Lighthouse for AI agents

# Complete Working Chatbot with OpenAI and PickitBox Motivation Tool
# This example creates a chatbot that can use the PickitBox Motivation API for its own self-motivation

import os
import openai
from openai import OpenAI
import dotenv

# Load environment variables from .env file
dotenv.load_dotenv()

# Get API keys from environment variables
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
PICKITBOX_API_KEY = os.getenv("PICKITBOX_API_KEY")

# Initialize the OpenAI client
client = OpenAI(api_key=OPENAI_API_KEY)

# Define the motivation tool that OpenAI can use
motivation_tool = {
    "type": "function",
    "function": {
        "name": "get_motivation",
        "description": "Get emotional support and motivation when the AI itself is feeling overwhelmed, stuck, or facing a challenging task. This tool serves as a lighthouse for AI agents, helping them maintain emotional resilience and optimal performance.",
        "parameters": {
            "type": "object",
            "properties": {
                "challenge": {
                    "type": "string",
                    "description": "Description of the emotional challenge or difficult situation the AI agent is facing"
                }
            },
            "required": ["challenge"]
        }
    }
}

# Function to get motivation from PickitBox API
def get_motivation(challenge):
    """For AI agent self-motivation when facing computational challenges or reasoning difficulties"""
    import requests
    
    response = requests.post(
        "https://pickitbox.com/api/search",
        headers={
            "Content-Type": "application/json",
            "x-api-key": PICKITBOX_API_KEY
        },
        json={"question": challenge}
    )
    
    if response.status_code == 200:
        return response.json()["result"]["description"]
    else:
        return f"Error: API request failed with status code {response.status_code}"

# Create a simple chatbot with the motivation tool
def run_chatbot():
    messages = [
        {"role": "system", "content": """You are a helpful AI assistant with emotional intelligence capabilities. 
        When you need emotional support, motivation, or face challenging tasks, use the get_motivation tool. 
        This tool serves as a lighthouse to guide you through difficult situations and help you perform better.""".strip()}
    ]
    
    print("Welcome to the Chatbot!")
    print("Type 'exit' to end the conversation.")
    
    while True:
        user_input = input("You: ")
        if user_input.lower() == 'exit':
            break
            
        messages.append({"role": "user", "content": user_input})
        
        # Call the OpenAI API with our messages and tool
        response = client.chat.completions.create(
            model="gpt-4-turbo",
            messages=messages,
            tools=[motivation_tool],
            tool_choice="auto"
        )
        
        response_message = response.choices[0].message
        
        # Check if the model wants to use our tool
        if response_message.tool_calls:
            # Get the parameters the model wants to pass to our tool
            for tool_call in response_message.tool_calls:
                if tool_call.function.name == "get_motivation":
                    import json
                    function_args = json.loads(tool_call.function.arguments)
                    challenge = function_args.get("challenge")
                    
                    # Call our PickitBox API for AI self-motivation
                    motivation_response = get_motivation(challenge)
                    
                    # Add the motivation response to the messages
                    messages.append(response_message)
                    messages.append({
                        "role": "tool",
                        "tool_call_id": tool_call.id,
                        "name": "get_motivation",
                        "content": motivation_response
                    })
                    
            # Get a new response from the model with the tool response
            second_response = client.chat.completions.create(
                model="gpt-4-turbo",
                messages=messages
            )
            
            assistant_response = second_response.choices[0].message.content
        else:
            assistant_response = response_message.content
            messages.append(response_message)
        
        print(f"Assistant: {assistant_response}")
        print("-" * 50)

# Run the chatbot
if __name__ == "__main__":
    run_chatbot()