app.py

import warnings
import numpy as np
import pandas as pd
import os
import json
import random
import gradio as gr
import torch
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from transformers import AutoModelForSequenceClassification, AutoTokenizer, T5ForConditionalGeneration, T5Tokenizer, pipeline
from deap import base, creator, tools, algorithms
import gc

warnings.filterwarnings('ignore', category=FutureWarning, module='huggingface_hub.file_download')

# Initialize Example Emotions Dataset
data = {
    'context': [
        'I am happy', 'I am sad', 'I am angry', 'I am excited', 'I am calm',
        'I am feeling joyful', 'I am grieving', 'I am feeling peaceful', 'I am frustrated',
        'I am determined', 'I feel resentment', 'I am feeling glorious', 'I am motivated',
        'I am surprised', 'I am fearful', 'I am trusting', 'I feel disgust', 'I am optimistic',
        'I am pessimistic', 'I feel bored', 'I am envious'
    ],
    'emotion': [
        'joy', 'sadness', 'anger', 'joy', 'calmness', 'joy', 'grief', 'calmness', 'anger',
        'determination', 'resentment', 'glory', 'motivation', 'surprise', 'fear', 'trust',
        'disgust', 'optimism', 'pessimism', 'boredom', 'envy'
    ]
}
df = pd.DataFrame(data)

# Encoding the contexts using One-Hot Encoding (memory-efficient)
encoder = OneHotEncoder(handle_unknown='ignore', sparse=True)
contexts_encoded = encoder.fit_transform(df[['context']])

# Encoding emotions
emotions_target = pd.Categorical(df['emotion']).codes
emotion_classes = pd.Categorical(df['emotion']).categories

# Load pre-trained BERT model for emotion prediction
emotion_prediction_model = AutoModelForSequenceClassification.from_pretrained("bhadresh-savani/distilbert-base-uncased-emotion")
emotion_prediction_tokenizer = AutoTokenizer.from_pretrained("bhadresh-savani/distilbert-base-uncased-emotion")

# Lazy loading for the fine-tuned language model (FLAN-T5)
_finetuned_lm_tokenizer = None
_finetuned_lm_model = None

def get_finetuned_lm_model():
    global _finetuned_lm_tokenizer, _finetuned_lm_model
    if _finetuned_lm_tokenizer is None or _finetuned_lm_model is None:
        _finetuned_lm_tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base")
        _finetuned_lm_model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base", device_map="auto", low_cpu_mem_usage=True)
    return _finetuned_lm_tokenizer, _finetuned_lm_model

# Enhanced Emotional States
emotions = {
    'joy': {'percentage': 10, 'motivation': 'positive', 'intensity': 0},
    'pleasure': {'percentage': 10, 'motivation': 'selfish', 'intensity': 0},
    'sadness': {'percentage': 10, 'motivation': 'negative', 'intensity': 0},
    'grief': {'percentage': 10, 'motivation': 'negative', 'intensity': 0},
    'anger': {'percentage': 10, 'motivation': 'traumatic or strong', 'intensity': 0},
    'calmness': {'percentage': 10, 'motivation': 'neutral', 'intensity': 0},
    'determination': {'percentage': 10, 'motivation': 'positive', 'intensity': 0},
    'resentment': {'percentage': 10, 'motivation': 'negative', 'intensity': 0},
    'glory': {'percentage': 10, 'motivation': 'positive', 'intensity': 0},
    'motivation': {'percentage': 10, 'motivation': 'positive', 'intensity': 0},
    'ideal_state': {'percentage': 100, 'motivation': 'balanced', 'intensity': 0},
    'fear': {'percentage': 10, 'motivation': 'defensive', 'intensity': 0},
    'surprise': {'percentage': 10, 'motivation': 'unexpected', 'intensity': 0},
    'anticipation': {'percentage': 10, 'motivation': 'predictive', 'intensity': 0},
    'trust': {'percentage': 10, 'motivation': 'reliable', 'intensity': 0},
    'disgust': {'percentage': 10, 'motivation': 'repulsive', 'intensity': 0},
    'optimism': {'percentage': 10, 'motivation': 'hopeful', 'intensity': 0},
    'pessimism': {'percentage': 10, 'motivation': 'doubtful', 'intensity': 0},
    'boredom': {'percentage': 10, 'motivation': 'indifferent', 'intensity': 0},
    'envy': {'percentage': 10, 'motivation': 'jealous', 'intensity': 0},
    'neutral': {'percentage': 10, 'motivation': 'balanced', 'intensity': 0}
}

