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, AutoModelForCausalLM, 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 (DialoGPT-medium)
_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:
        model_name = "microsoft/DialoGPT-medium"
        _finetuned_lm_tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side='left')
        _finetuned_lm_model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", low_cpu_mem_usage=True)
        _finetuned_lm_tokenizer.pad_token = _finetuned_lm_tokenizer.eos_token
    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},
    'wit': {'percentage': 15, 'motivation': 'clever', 'intensity': 0},
    'curiosity': {'percentage': 20, 'motivation': 'inquisitive', '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

    # Introduce some randomness in emotional evolution
    for e in emotions:
        if e != emotion and e != 'ideal_state':
            change = random.uniform(-2, 2)
            emotions[e]['percentage'] = max(0, emotions[e]['percentage'] + change)

    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[len(emotions) - 1:-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) - 1) +
                     (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[len(emotions) - 1:-1]
    ideal_state = best_individual[-1]

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

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

def update_emotion_history(emotion, percentage, intensity, context):
    entry = {
        'emotion': emotion,
        'percentage': percentage,
        'intensity': intensity,
        'context': context,
        'timestamp': pd.Timestamp.now().isoformat()
    }
    emotion_history.append(entry)
    if len(emotion_history) > 100:
        emotion_history.pop(0)
    save_historical_data(emotion_history)

def predict_emotion(context):
    tokens = emotion_prediction_tokenizer(context, return_tensors='pt', padding=True, truncation=True)
    outputs = emotion_prediction_model(**tokens)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=1)
    predicted_emotion = emotion_classes[predicted_class]

    # Get percentage and intensity based on context
    percentage = np.random.uniform(5, 20)
    intensity = np.random.uniform(1, 10)

    update_emotion(predicted_emotion, percentage, intensity)
    update_emotion_history(predicted_emotion, percentage, intensity, context)

    return predicted_emotion

def generate_response(context):
    tokenizer, model = get_finetuned_lm_model()
    inputs = tokenizer.encode(context, return_tensors='pt')
    outputs = model.generate(inputs, max_length=500, num_return_sequences=1, pad_token_id=tokenizer.eos_token)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

def handle_conversation(user_input):
    user_input = normalize_context(user_input)
    predicted_emotion = predict_emotion(user_input)
    bot_response = generate_response(user_input)
    return f"Emotion: {predicted_emotion}, Response: {bot_response}"

def update_ui(user_input):
    response = handle_conversation(user_input)
    return response

with gr.Blocks() as demo:
    user_input = gr.Textbox(label="User Input")
    response = gr.Textbox(label="Bot Response")
    submit = gr.Button("Submit")
    submit.click(update_ui, user_input, response)

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