🤖 Eviden Insight Reporter

Created by Algorzen

🤖 Eviden Insight Reporter

Autonomous Business Intelligence System by Algorzen

An enterprise-grade data analytics automation platform that transforms raw datasets into executive-level business intelligence reports with AI-powered narratives, comprehensive EDA, and professional PDF outputs.

🌟 Features

🔹 Core Analytics Engine

✅ Automatic Dataset Detection — Identifies sales, finance, customer, or general data types
✅ Comprehensive EDA — Missing values, statistics, correlations, distributions
✅ Smart KPI Extraction — Context-aware metrics based on dataset characteristics
✅ Interactive Visualizations — Heatmaps, distributions, and statistical plots

🔹 AI-Powered Insights

✅ GPT-4 Integration — Executive-level narratives with strategic recommendations
✅ Fallback Intelligence — Rule-based narrative generation when API unavailable
✅ Business Tone — Professional, McKinsey-style executive summaries
✅ Actionable Recommendations — Data-driven strategic insights

🔹 Professional Reporting

✅ Branded PDF Reports — Eviden formatting
✅ Executive Presentation Quality — Ready for stakeholder meetings
✅ Metadata Tracking — JSON reports with full traceability
✅ Multi-Format Support — CSV, Excel, Parquet inputs

🔹 Deployment Options

✅ Streamlit Web UI — User-friendly drag-and-drop interface
✅ CLI Tool — Scriptable command-line automation
✅ Modular API — Integrate into existing pipelines

📦 Installation

Prerequisites

Python 3.10 or higher
pip package manager

Quick Setup

# 1. Clone the repository
git clone https://github.com/rizzshi/AiInsight.git
cd AiInsight

# 2. Create virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# 3. Install dependencies
pip install -r requirements.txt

# 4. Set up environment variables (optional for GPT-4)
cp .env.example .env
# Edit .env and add your OpenAI API key

🚀 Quick Start

Option 1: Streamlit Web UI (Recommended for Beginners)

streamlit run streamlit_app.py

Then open your browser to http://localhost:8501 and:

Upload your dataset (CSV, Excel, or Parquet)
Configure settings in the sidebar
Click "Generate AI Report"
Download your professional PDF report

Option 2: Command Line Interface

# Generate sample dataset first (optional)
python -c "from src.utils import generate_sample_sales_data; generate_sample_sales_data(1000).to_csv('data/sample_dataset.csv', index=False)"

# Run analysis on sample data
python main.py data/sample_dataset.csv

# Or analyze your own dataset
python main.py path/to/your/data.csv --author "Your Name"

# With GPT-4 (requires API key)
python main.py data/your_data.csv --api-key sk-your-key-here --verbose

Option 3: Python API

import pandas as pd
from src.eda_engine import perform_eda
from src.kpi_extractor import extract_kpis
from src.ai_narrator import generate_narrative
from src.pdf_generator import generate_pdf_report

# Load your data
df = pd.read_csv('your_data.csv')

# Run automated analysis
eda_summary = perform_eda(df)
kpis = extract_kpis(df, eda_summary['dataset_info']['dataset_type'])
narrative = generate_narrative(eda_summary, kpis)

# Generate PDF report
pdf_path = generate_pdf_report(eda_summary, kpis, narrative)
print(f"Report saved to: {pdf_path}")

📊 Sample Dataset

A synthetic sales dataset with 1,000 records is included for testing:

# Generate sample data
python src/utils.py

# Analyze sample data
python main.py data/sample_dataset.csv --verbose

Sample Dataset Schema:

Transaction ID, Date, Product, Category
Region, Channel, Quantity, Pricing
Revenue, Discounts, Profit Margins
Customer IDs

🧠 How It Works

1. Dataset Type Detection

The system analyzes column names and data patterns to automatically classify datasets:

Sales: Revenue, products, quantities, pricing
Finance: Transactions, balances, debits/credits
Customer: Churn, segments, lifetime value
General: Fallback for other dataset types

2. Automated EDA

Comprehensive exploratory data analysis includes:

Missing value detection and quantification
Statistical summaries (mean, median, std dev, quartiles)
Correlation analysis with heatmap visualization
Distribution plots for numeric and categorical features

3. KPI Extraction

Context-aware KPI calculation based on dataset type:

Dataset Type	Example KPIs
Sales	Total Revenue, Average Order Value, Top Products, Margin Analysis
Finance	Total Balance, Net Position, Transaction Volume, Account Metrics
Customer	Churn Rate, Retention Rate, Avg Customer Value, Segment Distribution
General	Data Completeness, Record Count, Feature Diversity

4. AI Narrative Generation

Two-tier intelligent narrative system:

Tier 1: GPT-4 (when API key provided)

Executive summary (3-5 sentences)
Key findings (4-6 bullet points)
Actionable recommendations
Risks and limitations

Tier 2: Rule-Based Fallback

Pattern-based insights
Statistical observations
Domain-specific recommendations
Data quality assessment

5. PDF Report Assembly

Professional report generation with:

Eviden branding (Created by Algorzen)
Title page with metadata
KPI summary tables
Visualizations (heatmaps, distributions)
AI-generated narratives
Data quality appendix

⚙️ Configuration

Environment Variables (`.env`)

# OpenAI Configuration (optional)
OPENAI_API_KEY=sk-your-api-key-here
OPENAI_MODEL=gpt-4-turbo-preview

# Report Branding (optional)
COMPANY_NAME=Algorzen
AUTHOR_NAME=Rishi Singh

CLI Arguments

python main.py --help

Arguments:
  input_file              Path to dataset (CSV, Excel, Parquet)
  