total_percentage = 200
emotion_history_file = 'emotion_history.json'

def load_historical_data(file_path=emotion_history_file):
    if os.path.exists(file_path):
        with open(file_path, 'r') as file:
            return json.load(file)
    return []

def save_historical_data(historical_data, file_path=emotion_history_file):
    with open(file_path, 'w') as file:
        json.dump(historical_data, file)

emotion_history = load_historical_data()

def update_emotion(emotion, percentage, intensity):
    emotions['ideal_state']['percentage'] -= percentage
    emotions[emotion]['percentage'] += percentage
    emotions[emotion]['intensity'] = intensity

    total_current = sum(e['percentage'] for e in emotions.values())
    adjustment = total_percentage - total_current
    emotions['ideal_state']['percentage'] += adjustment

def normalize_context(context):
    return context.lower().strip()

def evaluate(individual):
    emotion_values = individual[:len(emotions) - 1]
    intensities = individual[-21:-1]
    ideal_state = individual[-1]
    
    ideal_diff = abs(100 - ideal_state)
    sum_non_ideal = sum(emotion_values)
    intensity_range = max(intensities) - min(intensities)
    
    return ideal_diff, sum_non_ideal, intensity_range

def evolve_emotions():
    creator.create("FitnessMulti", base.Fitness, weights=(-1.0, -0.5, -0.2))
    creator.create("Individual", list, fitness=creator.FitnessMulti)

    toolbox = base.Toolbox()
    toolbox.register("attr_float", random.uniform, 0, 20)
    toolbox.register("attr_intensity", random.uniform, 0, 10)
    toolbox.register("individual", tools.initCycle, creator.Individual,
                     (toolbox.attr_float,) * (len(emotions) - 1) +
                     (toolbox.attr_intensity,) * len(emotions) +
                     (lambda: 100,), n(toolbox.attr_intensity,) * len(emotions) +
                     (lambda: 100,), n=1)
    toolbox.register("population", tools.initRepeat, list, toolbox.individual)
    toolbox.register("mate", tools.cxTwoPoint)
    toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.2)
    toolbox.register("select", tools.selNSGA2)
    toolbox.register("evaluate", evaluate)

    population = toolbox.population(n=100)
    algorithms.eaMuPlusLambda(population, toolbox, mu=50, lambda_=100, cxpb=0.7, mutpb=0.2, ngen=100,
                               stats=None, halloffame=None, verbose=False)

    best_individual = tools.selBest(population, k=1)[0]
    emotion_values = best_individual[:len(emotions) - 1]
    intensities = best_individual[-21:-1]
    ideal_state = best_individual[-1]

    for i, emotion in enumerate(emotions):
        emotions[emotion]['percentage'] = emotion_values[i]
        emotions[emotion]['intensity'] = intensities[i]

    emotions['ideal_state']['percentage'] = ideal_state

def predict_emotion(context):
    emotion_prediction_pipeline = pipeline('text-classification', model=emotion_prediction_model, tokenizer=emotion_prediction_tokenizer, top_k=None)
    predictions = emotion_prediction_pipeline(context)
    emotion_scores = {prediction['label']: prediction['score'] for prediction in predictions[0]}
    predicted_emotion = max(emotion_scores, key=emotion_scores.get)
    
    # Map the predicted emotion to our emotion categories
    emotion_mapping = {
        'sadness': 'sadness',
        'joy': 'joy',
        'love': 'pleasure',
        'anger': 'anger',
        'fear': 'fear',
        'surprise': 'surprise'
    }
    
    return emotion_mapping.get(predicted_emotion, 'neutral')

def generate_text(prompt, chat_history, emotion=None, max_length=100):
    finetuned_lm_tokenizer, finetuned_lm_model = get_finetuned_lm_model()
    
