Update README. (#97)

README.md

@@ -50,12 +50,39 @@ Important: Free-tier OpenAI accounts may be subject to rate limits, which could
 
 
 ## Usage
-Before launching AutoGluon Assistant (AG-A), prepare your data files in the following structure. Here's an example using a dataset from the Titanic Kaggle Competition:
+
+We support two ways of using AutoGluoon Assistant: WebUI and CLI. 
+
+### Web UI
+The Autogluon Assistant Web UI is a user-friendly application that allows users to leverage the capabilities of the Autogluon-Assistant library through an intuitive web interface.
+
+The web UI enables users to upload datasets, configure Autogluon-Assistant runs with customized settings, preview data, monitor execution progress, view and download results, and supports secure, isolated sessions for concurrent users.
+
+#### To run the Autogluon Assistant Web UI:
+Navigate to the project directory and run the app:
+````
+cd src/autogluon_assistant/ui && streamlit run app.py
+````
+The Autogluon Assistant Web UI should now be accessible in your web browser at `http://localhost:8501`
+
+#### Add GPT4 Model to the LLM Option:
+If you’d like to add additional GPT4 model to the language model (LLM) dropdown:
+
+1. Navigate to src/autogluon_assistant/WebUI/constants.py
+
+2. Locate the `LLM_OPTIONS` variable, which looks like this:
+````
+LLM_OPTIONS = ["Claude 3.5 with Amazon Bedrock"]
+````
+3. Add "GPT 4o" to the list
+````
+LLM_OPTIONS = ["Claude 3.5 with Amazon Bedrock", "GPT 4o"]
+````
+
+### CLI
+
+Before launching AG-A CLI, prepare your data files in the following structure:
 ```
-.
-├── config # Configuration files directory
-│ └── [CONFIG_FILE].yaml # Your configuration file
-│
 └── data # Data files directory
     ├── train.[ext] # Training dataset (required)
     ├── test.[ext]  # Test dataset (required)
@@ -72,50 +99,23 @@ Note:
 
 Now you can launch the AutoGluon Assistant run using the following command:
 ```
-aga [NAME_OF_DATA_DIR]
-# e.g. aga ./toy_data
+aga [NAME_OF_DATA_DIR] --presets [PRESET_QUALITY]
+# e.g. aga ./toy_data --presets best_quality
 ```
 
-After the run is complete, model predictions on test dataset are saved into the `aga-output-<timestamp>.csv` file which is formatted according to `sample_submission.csv` file.
+We support three presets, including `medium_quality`, `high_quality` and `best_quality`. We use `best_quality` as a default setting.
 
-## Overriding parameters from the command-line
-AutoGluon Assistant uses [Hydra](https://hydra.cc) to manage configuration. See [here](https://hydra.cc/docs/advanced/override_grammar/basic/) for the complete override syntax.
-You can override specific settings in the YAML configuration defined in [`config.yaml`](https://github.com/autogluon/autogluon-assistant/blob/main/config/config.yaml) using
+After the run is complete, model predictions on test dataset are saved into the `aga-output-<timestamp>.csv` file. It will be formatted according to optional `sample_submission.csv` file if provided.
+
+#### Overriding Configs
+You can override specific settings in the YAML configuration defined in the [config folder](https://github.com/boranhan/autogluon-assistant/tree/main/src/autogluon_assistant/configs) using
 the `config_overrides` parameter with Hydra syntax from the command line.
 
 Here’s an example command with some configuration overrides:
 ```
-autogluon-assistant ./data ./config --output-filename my_output.csv --config-overrides "autogluon.predictor_fit_kwargs.time_limit=120 autogluon.predictor_fit_kwargs.verbosity=3 autogluon.predictor_fit_kwargs.presets=medium_quality llm.temperature=0.7 llm.max_tokens=256"
+aga toy_data --config_overrides "feature_transformers=[], autogluon.predictor_fit_kwargs.time_limit=3600"
 
 # OR
 
-aga ./data ./config --output-filename my_output.csv --config-overrides "autogluon.predictor_fit_kwargs.time_limit=120 autogluon.predictor_fit_kwargs.verbosity=3 autogluon.predictor_fit_kwargs.presets=medium_quality llm.temperature=0.7 llm.max_tokens=256"
-```
-
-`autogluon-assistant-tools` provides more functionality and utilities for benchmarking, wrapped around autogluon-assistant. Please check out the [repo](https://github.com/autogluon/autogluon-assistant-tools/) for more details.
-
-## Autogluon Assistant Web UI
-The Autogluon Assistant Web UI is a user-friendly application that allows users to leverage the capabilities of the Autogluon-Assistant library through an intuitive web interface.
-
-The web UI enables users to upload datasets, configure Autogluon-Assistant runs with customized settings, preview data, monitor execution progress, view and download results, and supports secure, isolated sessions for concurrent users.
-
-#### To run the Autogluon Assistant Web UI:
-Navigate to the project directory and run the app:
-````
-cd src/autogluon_assistant/ui && streamlit run app.py
-````
-The Autogluon Assistant Web UI should now be accessible in your web browser at `http://localhost:8501`
-
-#### Add GPT4 Model to the LLM Option:
-If you’d like to add additional GPT4 model to the language model (LLM) dropdown:
-
-1. Navigate to src/autogluon_assistant/WebUI/constants.py
-
-2. Locate the `LLM_OPTIONS` variable, which looks like this:
-````
-LLM_OPTIONS = ["Claude 3.5 with Amazon Bedrock"]
-````
-3. Add "GPT 4o" to the list
-````
-LLM_OPTIONS = ["Claude 3.5 with Amazon Bedrock", "GPT 4o"]
-````
+aga toy_data --config_overrides "feature_transformers=[]" --config_overrides "autogluon.predictor_fit_kwargs.time_limit=3600"
+```