[MRG] Updated the readme with v0.4.2

README.md

@@ -4,39 +4,42 @@
 <a href="https://trendshift.io/repositories/11658" target="_blank"><img src="https://trendshift.io/api/badge/repositories/11658" alt="MLSysOps%2FMLE-agent | Trendshift" style="width: 250px; height: 200px;" width="250" height="200px"/></a>
 <p align="center">:love_letter: Fathers' love for Kaia :love_letter:</p>
 
-![](https://github.com/MLSysOps/MLE-agent/actions/workflows/lint.yml/badge.svg) 
-![](https://github.com/MLSysOps/MLE-agent/actions/workflows/test.yml/badge.svg) 
+![](https://github.com/MLSysOps/MLE-agent/actions/workflows/lint.yml/badge.svg)
+![](https://github.com/MLSysOps/MLE-agent/actions/workflows/test.yml/badge.svg)
 ![PyPI - Version](https://img.shields.io/pypi/v/mle-agent)
 [![Downloads](https://static.pepy.tech/badge/mle-agent)](https://pepy.tech/project/mle-agent)
 ![GitHub License](https://img.shields.io/github/license/MLSysOps/MLE-agent)
 <a href="https://discord.gg/SgxBpENGRG"><img src="https://img.shields.io/badge/Discord-Join%20Us-purple?logo=discord&logoColor=white&style=flat" alt="Join our Discord community"></a>
 
-[📚 Docs](https://mle-agent-site.vercel.app/) | 
-[🐞 Report Issues](https://github.com/MLSysOps/MLE-agent/issues/new) | 
+[📚 Docs](https://mle-agent-site.vercel.app/) |
+[🐞 Report Issues](https://github.com/MLSysOps/MLE-agent/issues/new) |
 👋 Join us on <a href="https://discord.gg/SgxBpENGRG" target="_blank">Discord</a>
 
 </div>
 
-
 ## Overview
 
 MLE-Agent is designed as a pairing LLM agent for machine learning engineers and researchers. It is featured by:
 
-- 🤖 Autonomous Baseline Creation: Automatically builds ML/AI baselines.
-- 🔍 [Arxiv](https://arxiv.org/) and [Papers with Code](https://paperswithcode.com/) Integration: Access best practices and state-of-the-art methods.
+- 🤖 Autonomous Baseline: Automatically builds ML/AI baselines and solutions based on your requirements.
+- 🏅End-to-end ML Task: Participates in Kaggle competitions and completes tasks independently.
+- 🔍 [Arxiv](https://arxiv.org/) and [Papers with Code](https://paperswithcode.com/) Integration: Access best practices
+  and state-of-the-art methods.
 - 🐛 Smart Debugging: Ensures high-quality code through automatic debugger-coder interactions.
 - 📂 File System Integration: Organizes your project structure efficiently.
 - 🧰 Comprehensive Tools Integration: Includes AI/ML functions and MLOps tools for a seamless workflow.
 - ☕ Interactive CLI Chat: Enhances your projects with an easy-to-use chat interface.
+- 🧠 Smart Advisor: Provides personalized suggestions and recommendations for your ML/AI project.
 - 📊 Weekly Report: Automatically generates detailed summaries of your weekly works.
 
-
 https://github.com/user-attachments/assets/dac7be90-c662-4d0d-8d3a-2bc4df9cffb9
 
 ## Milestones
 
-- :rocket: 09/10/2024: Release the `0.4.0` with new CLIs like `MLE report`, `MLE kaggle`, `MLE integration` and many new models like `Mistral`.
-- :rocket: 07/25/2024: Release the `0.3.0` with huge refactoring, many integrations, etc (v0.3.0)
+- :rocket: 09/24/2024: Release the `0.4.2` with enhanced `Auto-Kaggle` mode to complete an end-to-end competition with minimal effort.
+- :rocket: 09/10/2024: Release the `0.4.0` with new CLIs like `MLE report`, `MLE kaggle`, `MLE integration` and many new
+  models like `Mistral`.
+- :rocket: 07/25/2024: Release the `0.3.0` with huge refactoring, many integrations, etc. (v0.3.0)
 - :rocket: 07/11/2024: Release the `0.2.0` with multiple agents interaction (v0.2.0)
 - 👨‍🍼 **07/03/2024: Kaia is born**
 - :rocket: 06/01/2024: Release the first rule-based version of MLE agent (v0.1.0)
@@ -58,7 +61,8 @@ pip install -e .
 mle new <project name>
 ```
 
-And a project directory will be created under the current path, you need to start the project under the project directory.
+And a project directory will be created under the current path, you need to start the project under the project
+directory.
 