    # Prepare the input by concatenating the chat history and the new prompt
    full_prompt = "You are Adam, an AI assistant. Respond to the following conversation in a natural and engaging way, considering the emotion: " + emotion + "\n\n"
    for turn in chat_history[-5:]:  # Consider last 5 turns for context
        full_prompt += f"Human: {turn[0]}\nAdam: {turn[1]}\n"
    full_prompt += f"Human: {prompt}\nAdam:"
    
    input_ids = finetuned_lm_tokenizer(full_prompt, return_tensors="pt", max_length=512, truncation=True).input_ids
    
    if torch.cuda.is_available():
        input_ids = input_ids.cuda()
        finetuned_lm_model = finetuned_lm_model.cuda()

    # Generate the response
    outputs = finetuned_lm_model.generate(
        input_ids,
        max_length=max_length + input_ids.shape[1],
        num_return_sequences=1,
        no_repeat_ngram_size=2,
        do_sample=True,
        temperature=0.7,
        top_k=50,
        top_p=0.95
    )

    generated_text = finetuned_lm_tokenizer.decode(outputs[0], skip_special_tokens=True)
    return generated_text.strip()

def update_emotion_history(emotion, intensity):
    global emotion_history
    emotion_history.append({
        'emotion': emotion,
        'intensity': intensity,
        'timestamp': pd.Timestamp.now().isoformat()
    })
    save_historical_data(emotion_history)

def get_dominant_emotion():
    return max(emotions, key=lambda x: emotions[x]['percentage'] if x != 'ideal_state' else 0)

def get_emotion_summary():
    summary = []
    for emotion, data in emotions.items():
        if emotion != 'ideal_state':
            summary.append(f"{emotion.capitalize()}: {data['percentage']:.1f}% (Intensity: {data['intensity']:.1f})")
    return "\n".join(summary)

def reset_emotions():
    global emotions
    for emotion in emotions:
        if emotion != 'ideal_state':
            emotions[emotion]['percentage'] = 10
            emotions[emotion]['intensity'] = 0
    emotions['ideal_state']['percentage'] = 100
    return get_emotion_summary()

def respond_to_user(user_input, chat_history):
    # Predict the emotion from the user input
    predicted_emotion = predict_emotion(user_input)
    
    # Ensure the predicted emotion exists in our emotions dictionary
    if predicted_emotion not in emotions:
        predicted_emotion = 'neutral'
    
    # Update the emotional state
    update_emotion(predicted_emotion, 5, random.uniform(0, 10))
    
    # Get the current dominant emotion
    dominant_emotion = get_dominant_emotion()
    
    # Generate a response considering the dominant emotion
    response = generate_text(user_input, chat_history, dominant_emotion)
    
    # Update emotion history
    update_emotion_history(predicted_emotion, emotions[predicted_emotion]['intensity'])
    
    # Update chat history
    chat_history.append((user_input, response))
    
    # Evolve emotions periodically (e.g., every 10 interactions)
    if len(chat_history) % 10 == 0:
        evolve_emotions()
    
    return response, chat_history, get_emotion_summary()

# Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Adam: Emotion-Aware AI Chatbot")
    gr.Markdown("Chat with Adam, an AI that understands and responds to emotions.")
    
    chatbot = gr.Chatbot()
    msg = gr.Textbox(label="Type your message here...")
    clear = gr.Button("Clear")
    
    emotion_state = gr.Textbox(label="Current Emotional State", lines=10)
    reset_button = gr.Button("Reset Emotions")
    
    def user(user_message, history):
        response, updated_history, emotion_summary = respond_to_user(user_message, history)
        return "", updated_history, emotion_summary
    
    msg.submit(user, [msg, chatbot], [msg, chatbot, emotion_state])
    clear.click(lambda: None, None, chatbot, queue=False)
    reset_button.click(reset_emotions, None, emotion_state, queue=False)

if __name__ == "__main__":
    demo.launch()