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title: LLM Data Analyst Agent | |
emoji: π€ | |
colorFrom: blue | |
colorTo: green | |
sdk: streamlit | |
sdk_version: 1.32.0 | |
app_file: app.py | |
pinned: false | |
license: apache-2.0 | |
# π€ LLM-powered Data Analyst Agent | |
An intelligent data analysis assistant that helps you explore and understand customer support datasets using advanced language models. | |
## π Features | |
- **Interactive Data Analysis**: Ask questions in natural language and get intelligent responses | |
- **Multiple Planning Modes**: Choose between pre-planning and reactive dynamic planning | |
- **Beautiful UI**: Modern, responsive interface with custom styling | |
- **Real-time Conversations**: Chat-like interface for seamless interaction | |
- **Dataset Insights**: Automatic analysis of customer support conversations | |
## π How to Use | |
1. **Ask Questions**: Type your question about the customer support data | |
2. **Get Insights**: The AI will analyze the data and provide detailed answers | |
3. **Explore Further**: Follow up with additional questions for deeper analysis | |
### Example Questions: | |
- "What are the most common customer issues?" | |
- "Show me examples of billing problems" | |
- "What's the distribution of customer intents?" | |
- "Summarize the main categories of support requests" | |
## π οΈ Technology Stack | |
- **Frontend**: Streamlit with custom CSS styling | |
- **AI Model**: Nebius API (Qwen/Qwen3-30B-A3B) | |
- **Data Processing**: Pandas for data manipulation | |
- **Dataset**: Bitext Customer Support Dataset | |
## π Dataset | |
This app analyzes the [Bitext Customer Support Dataset](https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset) which contains real customer support conversations with: | |
- **Categories**: Different types of customer issues | |
- **Intents**: Specific customer intentions | |
- **Customer Messages**: Original customer inquiries | |
- **Agent Responses**: Support agent replies | |
## π§ Configuration | |
The app requires a Nebius API key to function. This has been configured as an environment variable for this Space. | |
## π‘ Tips | |
- **Be Specific**: More specific questions often yield better insights | |
- **Explore Different Angles**: Try both quantitative ("how many") and qualitative ("why") questions | |
- **Use Follow-ups**: Build on previous answers for deeper analysis | |
## π― Planning Modes | |
- **Pre-planning**: The agent first classifies your question, then executes analysis | |
- **Reactive Planning**: The agent dynamically decides how to approach your question | |
Choose the mode that works best for your analysis style! | |
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*Built with β€οΈ using Streamlit and powered by advanced language models* |