database_setup.sql and migration files into database.sql
• ✅ Simplified schema - Removed unnecessary tables for core functionality

Documentation Cleanup

• ✅ Removed unnecessary files - Cleaned up CHANGELOG, TESTING_GUIDE, OPTIMIZATION_REPORT, etc.
• ✅ Kept only README - Single source of truth for project documentation
• ✅ Integrated docs - All documentation now in frontend/docs folder

🛠️ Technology Stack

• Frontend: HTML5, CSS3, JavaScript (ES6+), Chart.js
• Backend: Python, Flask, Flask-CORS
• Database: Supabase (PostgreSQL)
• Data Analysis: Pandas, NumPy, SciPy
• Authentication: JWT, Google OAuth 2.0
• AI/ML: Rule-based NLP, Anomaly Detection

� Features

Authentication

• Email/password registration and login
• Google OAuth integration
• JWT token-based authentication
• Password hashing with SHA-256

Dashboard

• Real-time business statistics
• Interactive charts (sales, profit, expenses)
• Recent activity feed
• Team management

AI Analysis

• Natural language chat interface
• Intent recognition and entity extraction
• Automated insights generation
• Anomaly detection
• Trend analysis
• Sales, profit, and expense analysis

Team Collaboration

• Create and manage teams
• Add team members
• Share business insights

🔧 Configuration

Environment Variables

Create a .env file in the backend directory:

# Flask Configuration
SECRET_KEY=your-super-secret-key-change-this
JWT_SECRET_KEY=your-jwt-secret-key
FLASK_ENV=development
FLASK_DEBUG=True

# Supabase Configuration
SUPABASE_URL=https://your-project.supabase.co
SUPABASE_KEY=your-supabase-anon-key

# Google OAuth Configuration
GOOGLE_CLIENT_ID=your-google-oauth-client-id
GOOGLE_CLIENT_SECRET=your-google-oauth-client-secret

Google OAuth Setup

1. Go to Google Cloud Console
2. Create a new project
3. Enable Google+ API
4. Create OAuth 2.0 credentials
5. Add authorized JavaScript origins:
   • http://localhost:5500
   • http://127.0.0.1:5500
6. Add authorized redirect URIs:
   • http://localhost:5500
7. Copy Client ID to .env and frontend/js/config.js

Supabase Setup

1. Create account at Supabase
2. Create new project
3. Copy project URL and anon key to .env
4. Run the SQL commands from step 4 of installation
5. Enable Row Level Security (RLS) policies as needed

📚 Documentation

Complete documentation is available in the frontend/docs folder:

• Overview: System architecture and features
• Setup Guide: Detailed installation instructions
• Workflow: Application workflow and data flow
• ER Diagram: Database schema and relationships
• Folder Structure: Project organization
• API Documentation: Swagger YAML in docs/api/

Access documentation by opening frontend/docs/index.html in a browser.

🎯 Usage

Adding Business Data

1. Login to your account
2. Navigate to Dashboard
3. Click "Add Data" button
4. Fill in:
   • Date
   • Category (Retail, Services, etc.)
   • Sales amount
   • Expenses amount
   • Profit (auto-calculated)
5. Submit to save

Using AI Analysis

1. Navigate to "AI Analysis" page
2. Type your question or click a suggestion:
   • "What are my total sales?"
   • "Show me profit analysis"
   • "Analyze my expenses"
   • "What are the trends?"
   • "Detect any anomalies"
3. AI will analyze your data and provide insights
4. View charts and recommendations
5. Ask follow-up questions

Creating Teams

1. Go to Dashboard
2. Scroll to "Your Teams" section
3. Click "Create Team"
4. Enter team name
5. Invite members (email functionality to be added)

🔐 Security

• Passwords are hashed using SHA-256 with salt
• JWT tokens for session management
• CORS configured for security
• Environment variables for sensitive data
• SQL injection protection via Supabase
• Input validation on all forms

🚧 Future Enhancements

• CSV/Excel data import (Already implemented!)
• Email notifications
• PDF report generation
• Advanced forecasting models
• Mobile responsive improvements
• Real-time collaboration
• More chart types
• Multi-language support
• Data export functionality
• Advanced team permissions

🤝 Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Open a Pull Request

� About SGP Group

24CS067 - ManavPatel 24CS072 - Prince Patel 24CS076 - Srujal Patel

📞 Contact Us

Have a question, an idea, or just want to say hi?
I’m always open to connecting!

• 📧 Email: 24CS067@charusat.edu.in & 24CS072@charusat.edu.in & 24CS076@charusat.edu.in
• 🐙 GitHub: DataXpert

Feel free to reach out — I’d love to hear from you! 😊

-- Made with ❤️ by DataXpert

Last Updated: March 4, 2026 (Optimized & Streamlined)