Simple CRM with AI

A simple yet powerful CRM (Customer Relationship Management) system built with Python, Streamlit, and AI analytics capabilities. The system uses Ollama's Gemma2 model for generating insights about clients and deals.

Features

• 🏢 Company Information Management
• 🛍️ Products/Services Management
• 👥 Client Management with AI-powered analysis
• 💰 Deal Tracking with AI-powered success prediction
• ✅ Task Management
• 📊 Advanced Analytics Dashboard
• 🤖 Local AI processing using Ollama
• 🔒 Secure local database storage

Screenshots

Company Information Management

Manage your company details, target market, and unique selling points in one place.

Task Management

Create, assign, and track tasks with due dates and client associations.

Deal Analytics

Monitor your deal pipeline and track performance with interactive visualizations.

Installation

1. Clone this repository:

git clone https://github.com/KazKozDev/Simple-CRM-with-AI.git
cd Simple-CRM-with-AI

2. Install required Python packages:

pip install -r requirements.txt

3. Install Ollama:

   • macOS: Visit https://ollama.ai and download the installer
   • Windows:
     • Install WSL2 following Microsoft's guide
     • Install Ollama in WSL2 following Ollama's guide

4. Pull the Gemma2 model:

# macOS
ollama pull gemma2:9b

# Windows (in WSL2)
wsl -d Ubuntu -e ollama pull gemma2:9b

Manual Startup

macOS

1. Start Ollama if not running:

open -a Ollama

2. Start the CRM:

streamlit run crm.py

Windows (WSL2)

1. Start Ubuntu (WSL):

wsl -d Ubuntu

2. In WSL terminal:

# Start Ollama service
sudo systemctl start ollama

# Start the CRM
streamlit run crm.py

The application will be available at http://localhost:8501 in your web browser.

Quick Start for macOS

The easiest way to run the application on macOS:

1. Install Ollama from https://ollama.ai

2. Make the startup script executable. You can do this in two ways:

Using Terminal:

chmod +x start_crm.command

Using Finder:

• Find start_crm.command in Finder
• Right-click (or Control + click) on the file
• Select "Get Info"
• In "Sharing & Permissions" section, check "Execute" for your user

Note: This step is only needed once after creating the file.

3. Run the application:

• Double-click start_crm.command in Finder
• Or run it from Terminal: ./start_crm.command

The script will:

• Check if Ollama is installed
• Start Ollama if it's not running
• Download the Gemma2 model if needed
• Launch the CRM application

The CRM interface will automatically open in your default web browser at http://localhost:8501

Requirements

• Python 3.8+
• Ollama installed locally (https://ollama.ai)
• Gemma2:9b model for Ollama
• Required Python packages (see requirements.txt)

Project Structure

Simple-CRM-with-AI/
│
├── crm.py              # Main application file
├── start_crm.command   # Startup script for macOS
├── requirements.txt    # Python dependencies
├── README.md          # Documentation
└── crm.db             # SQLite database (created on first run)

Features in Detail

Company Information Management

• Store and manage company details
• Track company description, target market, and unique selling points
• Keep history of company information updates

Products/Services Management

• Add and manage products and services
• Track pricing and features
• Define target audience for each offering

Client Management

• Comprehensive client profiles
• Contact information storage
• Client interaction history
• AI-powered client analysis
• Potential value assessment

Deal Tracking

• Track deal status and progress
• Monitor deal value
• AI-powered success prediction
• Deal analytics and reporting

Task Management

• Create and assign tasks
• Track task status
• Set due dates
• Link tasks to clients or deals

Analytics Dashboard

• Real-time business metrics
• Sales performance tracking
• Client engagement analytics
• Deal pipeline visualization

Acknowledgments

This project wouldn't be possible without the amazing work of these projects and their communities:

Core Technologies

• Ollama - Local large language model
• Google - The Gemma model
• Streamlit - The web application framework
• SQLite - Database engine

Python Libraries

• Pandas - Data manipulation and analysis
• Plotly - Interactive visualizations
• Requests - HTTP library
• Python-dateutil - Date utilities

Development Tools

• GitHub Actions - CI/CD pipeline
• Black - Code formatting
• Flake8 - Code linting

Special Thanks

• All contributors and issue reporters
• The open source community for their invaluable resources and support

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

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

Support

If you encounter any problems or have suggestions, please open an issue on GitHub.