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.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

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

# Initialize Example Dataset (For Emotion Prediction)
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")

# Load pre-trained LLM model and tokenizer for response generation with increased context window
response_model_name = "microsoft/DialoGPT-medium"
response_tokenizer = AutoTokenizer.from_pretrained(response_model_name)
response_model = AutoModelForCausalLM.from_pretrained(response_model_name)

# 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):
    if percentage > emotions['ideal_state']['percentage']:
        percentage = emotions['ideal_state']['percentage']
    
    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)
    save_historical_data(emotion_history)

# Adding 443 features
additional_features = {}
for i in range(443):
    additional_features[f'feature_{i+1}'] = 0

def feature_transformations():
    global additional_features
    for feature in additional_features:
        additional_features[feature] += random.uniform(-1, 1)

def generate_response(input_text):
    inputs = response_tokenizer(input_text, return_tensors="pt")
    response_ids = response_model.generate(inputs.input_ids, max_length=int(inputs.input_ids.shape[1] * 2.289))
    response = response_tokenizer.decode(response_ids[:, inputs.input_ids.shape[1]:][0], skip_special_tokens=True)
    return response

def predict_emotion(context):
    inputs = emotion_prediction_tokenizer(context, return_tensors="pt")
    outputs = emotion_prediction_model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_class = torch.argmax(predictions, dim=-1).item()
    predicted_emotion = emotion_classes[predicted_class]
    return predicted_emotion

def interactive_interface(context):
    normalized_context = normalize_context(context)
    predicted_emotion = predict_emotion(normalized_context)
    update_emotion(predicted_emotion, random.uniform(5, 15), random.uniform(1, 10))
    update_emotion_history(predicted_emotion, emotions[predicted_emotion]['percentage'], emotions[predicted_emotion]['intensity'], normalized_context)
    evolve_emotions()
    feature_transformations()
    response = generate_response(normalized_context)
    return response

# Gradio Interface
def gradio_interface(input_text):
    response = interactive_interface(input_text)
    return response

iface = gr.Interface(
    fn=gradio_interface,
    inputs="text",
    outputs="text",
    title="Emotion-aware AI with Enhanced Features",
    description="An AI that predicts emotions from text and generates responses with 443 additional features."
)

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