chatbot_simulator.py

from openai import OpenAI
import json_repair
from transformers import AutoTokenizer


class ChatbotSimulation:
    def __init__(self, site_map, page_details, user_state, task,
                 app_name, log_location, openai_api_key, agent='human',
                 max_steps=50, max_tokens=8192, buffer_tokens=500):
        self.sitemap = site_map
        self.page_details = page_details
        self.user_state = user_state
        self.user_state['current_page'] = 'Home'  # Initialize current page
        self.user_state['last_page'] = 'Home'
        self.user_state['task_completed'] = 'False'
        self.task = task
        self.app_name = app_name
        self.log_location = log_location
        self.agent = agent.lower()
        if self.agent not in ['human', 'llm']:
            raise ValueError("Invalid agent type. Expected 'Human' or 'llm'.")
        self.max_steps = max_steps
        self.max_tokens = max_tokens
        self.buffer_tokens = buffer_tokens
        self.conversation = []  # Stores recent conversation snippets
        self.prompt_count = 0
        self.client = OpenAI(api_key=openai_api_key)
        self.actions = []
        self.tokenizer = AutoTokenizer.from_pretrained("gpt2")

    def _get_page_uid(self, page_name):
        """Retrieve the UID of the given page from the sitemap."""
        return self.sitemap['pages'].get(page_name, {}).get('uid')

    def _get_page_details(self, page_name):
        """Retrieve the page details using its UID."""
        uid = self._get_page_uid(page_name)
        return self.page_details.get(uid, {})

    def _generate_system_prompt(self):
        """Create a dynamic system prompt based on the current state."""
        current_page = self.user_state['current_page']
        last_page = self.user_state['last_page']
        page_info = self._get_page_details(current_page)

        return f"""
You are a text-based simulator of {self.app_name} app. 
You are interacting with a user. User's task is: {self.task}.
User's last page was {last_page} and the user have taken actions: {self.actions}.
After action, user is currently on the {current_page} page. 
Current user state: {self.user_state}.

Page Information:
{page_info}

- **Features**: Represent available options the user can select on this page.
- **User Data**: Represents user-specific data accessible on this page.

Provide instructions or request input from the user. If the user provides an invalid action, respond with:
"Invalid action. Please select a valid option."

### Instruction Format:
<if actions is non-empty: You have successfully done actions[-1]>> You are at the {current_page} page. You have the following options:
1. Feature 1
2. Feature 2
3. Feature 3
4. Feature 4

Please enter your choice as 'Number. Description'. If you have a query, enter as 'Number. Description: query'

Rules:
- Be sure to display all options that is available in features. 
- Be robotic and emotionless. Avoid offering any advice to the user.
- **If the user requests information you do not possess** (such as a list of restaurants, menus, or similar details), you are permitted to create plausible and relevant information to fulfill the request. Present this fabricated information convincingly as if it were real data.
        """

    def _get_openai_response(self, prompt):
        """Fetch response from OpenAI API."""
        self._trim_conversation()
        response = self.client.chat.completions.create(
            model="gpt-4",
            messages=prompt,
            max_tokens=self.buffer_tokens,  # Adjusted max_tokens if needed
            temperature=0.7,
        )
        return response.choices[0].message.content

    def _calculate_token_count(self, conversation):
        """Accurately calculate the token count in the conversation using a tokenizer."""
        total_tokens = 0
        for entry in conversation:
            # Tokenize each entry content and count tokens
            tokens = self.tokenizer.encode(entry['content'], truncation=False, add_special_tokens=False)
            total_tokens += len(tokens)
        return total_tokens

    def _trim_conversation(self):
        """Trim the conversation to keep it within the token limit."""
        while self._calculate_token_count(self.conversation) >= (self.max_tokens - self.buffer_tokens * 2):
            self.conversation.pop(0)

    def one_conversation_round(self, user_input):
        """Conduct one round of conversation between the user and the assistant."""
        # User provides input
        self.actions.append(user_input + f'on {self.user_state["current_page"]} page')
        self.conversation.append({"role": "user", "content": user_input})

        # Update user state using GPT's response
        update_prompt = f"""
If user takes action '{user_input}' on {self.user_state['current_page']} page, which page will they move to?
Recall user's task: {self.task}
Update the user_state dictionary based on user's last action:
Current user_state: {self.user_state}
Sitemap: {self.sitemap}

Instructions:
1. If the 'current_page' has changed, update it to a page from the sitemap.
2. If the task is finished, update 'task_completed' to True. Otherwise, leave it as False.
3. If no updates are needed, return the user state exactly as provided, without modification.

Important:
- Ensure 'current_page' and 'task_completed' are keys in the returned dictionary.
- Return only the dictionary without additional output or wrapping.

Example Output Format:
{{
    'current_page': 'Home',
    'last_page': 'Home',
    'task_completed': 'False',
}}
                """

        self.conversation.append({"role": "assistant", "content": update_prompt})
        updated_state = self._get_openai_response(self.conversation)

        # Parse and update the user state
        updated_state = json_repair.loads(updated_state)

        while not isinstance(updated_state, dict):
            transform_prompt = f""""
            Transform {updated_state} to a properly formate JSON file.
            Example Output Format:
            {{
                'current_page': 'Home',
                'last_page': 'Home',
                'task_completed': 'False',
            }}
            """
            updated_state = self._get_openai_response([{"role": "system", "content": transform_prompt}])
            updated_state = json_repair.loads(updated_state)

        try:
            if updated_state['task_completed'].lower() == 'true':
                print(f"Task completed! You took {self.prompt_count} steps.")
        except:
            updated_state['task_completed'] = 'False'

        self.user_state = updated_state

        #self.conversation.clear()
        system_prompt = self._generate_system_prompt()

        # GPT generates the page instructions
        self.conversation.append({"role": "system", "content": system_prompt})
        gpt_instruction = self._get_openai_response(self.conversation)
        self.conversation.append({"role": "assistant", "content": gpt_instruction})
        return gpt_instruction

    def _generate_agent_input(self):
        """
        Simulate the agent generating input based on the conversation state.
        """
        agent_prompt = f"""
Imagine you are an agent navigate through the Uber environment.
Your overarching task is: {self.task}. You may have done some part of the task, or none at all.
You will have access to all of your previous actions in the environment, as well as the last message from the assistant giving the current state of the environment.
The last message from the assistant was: {self.conversation[-1]['content']}
Respond first with a brief "Plan" which suggests what steps you are going to take to accomplish the task, and what your immediate. 
Then generate an "Action" which is the immediate next step you can take.
        """

        messages = [{"role": "system", "content": agent_prompt}]
        for action in self.actions:
            messages.append({"role": "user", "content": action})
        messages.append({"role": "assistant", "content": self.conversation[-1]['content']})

        agent_response = self.client.chat.completions.create(
            model="gpt-4",
            messages=messages,
            temperature=0.7,
        )
        print(f"LLM-Agent: {agent_response.choices[0].message.content}")
        return agent_response.choices[0].message.content

    def start_conversation(self):
        greeting = f'\n Welcome to {self.app_name} simulator! Your task is: {self.task}. \n'
        system_prompt = self._generate_system_prompt()
        # GPT generates the page instructions
        self.conversation.append({"role": "system", "content": system_prompt})
        gpt_instruction = self._get_openai_response(self.conversation)
        self.conversation.append({"role": "assistant", "content": gpt_instruction})
        return greeting + gpt_instruction