Added Automated Code Generation AI Agent Tutorial

ai_agent_tutorials/ai_coding_agent/.gitignore

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+# Byte-compiled / optimized / DLL files
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+*$py.class
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ai_agent_tutorials/ai_coding_agent/LICENSE

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+MIT License
+
+Copyright (c) 2024 Sakalya Mitra
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

ai_agent_tutorials/ai_coding_agent/README.md

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+# AutoTabML - Automated Machine Learning Code Generator for Tabular Data
+
+AutoTabML is an innovative application designed to automate the generation of machine learning code for tabular data. Utilizing CrewAI and the Groq Llama 70B model, AutoTabML simplifies the process of building and debugging machine learning models for both regression and classification problems. With this tool, you can generate working code, debug errors, and run your code without writing a single line of code manually.
+
+## Features
+
+- **Automated Code Generation**: Generate Python code for machine learning tasks based on your tabular dataset and problem description.
+- **EDA and Feature Engineering**: Perform comprehensive Exploratory Data Analysis (EDA) and feature engineering.
+- **Model Recommendation**: Get suggestions for the most suitable machine learning models for your problem.
+- **Code Modification and Debugging**: Modify generated code based on user suggestions and debug errors effortlessly.
+- **In-app Execution**: Run the generated code within the application and view the results without the need for external IDEs or additional installations.
+
+## How It Works
+
+AutoTabML leverages multiple agents, each specializing in different aspects of the machine learning pipeline. Here's a brief overview of the agents and their roles:
+
+- **Data Reader Agent**: Reads and loads the uploaded dataset.
+- **Problem Definition Agent**: Clarifies the machine learning problem based on user input.
+- **EDA Agent**: Performs exploratory data analysis to understand data characteristics.
+- **Feature Engineering Agent**: Executes feature engineering based on EDA results.
+- **Model Recommendation Agent**: Suggests the most suitable machine learning models.
+- **Starter Code Generator Agent**: Generates the initial Python code template for the project.
+- **Code Modification Agent**: Adapts the generated code according to user feedback.
+- **Code Debugger Agent**: Debugs the generated code to fix any issues.
+- **Compiler Agent**: Extracts and compiles the Python code.
+
+## Technology Used
+- Python
+- CrewAI
+- Groq
+- Streamlit
+
+## Demo
+
+
+## Getting Started
+
+### Prerequisites
+
+- Required Python packages (listed in `requirements.txt`)
+
+### Installation
+
+1. Clone the repository:
+
+   ```bash
+   git clone https://github.com/Sakalya100/AutoTabML.git
+   cd AutoTabML
+   ```
+
+2. Create and activate a virtual environment:
+
+   ```bash
+   python3 -m venv venv
+   source venv/bin/activate
+   ```
+
+3. Install the required packages:
+
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+4. Set up the environment variables by creating a `.env` file in the root directory and adding your Groq API key:
+
+   ```
+   GROQ_API_KEY=your_groq_api_key
+   ```
+
+### Usage
+
+1. Run the Streamlit application:
+
+   ```bash
+   streamlit run app.py
+   ```
+
+2. Open your web browser and go to `http://localhost:8501`.
+
+3. Describe your machine learning problem and upload a sample CSV of your dataset.
+
+4. Click on "Process" to generate the initial code. You can then modify, debug, and run the code directly within the application.
+
+### Example Workflow
+
+1. **Describe Your Problem**: Enter a detailed description of the machine learning problem you want to solve.
+2. **Upload Dataset**: Upload your dataset in CSV format.
+3. **Generate Code**: Click the "Process" button to generate the initial Python code.
+4. **Modify and Debug**: Use the provided text areas to suggest code modifications or paste error messages for debugging.
+5. **Run the Code**: Execute the generated code and view the results, including any plots or outputs generated during execution.
+
+### Contributors
+
+- **Sakalya Mitra**   - **Shalu Singh**
+
+## License
+
+This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.