Options:
  --output DIR            Output directory (default: reports/)
  --author NAME           Report author (default: Rishi Singh)
  --api-key KEY           OpenAI API key for GPT-4
  --no-pdf                Skip PDF generation
  --verbose               Show detailed progress

📁 Project Structure

AiInsight/
├── src/
│   ├── eda_engine.py          # Automated EDA engine
│   ├── kpi_extractor.py       # KPI calculation module
│   ├── ai_narrator.py         # GPT-4 narrative generator
│   ├── pdf_generator.py       # PDF report builder
│   └── utils.py               # Helper functions
├── data/
│   └── sample_dataset.csv     # Sample sales data (1000 records)
├── reports/
│   ├── assets/                # Generated charts and visualizations
│   ├── Eviden_Insight_Report_YYYYMMDD.pdf
│   └── report_metadata.json   # Report metadata
├── main.py                    # CLI entry point
├── streamlit_app.py           # Web UI application
├── requirements.txt           # Python dependencies
├── .env.example               # Environment variable template
└── README.md                  # This file

🎯 Use Cases

Business Intelligence Teams

Automate routine data analysis reports
Generate executive summaries for stakeholders
Standardize reporting across departments

Data Analysts

Quick exploratory data analysis
Automated KPI tracking
Professional report generation

Consultants

Client data analysis and reporting
Strategic insights with AI narratives
Branded deliverables

Startups & SMBs

Cost-effective business intelligence
No-code analytics for non-technical users
Scalable reporting infrastructure

🔧 Advanced Usage

Custom Dataset Type Detection

from src.eda_engine import EDAEngine

# Force specific dataset type
df = pd.read_csv('your_data.csv')
engine = EDAEngine(df)
engine.dataset_type = 'finance'  # Override auto-detection
summary = engine.run_full_eda()

Integration with Data Pipelines

# Example: Daily automated reporting
import schedule
from src.utils import load_dataset
from main import main

def daily_report():
    # Your ETL pipeline
    df = extract_from_database()
    df.to_csv('temp_data.csv', index=False)
    
    # Generate report
    import sys
    sys.argv = ['main.py', 'temp_data.csv', '--verbose']
    main()

schedule.every().day.at("09:00").do(daily_report)

Custom KPI Definitions

from src.kpi_extractor import KPIExtractor

class CustomKPIExtractor(KPIExtractor):
    def extract_custom_kpis(self):
        kpis = {}
        # Your custom KPI logic here
        kpis['Custom Metric'] = calculate_custom_metric(self.df)
        return kpis

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Development Setup

# Clone and setup
git clone https://github.com/rizzshi/AiInsight.git
cd AiInsight

# Install development dependencies
pip install -r requirements.txt
pip install pytest black flake8

# Run tests (when available)
pytest tests/

# Format code
black src/ *.py

📝 License

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

👨‍💻 Author

Rishi Singh
Eviden (Created by Algorzen)

GitHub: @rizzshi
Project: DataSphere/AiInsight

🙏 Acknowledgments

OpenAI for GPT-4 API
ReportLab for PDF generation
Streamlit for web UI framework
The open-source data science community

📮 Support

For questions, issues, or feature requests:

Open an issue on GitHub
Contact: Rishi Singh via GitHub

🔮 Roadmap

Planned Features

Built with ❤️ by Algorzen

Eviden - Transforming Data into Strategic Intelligence

Name		Name	Last commit message	Last commit date
Latest commit History 18 Commits
assets		assets
data		data
examples		examples
src		src
.env.example		.env.example
.gitignore		.gitignore
ARCHITECTURE.md		ARCHITECTURE.md
CHECKLIST.md		CHECKLIST.md
CONTRIBUTING.md		CONTRIBUTING.md
GETTING_STARTED.md		GETTING_STARTED.md
INDEX.md		INDEX.md
LICENSE		LICENSE
PROJECT_SUMMARY.md		PROJECT_SUMMARY.md
QUICK_REFERENCE.md		QUICK_REFERENCE.md
README.md		README.md
TEST_RESULTS.md		TEST_RESULTS.md
TROUBLESHOOTING.md		TROUBLESHOOTING.md
generate_sample_data.py		generate_sample_data.py
main.py		main.py
requirements.txt		requirements.txt
setup.py		setup.py
streamlit_app.py		streamlit_app.py

License

rizzshi/Eviden

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🤖 Eviden Insight Reporter

🌟 Features

🔹 Core Analytics Engine

🔹 AI-Powered Insights

🔹 Professional Reporting

🔹 Deployment Options

📦 Installation

Prerequisites

Quick Setup

🚀 Quick Start

Option 1: Streamlit Web UI (Recommended for Beginners)

Option 2: Command Line Interface

Option 3: Python API

📊 Sample Dataset

🧠 How It Works

1. Dataset Type Detection

2. Automated EDA

3. KPI Extraction

4. AI Narrative Generation

5. PDF Report Assembly

⚙️ Configuration

Environment Variables (.env)

CLI Arguments

📁 Project Structure

🎯 Use Cases

Business Intelligence Teams

Data Analysts

Consultants

Startups & SMBs

🔧 Advanced Usage

Custom Dataset Type Detection

Integration with Data Pipelines

Custom KPI Definitions

🤝 Contributing

Development Setup

📝 License

👨‍💻 Author

🙏 Acknowledgments

📮 Support

🔮 Roadmap

Planned Features

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