 ```bash
 cd <project name>
@@ -73,81 +77,108 @@ mle chat
 
 ## Use cases
 
+### 🧪 Prototype an ML Baseline
+
+MLE agent can help you prototype an ML baseline with the given requirements, and test the model on the local machine.
+The requirements can be vague, such as "I want to predict the stock price based on the historical data".
+
+```bash
+cd <project name>
+mle start
+```
+
 ### :bar_chart: Generate Work Report
 
-MLE agent can help you summarize your weekly report, including development progress, communication notes, and to-do lists.
+MLE agent can help you summarize your weekly report, including development progress, communication notes, reference, and
+to-do lists.
 
 ```bash
 cd <project name>
 mle report
 ```
+
 Then, you can visit http://localhost:3000/ to generate your report locally.
 
 ### :trophy: Start with Kaggle Competition
 
-MLE agent can participate in Kaggle competitions and finish coding and debugging from data preparation to model training independently.
-For more details, see the [MLE-Agent Tutorials](https://mle-agent-site.vercel.app/tutorial/Start_a_kaggle_task).
+MLE agent can participate in Kaggle competitions and finish coding and debugging from data preparation to model training
+independently. Here is the basic command to start a Kaggle competition:
 
 ```bash
 cd <project name>
 mle kaggle
 ```
 
+Or you can let the agents finish the Kaggle task without human interaction if you have the dataset and submission file
+ready:
+
+```bash
+cd <project name>
+mle kaggle --auto \
+--datasets "<path_to_dataset1>,<path_to_dataset2>" \
+--description "<description_file_path_or_text>" \
+--submission "<submission_file_path>" \
+--sub_example "<submission_example_file_path>" \ 
+--comp_id "<competition_id>"
+```
+
+Please make sure you have joined the competition before running the command. For more details, see the [MLE-Agent Tutorials](https://mle-agent-site.vercel.app/tutorial/Start_a_kaggle_task).
+
 ## Roadmap
 
 The following is a list of the tasks we plan to do, welcome to propose something new!
 
 <details>
   <summary><b> :hammer: General Features</b></summary>
-
-  - [x] Understand users' requirements to create an end-to-end AI project
-  - [x] Suggest the SOTA data science solutions by using the web search
-  - [x] Plan the ML engineering tasks with human interaction
-  - [x] Execute the code on the local machine/cloud, debug and fix the errors
-  - [x] Leverage the built-in functions to complete ML engineering tasks
-  - [x] Interactive chat: A human-in-the-loop mode to help improve the existing ML projects
-  - [x] Kaggle mode: to finish a Kaggle task without humans
-  - [ ] Summary and reflect the whole ML/AI pipeline
-  - [ ] Integration with Cloud data and testing and debugging platforms
-  - [x] Local RAG support to make personal ML/AI coding assistant
-  - [ ] Function zoo: generate AI/ML functions and save them for future usage
 
+- [x] Understand users' requirements to create an end-to-end AI project
+- [x] Suggest the SOTA data science solutions by using the web search
+- [x] Plan the ML engineering tasks with human interaction
+- [x] Execute the code on the local machine/cloud, debug and fix the errors
+- [x] Leverage the built-in functions to complete ML engineering tasks
+- [x] Interactive chat: A human-in-the-loop mode to help improve the existing ML projects
+- [x] Kaggle mode: to finish a Kaggle task without humans
+- [x] Summary and reflect the whole ML/AI pipeline
+- [ ] Integration with Cloud data and testing and debugging platforms
+- [x] Local RAG support to make personal ML/AI coding assistant
+- [ ] Function zoo: generate AI/ML functions and save them for future usage
 
 </details>
 
 <details>
   <summary><b>:star: More LLMs and Serving Tools</b></summary>
-
-  - [x] Ollama LLama3
-  - [x] OpenAI GPTs
-  - [x] Anthropic Claude 3.5 Sonnet
+
+- [x] Ollama LLama3
+- [x] OpenAI GPTs
+- [x] Anthropic Claude 3.5 Sonnet
+
 </details>
 
 <details>
   <summary><b>:sparkling_heart: Better user experience</b></summary>
 
-  - [x] CLI Application
-  - [x] Web UI
-  - [x] Discord
+- [x] CLI Application
+- [x] Web UI
+- [x] Discord
+
 </details>
 
 <details>
   <summary><b>:jigsaw: Functions and Integrations</b></summary>
-
-  - [x] Local file system
-  - [x] Local code exectutor
-  - [x] Arxiv.org search
-  - [x] Papers with Code search
-  - [x] General keyword search
-  - [ ] Hugging Face
-  - [ ] SkyPilot cloud deployment
-  - [ ] Snowflake data
-  - [ ] AWS S3 data
-  - [ ] Databricks data catalog
-  - [ ] Wandb experiment monitoring
-  - [ ] MLflow management
-  - [ ] DBT data transform
-</details>
+
+- [x] Local file system
+- [x] Local code exectutor
+- [x] Arxiv.org search
+- [x] Papers with Code search
+- [x] General keyword search
+- [ ] Hugging Face
+- [ ] SkyPilot cloud deployment
+- [ ] Snowflake data
+- [ ] AWS S3 data
+- [ ] Databricks data catalog
+- [ ] Wandb experiment monitoring
+- [ ] MLflow management
+- [ ] DBT data transform
 
 </details>