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Text-Based Chess App with LLM Integration
This video demonstrates how to use the Text-Based Chess App with LLM Integration template.
import logging
from abilities import apply_llm_prompt_to_every_item
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.WARNING)
def get_llm_move(moves_list):
prompt = f"Given the list of chess moves {moves_list}, what is the next best move?"
user_language = "en"
expected_response_size = 1500
llm_model_name = "gpt-3.5-turbo-16k"
result = apply_llm_prompt_to_every_item(
prompt, [""], user_language, expected_response_size, llm_model_name
)
return result["responses"][0]
def main():
moves_list = []
while True:
user_move = input("Enter your move: ")
moves_list.append(user_move)
print("Moves so far:", moves_list)Frequently Asked Questions
How can this Text-Based Chess App with LLM Integration be used for educational purposes?
The Text-Based Chess App with LLM Integration can be a valuable educational tool for chess learners. It provides an interactive way for students to practice chess moves and receive immediate feedback from the LLM. The app can be used in chess classes or workshops to demonstrate different strategies and analyze game progression. Additionally, the move history feature allows for post-game analysis, helping students understand the flow of the game and learn from their decisions.
What are the potential business applications of this chess app beyond recreational use?
While the Text-Based Chess App with LLM Integration is great for recreational use, it has several business applications:
How can the Text-Based Chess App be monetized?
There are several ways to monetize the Text-Based Chess App with LLM Integration:
How can I modify the app to use a different LLM model?
To use a different LLM model in the Text-Based Chess App, you need to modify the get_llm_move function. Specifically, you should change the llm_model_name parameter in the apply_llm_prompt_to_every_item function call. Here's an example of how to modify the code to use a different model:
python
def get_llm_move(moves_list):
prompt = f"Given the list of chess moves {moves_list}, what is the next best move?"
user_language = "en"
expected_response_size = 1500
llm_model_name = "gpt-4" # Change this to your desired model
result = apply_llm_prompt_to_every_item(
prompt, [""], user_language, expected_response_size, llm_model_name
)
return result["responses"][0]
In this example, we've changed the model to "gpt-4". Make sure the model you choose is supported by your LLM provider.
How can I add error handling to the user input in the Text-Based Chess App?
To add error handling for user input in the Text-Based Chess App, you can modify the main function to include input validation. Here's an example of how you could implement this:
```python def is_valid_move(move): # Implement chess move validation logic here # This is a simplified example return len(move) >= 2 and move[0] in 'abcdefgh' and move[1] in '12345678'
def main(): moves_list = [] while True: user_move = input("Enter your move: ") if user_move.lower() == 'quit': print("Thanks for playing!") break if not is_valid_move(user_move): print("Invalid move. Please enter a valid chess move (e.g., 'e4').") continue moves_list.append(user_move) print("Moves so far:", moves_list) llm_move = get_llm_move(moves_list) moves_list.append(llm_move) print("LLM move:", llm_move) print("Moves so far:", moves_list) ```
This modification adds a simple move validation check and allows the user to quit the game by typing 'quit'. You would need to implement more comprehensive chess move validation logic in the is_valid_move function for a production-ready app.
Created: | Last Updated:
Introduction to the Text-Based Chess App with LLM Integration Template
Are you ready to build a text-based chess application that integrates with a language model to generate chess moves? This template provides a seamless way to create an app where users can input their chess moves, and the app will respond with the next best move using a language learning model (LLM). This guide will walk you through the steps to get your app up and running on the Lazy platform.
Getting Started with the Template
To begin using this template, simply click on "Start with this Template" on the Lazy platform. This will set up the template in your Lazy Builder interface, and you can proceed with the following steps without the need to copy or paste any code.
Using the Test Button
Once you have initiated the template, you can test the functionality of your app by clicking the "Test" button. This will deploy your app and launch the Lazy CLI. If the code requires user input, you will be prompted to provide it through the Lazy app at this stage.
As you interact with the CLI, you will enter your chess moves when prompted. The app will then display the moves made so far and use the integrated LLM to generate the next move. This process will continue for each move you make, allowing you to play a game of chess against the LLM.
How to Use the Interface
The interface for this text-based chess app is straightforward. You will interact with the app through the command line, entering your moves when prompted. The app will keep track of all moves made and display them after each turn. There is no need for additional setup or integration with other services or frontends.
If you need to interact with the app's API, Lazy will provide you with a dedicated server link to use the API. However, for this particular template, there is no need to use an external API link as all interactions are handled through the command line interface.
Conclusion
By following these simple steps, you can quickly set up and start using your text-based chess app with LLM integration. The Lazy platform handles all the deployment details, so you can focus on enjoying your chess game and exploring the capabilities of the integrated language model. Happy building and enjoy your chess matches!
Template Benefits
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AI-Powered Chess Training: This template can be used to create an intelligent chess training tool, allowing players of all levels to practice against an AI opponent that adapts to their skill level and playing style.
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Accessible Chess Platform: The text-based nature of the app makes it highly accessible, enabling visually impaired users to play chess through screen readers or other assistive technologies.
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Data Collection for Chess Analysis: By logging all moves and AI responses, this template provides a foundation for collecting valuable data on chess strategies and player behaviors, which can be used for research or to improve chess AI systems.
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Low-Resource Chess Solution: The text-based interface requires minimal computational resources, making it ideal for deployment in areas with limited internet connectivity or on devices with low processing power.
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Customizable AI Chess Engine: The template's integration with LLM allows for easy customization of the AI's playing style or difficulty level, making it suitable for creating personalized chess experiences or developing specialized training programs for professional players.
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