🤖 Analyzia

Talk to your data. Instantly analyze, visualize, and transform.

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Transform your CSV data into meaningful insights with AI-powered analysis and beautiful visualizations

🚀 Get Started • 📖 Documentation • 🤝 Contributing • 💬 Support

📋 Table of Contents

• ✨ Features
• 🎯 What Makes Analyzia Special
• 🖼️ Screenshots
• 🚀 Quick Start
• ⚙️ Installation
• 🔧 Configuration
• 📊 Usage Examples
• 🏗️ Architecture
• 🛠️ Technology Stack
• 🤝 Contributing
• 📄 License
• 💬 Support
• 🙏 Acknowledgments

✨ Features

🤖 AI-Powered Analysis Natural Language Queries: Ask questions in plain English Intelligent Data Interpretation: Understands context and relationships Google Gemini Integration: Powered by cutting-edge AI 📊 Advanced Visualizations Professional Charts: Beautiful, publication-ready plots Interactive Dashboards: Plotly-powered dynamic visualizations Statistical Graphics: Correlation matrices, distributions, trends

🔍 Smart Data Processing Automatic Data Validation: Intelligent error detection Type Inference: Smart column type recognition Missing Data Handling: Robust data cleaning 💬 Conversational Interface Chat-Based Interaction: Intuitive conversation flow Memory Retention: Remembers previous questions Error Recovery: Graceful handling of edge cases

🎯 What Makes Analyzia Special

🚀 No Code Required

Transform complex data analysis into simple conversations. No need to write SQL queries or Python scripts.

🧠 Context-Aware AI

Analyzia understands your data structure and provides relevant insights based on your specific dataset.

📈 Professional Reporting

Get comprehensive analysis reports with:

• Executive summaries
• Statistical insights
• Business recommendations
• Actionable next steps

🎨 Beautiful Visualizations

Every chart is professionally styled with:

• Modern color palettes
• Clear annotations
• Publication-ready quality
• Interactive elements

🖼️ Screenshots

Analyzia's intuitive interface - Upload your CSV, ask questions in natural language, and get AI-powered insights with beautiful visualizations

✨ Key Interface Features:

• 🏠 Clean Welcome Screen - Intuitive interface to get you started
• 📊 Data Analysis in Action - AI-powered insights with beautiful visualizations
• 💬 Natural Language Queries - Ask questions like "Show me the correlation between sales and profit"
• 🤖 Conversational AI - Chat-based interaction with memory retention
• 📈 Professional Charts - Publication-ready visualizations

🚀 Quick Start

Prerequisites

• 🐍 Python 3.11 or higher
• 🔑 Google API Key (Get one here)

1️⃣ Clone the Repository

git clone https://github.com/ahammadnafiz/Analyzia.git
cd Analyzia

2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Set Up Environment

Create a .env file in the project root:

GOOGLE_API_KEY=your_google_api_key_here

4️⃣ Launch Analyzia

streamlit run app.py

5️⃣ Start Analyzing

1. 📤 Upload your CSV file
2. 💬 Ask questions about your data
3. 📊 Get instant insights and visualizations

⚙️ Installation

Option 1: Standard Installation

# Clone repository
git clone https://github.com/ahammadnafiz/Analyzia.git
cd Analyzia

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

# Install dependencies
pip install -r requirements.txt

Option 2: Development Installation

# Clone repository
git clone https://github.com/ahammadnafiz/Analyzia.git
cd Analyzia

# Install in development mode
pip install -e .
pip install -r requirements.txt

🔧 Configuration

Environment Variables

Create a .env file with the following variables:

# Required
GOOGLE_API_KEY=your_google_api_key_here

# Optional
STREAMLIT_SERVER_PORT=8501
STREAMLIT_SERVER_ADDRESS=localhost

Google API Key Setup

1. 🌐 Visit Google AI Studio
2. 🔑 Create a new API key
3. 📋 Copy the key to your .env file
4. ✅ Ensure billing is enabled for your Google Cloud project

📊 Usage Examples

Basic Data Analysis

# Upload your CSV file through the web interface
# Then ask natural language questions:

"What are the main trends in this dataset?"
"Show me the correlation between sales and profit"
"Which product category has the highest revenue?"
"Create a visualization showing monthly sales trends"

Advanced Queries

# Statistical Analysis
"Perform a statistical summary of all numeric columns"
"Find outliers in the sales data"
"What's the distribution of customer ages?"

# Business Intelligence
"Which regions are underperforming?"
"What factors contribute most to customer satisfaction?"
"Show me year-over-year growth rates"

Visualization Requests

# Chart Types
"Create a scatter plot of price vs sales"
"Show me a heatmap of correlations"
"Generate a bar chart of top 10 products"
"Make a time series plot of revenue trends"

🏗️ Architecture

graph TD
    A[User Input] --> B[Streamlit Interface]
    B --> C[LangChain Agent]
    C --> D[Google Gemini LLM]
    D --> E[Python Code Generation]
    E --> F[Data Processing]
    F --> G[Matplotlib/Plotly Visualization]
    G --> H[Streamlit Display]
    
    I[CSV Upload] --> J[Pandas DataFrame]
    J --> F
    
    K[Conversation Memory] --> C
    L[Error Handling] --> C

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Key Components

• 🖥️ Streamlit Frontend: Clean, responsive web interface
• 🤖 LangChain Agent: Orchestrates AI interactions
• 🧠 Google Gemini: Powers natural language understanding
• 📊 Visualization Engine: Creates beautiful charts and graphs
• 💾 Memory System: Maintains conversation context

🛠️ Technology Stack

Category	Technologies
🖥️ Frontend	Streamlit, HTML/CSS
🤖 AI/ML	LangChain, Google Gemini, Python AST
📊 Data Processing	Pandas, NumPy, SciPy
📈 Visualization	Matplotlib, Seaborn, Plotly
🔧 Backend	Python 3.11+, dotenv
🧪 Development	Git, Virtual Environment

🤝 Contributing

We welcome contributions from the community! Here's how you can help:

🐛 Bug Reports

1. Check existing issues first
2. Create a detailed bug report with:
   • Steps to reproduce
   • Expected behavior
   • Actual behavior
   • Screenshots (if applicable)

✨ Feature Requests

1. Open an issue with the enhancement label
2. Describe the feature and its benefits
3. Provide use cases and examples

🔧 Code Contributions

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Commit your changes: git commit -m 'Add amazing feature'
4. Push to the branch: git push origin feature/amazing-feature
5. Open a Pull Request

📋 Development Guidelines

• Follow PEP 8 style guidelines
• Add docstrings to functions and classes
• Include tests for new features
• Update documentation as needed

📄 License

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

MIT License - Free for commercial and personal use

💬 Support

🆘 Getting Help

• 📚 Documentation: Check this README and code comments
• 🐛 Issues: GitHub Issues
• 💡 Discussions: GitHub Discussions

📞 Contact

• 👨‍💻 Developer: ahammadnafiz
• 🐙 GitHub: @ahammadnafiz
• 📧 Email: Create an issue for support

🙏 Acknowledgments

Special thanks to:

• 🤖 Google AI: For providing the Gemini API
• 🦜 LangChain: For the excellent AI framework
• 🎨 Streamlit: For the amazing web app framework
• 📊 Plotly: For interactive visualizations
• 🐼 Pandas: For data manipulation capabilities
• 🤝 Open Source Community: For continuous inspiration

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Made with ❤️ by ahammadnafiz

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