Skip to content

Commit

Permalink
Update README.md include WebUI video demo
Browse files Browse the repository at this point in the history
  • Loading branch information
ZhengyaoJiang authored Nov 27, 2024
1 parent 113f409 commit eff469b
Showing 1 changed file with 56 additions and 51 deletions.
107 changes: 56 additions & 51 deletions README.md
Original file line number Diff line number Diff line change
Expand Up @@ -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:

Expand All @@ -39,8 +94,6 @@ export OPENAI_API_KEY=<your API key>
export ANTHROPIC_API_KEY=<your API key>
```

## Running AIDE via the Command Line

To run AIDE:

```bash
Expand Down Expand Up @@ -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:
Expand Down

0 comments on commit eff469b

Please sign in to comment.