README.md

metadata

title: LLM Data Analyst Agent
emoji: 🤖
colorFrom: blue
colorTo: green
sdk: streamlit
sdk_version: 1.32.0
app_file: app.py
pinned: false
license: apache-2.0

🤖 LLM-powered Data Analyst Agent

An intelligent data analysis assistant that helps you explore and understand customer support datasets using advanced language models.

🌟 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

🚀 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"

🛠️ 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

📊 Dataset

This app analyzes the Bitext Customer Support 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

🔧 Configuration

The app requires a Nebius API key to function. This has been configured as an environment variable for this Space.

💡 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

🎯 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 ❤️ using Streamlit and powered by advanced language models