app.py

import warnings
import numpy as np
import pandas as pd
import os
import json
import random
import gradio as gr
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset, IterableDataset
from sklearn.ensemble import IsolationForest, RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.neural_network import MLPClassifier
from deap import base, creator, tools, algorithms
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, AutoModelForSequenceClassification
import gc
import multiprocessing as mp
from joblib import Parallel, delayed

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

# Memory-efficient Neural Network with PyTorch
class MemoryEfficientNN(nn.Module):
    def __init__(self, input_size, hidden_size, num_classes):
        super(MemoryEfficientNN, self).__init__()
        self.layers = nn.Sequential(
            nn.Linear(input_size, hidden_size),
            nn.ReLU(),
            nn.Dropout(0.2),
            nn.Linear(hidden_size, hidden_size),
            nn.ReLU(),
            nn.Dropout(0.2),
            nn.Linear(hidden_size, num_classes)
        )
        
    def forward(self, x):
        return self.layers(x)

# Memory-efficient dataset
class MemoryEfficientDataset(IterableDataset):
    def __init__(self, X, y, batch_size):
        self.X = X
        self.y = y
        self.batch_size = batch_size

    def __iter__(self):
        for i in range(0, len(self.y), self.batch_size):
            X_batch = self.X[i:i+self.batch_size].toarray()
            y_batch = self.y[i:i+self.batch_size]
            yield torch.FloatTensor(X_batch), torch.LongTensor(y_batch)

# Train Memory-Efficient Neural Network
X_train, X_test, y_train, y_test = train_test_split(contexts_encoded, emotions_target, test_size=0.2, random_state=42)
input_size = X_train.shape[1]
hidden_size = 64
num_classes = len(emotion_classes)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = MemoryEfficientNN(input_size, hidden_size, num_classes).to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

train_dataset = MemoryEfficientDataset(X_train, y_train, batch_size=32)
train_loader = DataLoader(train_dataset, batch_size=None)

num_epochs = 100
for epoch in range(num_epochs):
    for batch_X, batch_y in train_loader:
        batch_X, batch_y = batch_X.to(device), batch_y.to(device)
        outputs = model(batch_X)
        loss = criterion(outputs, batch_y)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
    gc.collect()  # Garbage collection after each epoch

# Ensemble with Random Forest (memory-efficient)
rf_model = RandomForestClassifier(n_estimators=50, random_state=42, n_jobs=-1)
rf_model.fit(X_train, y_train)

# Isolation Forest Anomaly Detection Model (memory-efficient)
isolation_forest = IsolationForest(contamination=0.1, random_state=42, n_jobs=-1, max_samples='auto')
isolation_forest.fit(X_train)  # Fit the model before using it

# 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}
}
total_percentage = 200
default_percentage = total_percentage / len(emotions)
for emotion in emotions:
    emotions[emotion]['percentage'] = default_percentage
    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()

# Memory-efficient genetic algorithm for emotion evolution
def evolve_emotions():
    def evaluate(individual):
        ideal_state = individual[-1]
        other_emotions = individual[:-1]
        intensities = individual[-21:-1]
        return (abs(ideal_state - 100), 
                sum(other_emotions), 
                max(intensities) - min(intensities))

    creator.create("FitnessMulti", base.Fitness, weights=(-1.0, -1.0, -1.0))
    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: random.uniform(80, 120),),
                     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 process_input(text):
    try:
        normalized_text = normalize_context(text)
        encoded_text = encoder.transform([[normalized_text]])
        
        rf_prediction = rf_model.predict(encoded_text)[0]
        isolation_score = isolation_forest.decision_function(encoded_text)[0]
        nn_prediction = model(torch.FloatTensor(encoded_text.toarray()).to(device)).argmax(dim=1).item()
        
        predicted_emotion = emotion_classes[rf_prediction]
        sentiment_score = isolation_score
        generated_text = emotion_classes[nn_prediction]

        historical_data = load_historical_data()
        historical_data.append({
            'context': text,
            'predicted_emotion': predicted_emotion,
            'sentiment_score': sentiment_score,
            'generated_text': generated_text
        })
        save_historical_data(historical_data)

        return predicted_emotion, sentiment_score, generated_text

    except Exception as e:
        error_message = f"An error occurred: {str(e)}"
        print(error_message)  # Logging the error
        return error_message, error_message, error_message

iface = gr.Interface(
    fn=process_input,
    inputs="text",
    outputs=[
        gr.outputs.Textbox(label="Emotional Response"),
        gr.outputs.Textbox(label="Sentiment Response"),
        gr.outputs.Textbox(label="Generated Text")
    ],
    live=True
)

iface.launch(share=True)