data_utils.py

from abc import ABC
import pandas as pd
from enum import Enum
from uuid import uuid4
from datetime import datetime
from dataclasses import dataclass, field
from langchain_core.prompts import PromptTemplate
from datasets import load_dataset, DatasetDict, Dataset, concatenate_datasets

from typing import List, Dict, Any, Literal, Optional

username = 'mimipynb'

class HFConfig(Enum):
    chat = username + '/naomi-dialogue'
    users = username + '/naomi-users'
    results = username + '/naomi-eval'
    hub = username + '/agentNet'
    pepe = username + '/agentNetHuman'

def load_agent_from_hf(agent_name):
    """ Loads agent from HF """

    botnet = load_dataset(HFConfig.hub.value, token=True, split='train').to_pandas()
    chatbot = dict(zip(botnet.columns, *botnet[botnet['name'] == agent_name].values))
    chatbot.pop('agent_type')

    return Agent(**chatbot)

def load_main_user():
    """ Loads main user from HF. To be removed / changed. """
    pepes = load_dataset(HFConfig.pepe.value, token=True, split='train').to_pandas()
    pepe = dict(zip(pepes.columns, *pepes[pepes['user_type'] == 'main'].values))
    pepe.pop('user_type')
    pepe.pop('relation_type')
    pepe.pop('repo_id')
    pepe.pop('input_file_path')
    pepe.pop('output_file_path')

    return User(**pepe)

def uploader(repo_id, new_data):
    """ Appends new streaming sessions to HF space. """

    original = load_dataset(repo_id=repo_id, token=True)
    if isinstance(original, DatasetDict):
        original = original['train']

    concat = concatenate_datasets([original, new_data])

    if len(concat) != len(original) + len(new_data):
        raise ValueError(f"Expected concatenated data to be to be the sum of {len(original)} and {len(new_data)} but received {len(concat)} ")

    concat.push_to_hub(
        repo_id=repo_id,
        private=True
    )
    print(f"Finished pushing to {repo_id}")

def end_session(naomi):
    """ Data Handlers to run end of chat session. """

    chat = naomi.chat.messages
    user = naomi.user
    results = naomi.results
    uploader(HFConfig.chat.value, Dataset.from_pandas(chat))
    uploader(HFConfig.users.value, Dataset.from_dict(user))
    uploader(HFConfig.results.value, Dataset.from_pandas(results))

chat_messages = [
    {'role': 'user', 'content': 'Hello!'},
    {'role': 'assistant', 'content': 'Hi there! How can I assist you today?'},
    {'role': 'user', 'content': 'I have a question about my order.'},
    {'role': 'assistant', 'content': 'Sure! What would you like to know about your order?'},
    {'role': 'user', 'content': 'When will it be delivered?'},
    {'role': 'assistant', 'content': 'Let me check that for you. Can you provide me with your order number?'},
    {'role': 'user', 'content': 'My order number is 123456.'},
    {'role': 'assistant', 'content': 'Thank you! Your order is scheduled to be delivered on March 5th.'},
    {'role': 'user', 'content': 'Great! Thank you for your help.'},
    {'role': 'assistant', 'content': 'You’re welcome! If you have any more questions, feel free to ask.'},
    {'role': 'user', 'content': 'Will do! Have a nice day.'},
    {'role': 'assistant', 'content': 'You too! Take care!'}
]

@dataclass
class ChatMessage:
    role: str
    content: str
    timestamp: str = field(default=datetime.now().isoformat())
    inference: Dict[str, Any] = field(default_factory=dict)

    def preprocess(self):
        # Example preprocessing: strip whitespace and convert to lowercase
        self.content = self.content.strip().lower()

    def collect_features(self):
        """ TODO:
            - connect to classifiers / pipeline
            - connect to agentDial
        """
        self.inference['positive'] = 0.05
        self.inference['negative'] = 0.05
        self.inference['neutral'] = 0.90
        self.inference['intent'] = 'greeting'
        self.inference['mood'] = 'neutral'

    def __post_init__(self):
        """ Workflow of inferencing tools. """
        self.preprocess()
        self.collect_features()

