Wind Walker Program

 Wind Walker Program

Me: “I want to set up a small server that is always running on my Hobohost Service.

I want this Server to automatically send me a Message with a Dialog Box.
This Message is from Wind Walker, my Fictional Character.

She asks about my Plans, and how I intend on completing them.

The Dialogue Box allows me to reply. She saves the response in a Spreadsheet.

The next time she sends me a message, she refers to this Spreadsheet to continue the conversation on my Productivity that she manages.

I imagine this would need to employ Conversational LLM such as Llama3.”

ChatGPT: "Setting up a server on Hobohost that interacts with you via a character named Wind Walker, who manages your productivity, involves several components. Here's a step-by-step outline of how you can achieve this:

1. Server Setup on Hobohost

First, ensure you have a server running on Hobohost. You’ll need a script that can handle requests and responses, and a database to store your interactions.




2. Conversational LLM Integration

Using a conversational LLM like Llama3 will require setting up the model to generate responses. Here, we’ll use a hypothetical setup, assuming you have access to a suitable LLM API.

3. Scripting the Dialog System

You'll need a script to manage the dialog, store responses in a spreadsheet, and retrieve past interactions.

4. Automated Messaging

Schedule your server to send messages at regular intervals using a task scheduler like `cron`.

5. Dialog Box for User Response

Create a web interface where you can interact with the dialog box.

6. Saving and Referring to Responses

Implement a system to save responses to a spreadsheet and refer to them in future interactions.

Detailed Steps:

1. Server Setup

- Set up a basic server using Flask (Python) or any server-side framework you prefer.
- Ensure your server can handle HTTP requests and responses.


2. Conversational LLM Integration

- You can use an API to connect with Llama3 or any other LLM you have access to.
- For example, you might use OpenAI’s GPT-4 if Llama3 is unavailable:

```python
import openai
openai.api_key = 'your-api-key'
def get_response(prompt):
response = openai.Completion.create(
model="gpt-4",
prompt=prompt,
max_tokens=150
)
return response.choices[0].text.strip()
```

3. Scripting the Dialog System

- Create a Python script to handle the dialog:
```python
import csv
def save_response(user_input):
with open('responses.csv', mode='a') as file:
writer = csv.writer(file)
writer.writerow([user_input])
def get_previous_responses():
responses = []
try:
with open('responses.csv', mode='r') as file:
reader = csv.reader(file)
responses = [row[0] for row in reader]
except FileNotFoundError:
pass
return responses
def generate_prompt(responses):
conversation_history = "\n".join(responses)
prompt = f"Wind Walker: What are your plans?\nUser: {conversation_history}\nUser:"
return prompt
```

4. Automated Messaging

- Use `cron` to schedule your script:
```bash
crontab -e
```
Add a line to schedule the script:
```bash
0 9 * * * /usr/bin/python3 /path/to/your/script.py
```


5. Dialog Box for User Response

- Create a simple web interface using Flask:
```python
from flask import Flask, request, render_template
app = Flask(__name__)
@app.route('/', methods=['GET', 'POST'])
def index():
if request.method == 'POST':
user_input = request.form['response']
save_response(user_input)
previous_responses = get_previous_responses()
prompt = generate_prompt(previous_responses)
response = get_response(prompt)
return render_template('index.html', response=response)
return render_template('index.html', response='')
if __name__ == '__main__':
app.run(debug=True)
```

Create a simple HTML template `index.html`:
```html

Wind Walker Dialog

{{ response }}

Send

```

6. Saving and Referring to Responses

- This is handled in the script by saving responses to `responses.csv` and retrieving them as needed.

Conclusion

This setup ensures that Wind Walker interacts with you based on your previous responses, helping you manage your productivity. You can further refine the dialog logic and UI as needed."

Me: “Can this be made easier with the Wick Editor?”

ChatGPT: “Yes, using the Wick Editor can simplify the process of creating an interactive dialog with your character Wind Walker. The Wick Editor is a free and open-source tool for creating interactive projects, and it supports scripting with JavaScript. Here's how you can set up your project using the Wick Editor:

Step-by-Step Guide

1. Set Up Wick Editor:

- Open the Wick Editor (either online at [wickeditor.com](https://www.wickeditor.com/) or download the desktop version).

2. Create a New Project:

- Start a new project in the Wick Editor.

3. Design the Interface:

- Design the dialog box interface where Wind Walker will interact with you. You can use the drawing tools to create a textbox for Wind Walker's message and an input field for your responses.

4. Add Scripts for Interaction:

- Use JavaScript within the Wick Editor to handle the interaction logic.



Sample Script


Here's a basic example of how you might script the interaction:

1. Create a Text Input Field:

- Draw a text input field and give it a name, e.g., `userInput`.

2. Create a Text Box for Wind Walker's Message:

- Draw a text box and give it a name, e.g., `windWalkerMessage`.

3. Add a Submit Button:

- Draw a button and add a script to handle the submission of the user's response.

Example Wick Editor Script:


```javascript
// Initialize the responses array to store user responses
let responses = [];

// Function to get the Wind Walker message based on previous responses
function getWindWalkerMessage() {
// Generate the prompt using previous responses
let conversationHistory = responses.join("\n");
let prompt = `Wind Walker: What are your plans?\nUser: ${conversationHistory}\nUser:`;

// Use a simple placeholder for LLM response
// In a real scenario, this would be an API call to a conversational LLM
let response = generateLLMResponse(prompt);

return response;
}

// Placeholder function to simulate LLM response
function generateLLMResponse(prompt) {
// Simulate different responses based on the prompt
if (prompt.includes("plan")) {
return "Wind Walker: That's a great plan! How will you start?";
} else {
return "Wind Walker: Tell me more about your plans.";
}
}

// Function to handle the user's response submission
function handleSubmit() {
// Get user input
let userInput = project.userInput.text;

// Save the user input
responses.push(userInput);

// Get Wind Walker's next message
let windWalkerMessage = getWindWalkerMessage();

// Update the Wind Walker message text box
project.windWalkerMessage.text = windWalkerMessage;

// Clear the user input field
project.userInput.text = '';
}

// Attach the handleSubmit function to the submit button
project.submitButton.on('click', handleSubmit);
```

Save Responses to a Spreadsheet


Since the Wick Editor runs in the browser, saving responses to a spreadsheet would require backend support. However, you can simulate saving data within the project or use browser storage:

1. Local Storage:

```javascript
function saveResponses() {
localStorage.setItem('responses', JSON.stringify(responses));
}

function loadResponses() {
let savedResponses = localStorage.getItem('responses');
if (savedResponses) {
responses = JSON.parse(savedResponses);
}
}

// Call loadResponses on project load
loadResponses();
```

Conclusion

Using the Wick Editor simplifies the visual and interactive aspects of your project, allowing you to focus more on the interaction design rather than server setup and complex backend logic. While it might be less powerful than a full server setup with LLM integration, it's a great way to prototype and create engaging interactive projects.”