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README.md

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-# LLM-powered Data Analyst Agent
-
-This Streamlit application uses an LLM-powered agent to analyze the Bitext Customer Support LLM Chatbot Training Dataset. The agent can answer user questions about the dataset, performing both structured (quantitative) and unstructured (qualitative) analysis.
-
-## Features
-
-- Ask questions about the customer support dataset
-- Support for different types of analysis:
-  - Structured (Quantitative): Category frequencies, examples, intent distributions
-  - Unstructured (Qualitative): Summarize categories, analyze intents
-- Scope detection to identify if questions are answerable from the dataset
-- Support for follow-up questions
-- Toggle between planning modes:
-  - Pre-planning + Execution: First classify the question, then execute the response
-  - ReActive Dynamic Planning: Let the LLM dynamically plan and execute the response
-
-## Setup
-
-1. Clone this repository
-2. Install the required dependencies:
-   ```
-   pip install -r requirements.txt
-   ```
-3. Run the Streamlit app:
-   ```
-   streamlit run app.py
-   ```
-4. Enter your OpenAI API key when prompted
-
-## Example Questions
-
-- "What are the most frequent categories?"
-- "Show examples of billing category"
-- "What categories exist in the dataset?"
-- "Summarize the technical support category"
-- "What are the common intents in the billing category?"
-- "How do agents typically respond to refund requests?"
-
-## Requirements
-
-- Python 3.8+
-- OpenAI API key (gpt-4o model access)
-- Internet connection (to download the dataset)