@dataclass
class ChatSession:
    _messages: List[ChatMessage] = field(default_factory=list)
    session_id: str = field(default=uuid4().hex)

    def __iter__(self):
        # Iterates only the role and content for tokenizing.
        for item in self._messages:
            yield {
                'role': item.role,
                'content': item.content
            }

    def __getitem__(self, index):
        """ Only returns the role and content for the requested index."""
        if -len(self._messages) <= index < len(self._messages):
            msg = self._messages[index]
            return {
                'role': msg.role,
                'content': msg.content
            }
        raise IndexError

    @property
    def messages(self):
        """ Returns dataframe. Includes inferenced features. """
        data = pd.DataFrame(self._messages)
        data['session_id'] = self.session_id
        return data

    def add_message(self, role: Literal['user', 'role', 'system'], content: str):
        """ Adds messages to the chat sessions. """
        message = ChatMessage(role=role, content=content)
        self._messages.append(message)

@dataclass
class ProfileBase(ABC):
    def __post_init__(self):
        """ Base checks """
        if hasattr(self, 'name') and self.name:
            self.name = self.name.lower().capitalize()
        if hasattr(self, 'prompt'):
            prompt = PromptTemplate.from_template(self.prompt)
            self.prompt = prompt

@dataclass
class Agent(ProfileBase):
    """Setup Agent Profile or Adds Agent to Bot Family"""

    name: str
    prompt: str
    data: dict

    def system_prompt(self, candidate):
        try:
            main_user = load_main_user()
            prompt = self.prompt.invoke(
                input=dict(
                    user_name=main_user.name,
                    user_likes="\n".join(main_user.likes),
                    user_dislikes="\n".join(main_user.dislikes),
                    candidate_details=candidate.format_profile(),
                    **self.data
                )
            )
            print(f"Parsed prompt: {prompt}. Full input: \n{prompt.text}")

            return [{'role': 'system', 'content': prompt.text}]

        except Exception as e:
            print(e)
            raise

@dataclass
class Contact(ProfileBase):
    """User's Metaclasses -- Social"""

    instagram: Optional[str] = None
    email: Optional[str] = None
    mobile: Optional[str] = None

@dataclass
class Biography:
    """User's Metaclasses -- Biography / FAQs"""

    dob: Optional[str] = None
    location: Optional[str] = None
    mbti_label: Optional[str] = None
    education: Optional[str] = None
    occupation: Optional[str] = None

@dataclass
class User(Biography, Contact):
    """User's Datahandler for account creation. Metaclass: Contact"""

    name: str = field(default_factory=str)
    likes: List[str] = field(default_factory=list)
    dislikes: List[str] = field(default_factory=list)

@dataclass
class Candidate(Contact, Biography):
    """Interviewing Candidate Accessor for Agents roleplaying as Interviewers."""

    name: str = field(default=str)
    id: str = field(default=uuid4().hex)

    def format_profile(self):
        return "".join([f"{key}: {val}\n" for key, val in self.__dict__.items() if val is not None or key not in ('output_file_path', 'input_file_path', 'id')])

def new_user(**kwargs):
    """ Process inputs collected from frontend to backend. Returns Candidate. """

    contact_type = kwargs.get('contact_type', None)
    if contact_type is not None:
        contact = Contact.__match_args__[contact_type] if isinstance(contact_type, int) else contact_type
        kwargs.update({contact: kwargs.get('contact', None)})

    kwargs.pop('contact_type')
    kwargs.pop('contact')
    return Candidate(**kwargs)

if __name__ == "__main__":
    # Example usage for chat session
    """
    chat_session = ChatSession()
    for msg in chat_messages:
        chat_session.add_message(msg['role'], msg['content'])
    print(chat_session.messages)
    """
    # user = load_main_user()
    # print(user)

    test_user = {
        'name': 'mike',
        'contact_type': 1,
        'contact': 'asdf@asdf.com',
        'dob': '29/12/1800',
        'location': 'north korea',
        'intake_submission': True
    }
    candy = new_user(**test_user)
    print(candy)