https://github.com/cyprianfusi/math-problem-solver
A Streamlit-based intelligent assistant that combines mathematical problem-solving capabilities with data search functionality, powered by Google's Gemma 2 model via Groq API.
https://github.com/cyprianfusi/math-problem-solver
agentic-ai gemma2 generative-ai langchain llm reasoning streamlit
Last synced: about 2 months ago
JSON representation
A Streamlit-based intelligent assistant that combines mathematical problem-solving capabilities with data search functionality, powered by Google's Gemma 2 model via Groq API.
- Host: GitHub
- URL: https://github.com/cyprianfusi/math-problem-solver
- Owner: CyprianFusi
- Created: 2025-09-17T16:49:33.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-09-20T05:44:20.000Z (9 months ago)
- Last Synced: 2025-10-23T06:38:41.977Z (8 months ago)
- Topics: agentic-ai, gemma2, generative-ai, langchain, llm, reasoning, streamlit
- Language: Python
- Homepage: https://binati-ai.com
- Size: 1.71 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Text to Math Problem Solver and Data Search Assistant
_By **BINATI AInalytics**_
A Streamlit-based intelligent assistant that combines mathematical problem-solving capabilities with data search functionality, powered by Google's Gemma 2 model via Groq API.
# Demo
## 🌟 Features
- **Mathematical Problem Solving**: Handles complex math problems with step-by-step reasoning
- **Wikipedia Integration**: Searches and retrieves information from Wikipedia for context
- **Logical Reasoning**: Provides detailed explanations with bullet-point breakdowns
- **Interactive Chat Interface**: User-friendly conversational interface
- **Real-time Processing**: Live response generation with visual feedback
## 🛠️ Technology Stack
- **Frontend**: Streamlit
- **LLM**: Google Gemma 2 (9B parameters) via Groq API
- **Framework**: LangChain for agent orchestration
- **Tools Integration**:
- Wikipedia API for information retrieval
- Built-in calculator for mathematical computations
- Custom reasoning chain for logical problem-solving
## 📋 Prerequisites
- Python 3.8 or higher
- Groq API key (get one from [Groq Console](https://console.groq.com/))
## 🚀 Installation
1. **Clone the repository**
```bash
git clone https://github.com/CyprianFusi/math-problem-solver.git
cd math-problem-solver
```
2. **Install required packages**
```bash
pip install -r requirements.txt
```
3. **Run the application**
```bash
streamlit run app.py
```
## 🔧 Configuration
1. Launch the application
2. Enter your Groq API key in the sidebar
3. Start asking mathematical questions or requesting data searches
## 💡 Usage Examples
### Mathematical Problems
```
Question: "I have 5 bananas and 7 grapes. I eat 2 bananas and give away 3 grapes. Then I buy a dozen apples and 2 packs of blueberries. Each pack of blueberries contains 25 berries. How many total pieces of fruit do I have at the end?"
```
### Data Search Queries
```
Question: "What is the population of Tokyo and how does it compare to other major cities?"
```
### Logical Reasoning
```
Question: "If a train travels at 60 mph for 2 hours, then 80 mph for 1.5 hours, what's the average speed for the entire journey?"
```
## 🏗️ Architecture
The application uses a multi-agent architecture with three specialized tools:
1. **Wikipedia Tool**: Searches Wikipedia for factual information
2. **Calculator Tool**: Performs mathematical calculations using LangChain's LLMMathChain
3. **Reasoning Tool**: Provides logical step-by-step problem-solving
These tools are orchestrated by a ZERO_SHOT_REACT_DESCRIPTION agent that determines which tool to use based on the user's query.
## 🎯 Key Components
- **Agent Initialization**: Sets up the multi-tool agent with Wikipedia, calculator, and reasoning capabilities
- **Session Management**: Maintains conversation history using Streamlit's session state
- **Real-time Callbacks**: Provides live feedback during response generation
- **Error Handling**: Robust parsing error management
## 🔒 Security Notes
- API keys are handled securely through Streamlit's sidebar input
- No API keys are stored permanently in the application
- Users must provide their own Groq API credentials
## 🤝 Contributing
1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add some amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request
## 📝 License
This project is open source and available under the [MIT License](https://raw.githubusercontent.com/Dogfalo/materialize/master/LICENSE).
## 🐛 Troubleshooting
**Common Issues:**
1. **API Key Error**: Ensure you have a valid Groq API key
2. **Package Installation**: Make sure all dependencies are installed correctly
3. **Port Issues**: If default port is busy, Streamlit will suggest an alternative
**Error Messages:**
- "Please add your Groq API key to continue" → Enter your API key in the sidebar
- Package import errors → Run `pip install -r requirements.txt`
## 📞 Support
If you encounter any issues or have questions, please open an issue in the GitHub repository.
## 🎉 Acknowledgments
- Google for the Gemma 2 model
- Groq for providing fast inference API
- LangChain team for the excellent framework
- Streamlit for the intuitive web app framework