aiwifu.md

Your outline is a solid starting point. Here’s a more detailed and structured approach to create an AI chatbot with personality, memory, and contextual awareness:

Step-by-Step Plan

1. Define the Project Requirements and Goals
2. Create a Diverse and Comprehensive Dataset
3. Build and Fine-Tune Models
4. Implement Memory and Context Handling
5. Develop Input Classification and Response Routing
6. Integrate Additional Features (Speech-to-Text, Text-to-Speech)
7. Test and Iterate

Detailed Steps

1. Define the Project Requirements and Goals

Goal: Clearly outline what the chatbot should achieve and its personality traits.

• Define the chatbot's purpose (e.g., customer support, personal assistant).
• Identify key functionalities (e.g., handling FAQs, providing weather updates, engaging in small talk).
• Determine the chatbot’s personality (e.g., friendly, professional, humorous).

2. Create a Diverse and Comprehensive Dataset

Goal: Prepare a dataset that reflects the chatbot's personality and expected interactions.

• Collect or create conversation data that covers different scenarios and emotions.
• Include multiple variations for common interactions to ensure diverse responses.

Example JSON Dataset:

{
  "input": "Hey there, darling! What do you think you're doing, talking to other girls?",
  "output": {
    "responses": [
      {
        "response": "Oh, my dear, you know I can't stand seeing you with anyone else. You're mine and mine alone!",
        "emotion": "Possessive"
      },
      {
        "response": "Hmph, why would you even bother with those other girls when you have me? I'm the only one who truly understands you!",
        "emotion": "Jealous"
      },
      {
        "response": "Sweetheart, you should know better than to stray away from me. After all, we're meant to be together forever!",
        "emotion": "Affectionate"
      },
      {
        "response": "Darling, it's not like I'm jealous or anything... But promise me you'll only have eyes for me, okay?",
        "emotion": "Insecure"
      },
      {
        "response": "What's the matter, my love? Are you trying to make me jealous? Because it's working!",
        "emotion": "Playful"
      }
    ],
    "personality": {
      "cute": true,
      "possessive": true,
      "anime": true,
      "chuunibyou": true,
      "waifu": true
    }
  },
  "text": "These responses embody the persona of a cute, possessive anime Chuunibyou waifu, expressing affection, jealousy, and playfulness towards their beloved.",
  "emotion": ["Possessive", "Jealous", "Affectionate", "Insecure", "Playful"]
}

3. Build and Fine-Tune Models

Goal: Create and fine-tune models for different types of interactions.

• Use pre-trained models (e.g., GPT-3, GPT-4) and fine-tune them on your dataset.
• Create specialized models for different types of responses (e.g., greetings, questions, commands).

Fine-Tuning Example:

from transformers import GPT2Tokenizer, GPT2LMHeadModel, Trainer, TrainingArguments
import torch
import json

# Load pre-trained model and tokenizer
model_name = "gpt-2"
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)

# Prepare dataset
with open("path_to_your_json_dataset.json") as f:
    data = json.load(f)

# Custom dataset class
class ChatDataset(torch.utils.data.Dataset):
    def __init__(self, conversations, tokenizer):
        self.inputs = []
        self.labels = []
        for conversation in conversations:
            for response in conversation["responses"]:
                self.inputs.append(tokenizer(conversation["input"], return_tensors="pt", padding=True, truncation=True))
                self.labels.append(tokenizer(response, return_tensors="pt", padding=True, truncation=True))
    
    def __len__(self):
        return len(self.inputs)
    
    def __getitem__(self, idx):
        return {
            "input_ids": self.inputs[idx]["input_ids"][0],
            "attention_mask": self.inputs[idx]["attention_mask"][0],
            "labels": self.labels[idx]["input_ids"][0]
        }

# Create dataset and data loader
dataset = ChatDataset(data["conversations"], tokenizer)
data_loader = torch.utils.data.DataLoader(dataset, batch_size=4, shuffle=True)

# Fine-tuning arguments
training_args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=3,
    per_device_train_batch_size=4,
    save_steps=10_000,
    save_total_limit=2,
)

# Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=dataset,
)

# Train the model
trainer.train()

4. Implement Memory and Context Handling

Goal: Enable the chatbot to remember previous interactions.

