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 ---
-title: "Chat-with-MySQL"                # Display title for the Space
-emoji: "📰"                                 # Space emoji (emoji-only character allowed)
-colorFrom: "blue"                           # Color for Thumbnail gradient
-colorTo: "green"                            # Color for Thumbnail gradient
-sdk: streamlit                              # SDK being used (gradio, streamlit, docker, or static)
-sdk_version: "1.38.0"                       # Specify the version of the selected SDK
-app_file: "src/app.py"                      # Path to your main application file
-pinned: false                               # Whether the Space stays on top of your profile
+title: Chat with MySQL
+emoji: 💬
+colorFrom: purple
+colorTo: blue
+sdk: streamlit
+sdk_version: "1.0"
+app_file: app.py
+pinned: false
 ---
-# MySQL Python Chatbot with GPT-4 and Mistral AI
 
-Welcome to the GitHub repository for our tutorial on building a natural language SQL chatbot using GPT-4! This project guides you through the development of a chatbot that can interpret natural language queries, generate SQL queries, and fetch results from a SQL database, all in an intuitive and user-friendly way. It utilizes the power of OpenAI's GPT-4 model, integrated with a Streamlit GUI for an enhanced interaction experience.
+# Chat with MySQL
 
-🟡 This repository serves as supporting material for the [YouTube video tutorial](https://youtu.be/YqqRkuizNN4).
+This is a Streamlit application that allows users to interact with a MySQL database via natural language queries. The app uses LangChain, Groq, and Streamlit to generate SQL queries and respond with database results in natural language.
 
 ## Features
-- **Natural Language Processing**: Uses GPT-4 to interpret and respond to user queries in natural language.
-- **SQL Query Generation**: Dynamically generates SQL queries based on the user's natural language input.
-- **Database Interaction**: Connects to a SQL database to retrieve query results, demonstrating practical database interaction.
-- **Streamlit GUI**: Features a user-friendly interface built with Streamlit, making it easy for users of all skill levels.
-- **Python-based**: Entirely coded in Python, showcasing best practices in software development with modern technologies.
-
-## Brief Explanation of How the Chatbot Works
-
-The chatbot works by taking a user's natural language query, converting it into a SQL query using GPT-4, executing the query on a SQL database, and then presenting the results back to the user in natural language. This process involves several steps of data processing and interaction with the OpenAI API and a SQL database, all seamlessly integrated into a Streamlit application.
-
-Consider the following diagram to understand how the different chains and components are built:
-
-![Chatbot Architecture](./docs/mysql-chains.png)
-
-For a more detailed explanation and a step-by-step guide, refer this other video: [YouTube video tutorial](https://youtu.be/9ccl1_Wu24Q).
-
-For a more detailed explanation and a step-by-step guide, refer to the [YouTube video tutorial](Chat with MySQL Database with Python | LangChain Tutorial).
-
-## Installation
-Ensure you have Python installed on your machine. Then clone this repository:
-
-```bash
-git clone [repository-link]
-cd [repository-directory]
-```
-
-Install the required packages:
-
-```bash
+- Connect to your MySQL database and chat with it using natural language.
+- Automatically generate SQL queries based on your questions.
+- Receive responses both in SQL and human-readable formats.
+
+## Libraries and Tools Used
+- **dotenv**: Loads environment variables from a `.env` file.
+- **LangChain**: Handles the prompt templates and chains for generating SQL queries and responses.
+- **Groq**: Utilized as the model for chat-based interactions and SQL generation.
+- **Streamlit**: Provides the interface for interacting with the database and handling the conversation.
+- **SQLDatabase**: LangChain's utility to manage SQL database connections and queries.
+  
+## Setup Instructions
+
+1. Clone the repository:
+   ```bash
+   git clone https://github.com/your-repo/chat-with-mysql.git
+   cd chat-with-mysql
+Install the required Python libraries:
+
+bash
+Copy code
 pip install -r requirements.txt
-```
-
-Create your own .env file with the necessary variables, including your OpenAI API key:
-
-```bash
-OPENAI_API_KEY=[your-openai-api-key]
-```
-
-## Usage
-To launch the Streamlit app and interact with the chatbot:
-
-```bash
+Create a .env file in the root directory of the project and add your database credentials:
+
+bash
+Copy code
+DB_USER=root
+DB_PASSWORD=admin
+DB_HOST=localhost
+DB_PORT=3306
+DB_NAME=Chinook
+Run the application:
+
+bash
+Copy code
 streamlit run app.py
-```
-
-## Contributing
-As this repository accompanies the [YouTube video tutorial](https://youtu.be/YqqRkuizNN4), we are primarily focused on providing a comprehensive learning experience. Contributions for bug fixes or typos are welcome.
+Open your browser and go to the Streamlit web app, typically at http://localhost:8501.
 
-## License
-This project is licensed under the MIT License - see the LICENSE file for details.
-
----
-
-**Note**: This project is intended for educational and research purposes. Please ensure compliance with the terms of use and guidelines of any APIs or services used.
-
----
+How It Works
+The app connects to a MySQL database using credentials from environment variables.
+It uses a LangChain model to process user queries, convert them into SQL statements, and return the results.
+You can view the SQL query generated from your questions and the corresponding response.
+Configuration
+Check out the configuration reference at Hugging Face Spaces Config Reference.
 
-We hope this repository aids in your exploration of integrating AI with web technologies. For more informative tutorials, be sure to check out [Your YouTube Channel].
-
-Happy Coding! 🚀👨‍💻🤖
-
----
-
-*If you find this project helpful, please consider giving it a star!*
-
----