diff --git a/ai_agent_tutorials/ai_data_visualisation_agent/README.md b/ai_agent_tutorials/ai_data_visualisation_agent/README.md
index 802d724..7abffe4 100644
--- a/ai_agent_tutorials/ai_data_visualisation_agent/README.md
+++ b/ai_agent_tutorials/ai_data_visualisation_agent/README.md
@@ -1,43 +1,41 @@
-# AI Data Visualization Agent
-
-This Assistant is designed to help anyone create and visualize data using natural language commands, and it is built using Together AI and E2B Code Interpreter. User gets to upload a dataset and ask questions to the LLM to get the data visualized. This demo can be considered as a demo for the E2B Code Interpreter and Together AI, for anyone who's getting started with these libraries!
-
-## Demo
-
-https://github.com/user-attachments/assets/d8414c37-5edd-4e4d-a7b1-b9ab500bd8cd
+# 📊 AI Data Visualization Agent
+A Streamlit application that acts as your personal data visualization expert, powered by LLMs. Simply upload your dataset and ask questions in natural language - the AI agent will analyze your data, generate appropriate visualizations, and provide insights through a combination of charts, statistics, and explanations.
 
 ## Features
+#### Natural Language Data Analysis
+- Ask questions about your data in plain English
+- Get instant visualizations and statistical analysis
+- Receive explanations of findings and insights
+- Interactive follow-up questioning
 
-- 🎨 Natural language-driven visualization creation
-- 📊 Support for multiple chart types (line, bar, scatter, pie, bubble)
-- 📈 Automatic data preprocessing and cleaning
-- 🎯 Available Models:
-  - Meta-Llama 3.1 405B
-  - DeepSeek V3
-  - Qwen 2.5 7B
-  - Meta-Llama 3.3 70B
-- 📱 The Code runs in the E2B Sandbox environment, so it is secure and fast
-- Streamlit for clear and interactive user interface
+#### Intelligent Visualization Selection
+- Automatic choice of appropriate chart types
+- Dynamic visualization generation
+- Statistical visualization support
+- Custom plot formatting and styling
+
+#### Multi-Model AI Support
+- Meta-Llama 3.1 405B for complex analysis
+- DeepSeek V3 for detailed insights
+- Qwen 2.5 7B for quick analysis
+- Meta-Llama 3.3 70B for advanced queries
 
 ## How to Run
 
 Follow the steps below to set up and run the application:
-Before anything else, Please get a free Together AI API Key here: https://api.together.ai/signin
-Get a free E2B API Key here: https://e2b.dev/ ; https://e2b.dev/docs/legacy/getting-started/api-key
+- Before anything else, Please get a free Together AI API Key here: https://api.together.ai/signin
+- Get a free E2B API Key here: https://e2b.dev/ ; https://e2b.dev/docs/legacy/getting-started/api-key
 
-1. **Clone the Repository**:
+1. **Clone the Repository**
    ```bash
    git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
    cd ai_agent_tutorials/ai_data_visualisation_agent
    ```
-
 2. **Install the dependencies**
     ```bash
     pip install -r requirements.txt
     ```
-
 3. **Run the Streamlit app**
     ```bash
     streamlit run ai_data_visualisation_agent.py
-    ```
-
+    ```
\ No newline at end of file
diff --git a/ai_agent_tutorials/ai_data_visualisation_agent/ai_data_visualisation_agent.py b/ai_agent_tutorials/ai_data_visualisation_agent/ai_data_visualisation_agent.py
index 5260b4e..226bb55 100644
--- a/ai_agent_tutorials/ai_data_visualisation_agent/ai_data_visualisation_agent.py
+++ b/ai_agent_tutorials/ai_data_visualisation_agent/ai_data_visualisation_agent.py
@@ -89,7 +89,7 @@ def upload_dataset(code_interpreter: Sandbox, uploaded_file) -> str:
 
 def main():
     """Main Streamlit application."""
-    st.title("AI Data Visualization Agent")
+    st.title("📊 AI Data Visualization Agent")
     st.write("Upload your dataset and ask questions about it!")
 
     # Initialize session state variables