• Use a data structure (e.g., deque) to store previous interactions.
• Incorporate this context into the input for generating new responses.

Example Memory Implementation:

import time
from collections import deque
from transformers import GPT2Tokenizer, GPT2LMHeadModel

class InteractionMemory:
    def __init__(self, max_size=10, expiry_seconds=300):
        self.memory = deque(maxlen=max_size)
        self.expiry_seconds = expiry_seconds
    
    def add_interaction(self, user_input, response, emotion):
        timestamp = time.time()
        self.memory.append({
            "user_input": user_input,
            "response": response,
            "emotion": emotion,
            "timestamp": timestamp
        })

    def get_context(self):
        current_time = time.time()
        self.memory = deque(
            [interaction for interaction in self.memory if current_time - interaction["timestamp"] < self.expiry_seconds],
            maxlen=self.memory.maxlen
        )
        return list(self.memory)

5. Develop Input Classification and Response Routing

Goal: Classify user inputs and route them to the appropriate specialized model.

• Use a text classification model to determine the type of input.
• Route the input to the corresponding specialized model for response generation.

Example Classification and Routing:

from transformers import pipeline

# Load a text classification pipeline
classifier = pipeline("text-classification", model="your_classification_model")

def classify_input(user_input):
    categories = classifier(user_input)
    return categories[0]['label']

# Load pre-trained models for different tasks
greeting_model = GPT2LMHeadModel.from_pretrained("greeting_model_path")
question_model = GPT2LMHeadModel.from_pretrained("question_model_path")
command_model = GPT2LMHeadModel.from_pretrained("command_model_path")
small_talk_model = GPT2LMHeadModel.from_pretrained("small_talk_model_path")

tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

def generate_response(model, user_input):
    inputs = tokenizer(user_input, return_tensors="pt")
    outputs = model.generate(inputs["input_ids"], max_length=50, num_return_sequences=1)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

def handle_user_input(user_input):
    category = classify_input(user_input)
    
    if category == "greeting":
        response = generate_response(greeting_model, user_input)
    elif category == "question":
        response = generate_response(question_model, user_input)
    elif category == "command":
        response = execute_command(user_input)  # Implement command execution logic
    elif category == "small_talk":
        response = generate_response(small_talk_model, user_input)
    else:
        response = "I'm not sure how to respond to that."

    return response

# Example usage
user_input = "Hello, how are you?"
response = handle_user_input(user_input)
print(response)

6. Integrate Additional Features (Speech-to-Text, Text-to-Speech)

Goal: Add speech capabilities to the chatbot.

• Use libraries like Google Speech Recognition for speech-to-text.
• Use libraries like pyttsx3 for text-to-speech.

Example Integration:

import speech_recognition as sr
import pyttsx3

# Initialize speech recognizer and text-to-speech engine
recognizer = sr.Recognizer()
tts_engine = pyttsx3.init()

def listen():
    with sr.Microphone() as source:
        print("Listening...")
        audio = recognizer.listen(source)
        try:
            user_input = recognizer.recognize_google(audio)
            print(f"User said: {user_input}")
            return user_input
        except sr.UnknownValueError:
            return "Sorry, I did not understand that."

def speak(text):
    tts_engine.say(text)
    tts_engine.runAndWait()

# Example usage
user_input = listen()
response = handle_user_input(user_input)
speak(response)

7. Test and Iterate

Goal: Ensure the chatbot functions correctly and meets user expectations.

• Test the chatbot with real users to gather feedback.
• Iterate on the design, dataset, and model fine-tuning based on user feedback and performance metrics.

Summary

1. Define the Project Requirements and Goals: Understand the chatbot’s purpose and personality.
2. Create a Diverse and Comprehensive Dataset: Prepare a dataset that reflects expected interactions.
3. Build and Fine-Tune Models: Create and fine-tune models for different types of responses.
4. Implement Memory and Context Handling: Enable the chatbot to remember previous interactions.
5. Develop Input Classification and Response Routing: Classify user inputs and route them to the appropriate model.
6. Integrate Additional Features: Add speech-to-text and text-to-speech capabilities.
7. Test and Iterate: Continuously improve the chatbot based on user feedback.

By following this structured approach, you can create a chatbot with a dynamic personality, memory, and contextual awareness that adapts to different situations and user emotions.