diff --git a/README.md b/README.md index 6e8333a..e906389 100644 --- a/README.md +++ b/README.md @@ -21,7 +21,62 @@ More specifically, AIDE has the following features: # How to Use AIDE? -## Setup +## Running AIDE via the Web UI + + +https://github.com/user-attachments/assets/1da42853-fe36-45e1-b6a2-852f88470af6 + + +We have developed a user-friendly Web UI using Streamlit to make it even easier to interact with AIDE. + +### Prerequisites + +Ensure you have installed the development version of AIDE and its dependencies as described in the [Development](#development) section. + +### Running the Web UI + +Navigate to the `aide/webui` directory and run the Streamlit application: + +```bash +cd aide/webui +streamlit run app.py +``` + +Alternatively, you can run it from the root directory: + +```bash +streamlit run aide/webui/app.py +``` + +### Using the Web UI + +1. **API Key Configuration**: In the sidebar, input your OpenAI API key or Anthropic API key and click "Save API Keys". + +2. **Input Data**: + - You can either **upload your dataset files** (`.csv`, `.txt`, `.json`, `.md`) using the "Upload Data Files" feature. + - Or click on "Load Example Experiment" to use the example house prices dataset. + +3. **Define Goal and Evaluation Criteria**: + - In the "Goal" text area, describe what you want the model to achieve (e.g., "Predict the sales price for each house"). + - In the "Evaluation Criteria" text area, specify the evaluation metric (e.g., "Use the RMSE metric between the logarithm of the predicted and observed values."). + +4. **Configure Steps**: + - Use the slider to set the number of steps (iterations) for the experiment. + +5. **Run the Experiment**: + - Click on "Run AIDE" to start the experiment. + - Progress and status updates will be displayed in the "Results" section. + +6. **View Results**: + - **Tree Visualization**: Explore the solution tree to understand how AIDE experimented and optimized the models. + - **Best Solution**: View the Python code of the best solution found. + - **Config**: Review the configuration used for the experiment. + - **Journal**: Examine the detailed journal entries for each step. + + +## Running AIDE via the Command Line + +### Setup Make sure you have `Python>=3.10` installed and run: @@ -39,8 +94,6 @@ export OPENAI_API_KEY= export ANTHROPIC_API_KEY= ``` -## Running AIDE via the Command Line - To run AIDE: ```bash @@ -108,54 +161,6 @@ AIDE supports using local LLMs through OpenAI-compatible APIs. Here's how to set eval="Use the RMSE metric between the logarithm of the predicted and observed values." ``` -## Running AIDE via the Web UI - -We have developed a user-friendly Web UI using Streamlit to make it even easier to interact with AIDE. - -### Prerequisites - -Ensure you have installed the development version of AIDE and its dependencies as described in the [Development](#development) section. - -### Running the Web UI - -Navigate to the `aide/webui` directory and run the Streamlit application: - -```bash -cd aide/webui -streamlit run app.py -``` - -Alternatively, you can run it from the root directory: - -```bash -streamlit run aide/webui/app.py -``` - -### Using the Web UI - -1. **API Key Configuration**: In the sidebar, input your OpenAI API key or Anthropic API key and click "Save API Keys". - -2. **Input Data**: - - You can either **upload your dataset files** (`.csv`, `.txt`, `.json`, `.md`) using the "Upload Data Files" feature. - - Or click on "Load Example Experiment" to use the example house prices dataset. - -3. **Define Goal and Evaluation Criteria**: - - In the "Goal" text area, describe what you want the model to achieve (e.g., "Predict the sales price for each house"). - - In the "Evaluation Criteria" text area, specify the evaluation metric (e.g., "Use the RMSE metric between the logarithm of the predicted and observed values."). - -4. **Configure Steps**: - - Use the slider to set the number of steps (iterations) for the experiment. - -5. **Run the Experiment**: - - Click on "Run AIDE" to start the experiment. - - Progress and status updates will be displayed in the "Results" section. - -6. **View Results**: - - **Tree Visualization**: Explore the solution tree to understand how AIDE experimented and optimized the models. - - **Best Solution**: View the Python code of the best solution found. - - **Config**: Review the configuration used for the experiment. - - **Journal**: Examine the detailed journal entries for each step. - ## Using AIDE in Python Using AIDE within your Python script/project is easy. Follow the setup steps above, and then create an AIDE experiment like below and start running: