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.preprocessing import OneHotEncoder
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoModelForCausalLM, pipeline
from deap import base, creator, tools, algorithms
import nltk
from nltk.sentiment import SentimentIntensityAnalyzer
from nltk.tokenize import word_tokenize
from nltk.tag import pos_tag
from nltk.chunk import ne_chunk
from textblob import TextBlob
import matplotlib.pyplot as plt
import seaborn as sns
import ssl

# NLTK data download
try:
    _create_unverified_https_context = ssl._create_unverified_context
except AttributeError:
    pass
else:
    ssl._create_default_https_context = _create_unverified_https_context

nltk.download('words', quiet=True)
nltk.download('vader_lexicon', quiet=True)
nltk.download('punkt', quiet=True)
nltk.download('averaged_perceptron_tagger', quiet=True)
nltk.download('maxent_ne_chunker', quiet=True)

# Set NLTK data path
nltk.data.path.append('/home/user/nltk_data')

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

# Initialize Example Dataset (For Emotion Prediction)
data = {
    'context': [
        'I am overjoyed', 'I am deeply saddened', 'I am seething with rage', 'I am exhilarated', 'I am tranquil',
        'I am brimming with joy', 'I am grieving profoundly', 'I am at peace', 'I am frustrated beyond measure',
        'I am determined to succeed', 'I feel resentment burning within me', 'I am feeling glorious and triumphant',
        'I am motivated and inspired', 'I am utterly surprised', 'I am gripped by fear', 'I am trusting and open',
        'I feel a sense of disgust', 'I am optimistic and hopeful', 'I am pessimistic and gloomy', 'I feel bored and listless',
        'I am envious and jealous'
    ],
    '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)
try:
    encoder = OneHotEncoder(handle_unknown='ignore', sparse_output=True)
except TypeError:
    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 = None
emotion_prediction_tokenizer = None

# Load pre-trained large language model and tokenizer for response generation
response_model = None
response_tokenizer = None

def load_models():
    global emotion_prediction_model, emotion_prediction_tokenizer, response_model, response_tokenizer
    if emotion_prediction_model is None or response_model is None:
        emotion_prediction_model = AutoModelForSequenceClassification.from_pretrained("bhadresh-savani/distilbert-base-uncased-emotion")
        emotion_prediction_tokenizer = AutoTokenizer.from_pretrained("bhadresh-savani/distilbert-base-uncased-emotion")
        response_model_name = "gpt2-xl"
        response_tokenizer = AutoTokenizer.from_pretrained(response_model_name)
        response_model = AutoModelForCausalLM.from_pretrained(response_model_name)
        response_tokenizer.pad_token = response_tokenizer.eos_token

# Enhanced Emotional States
emotions = {
    'joy': {'percentage': 20, 'motivation': 'positive and uplifting', 'intensity': 8},
    'sadness': {'percentage': 15, 'motivation': 'reflective and introspective', 'intensity': 6},
    'anger': {'percentage': 15, 'motivation': 'passionate and driven', 'intensity': 7},
    'fear': {'percentage': 10, 'motivation': 'cautious and protective', 'intensity': 5},
    'love': {'percentage': 15, 'motivation': 'affectionate and caring', 'intensity': 7},
    'surprise': {'percentage': 10, 'motivation': 'curious and intrigued', 'intensity': 6},
    'neutral': {'percentage': 15, 'motivation': 'balanced and composed', 'intensity': 4},
}

total_percentage = 100
emotion_history_file = 'emotion_history.json'
global conversation_history
conversation_history = []
max_history_length = 1000  # Increase the maximum history length

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[emotion]['percentage'] += percentage
    emotions[emotion]['intensity'] = intensity

    # Normalize percentages
    total = sum(e['percentage'] for e in emotions.values())
    for e in emotions:
        emotions[e]['percentage'] = (emotions[e]['percentage'] / total) * 100

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

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

def evaluate(individual):
    emotion_values = individual[:len(emotions)]
    intensities = individual[len(emotions):]
    
    total_diff = abs(100 - sum(emotion_values))
    intensity_range = max(intensities) - min(intensities)
    emotion_balance = max(emotion_values) - min(emotion_values)
    
    return total_diff, intensity_range, emotion_balance

def evolve_emotions():
    toolbox = base.Toolbox()
    toolbox.register("attr_float", random.uniform, 0, 100)
    toolbox.register("attr_intensity", random.uniform, 0, 10)
    toolbox.register("individual", tools.initCycle, creator.Individual,
                     (toolbox.attr_float,) * len(emotions) +
                     (toolbox.attr_intensity,) * len(emotions), 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=50,
                               stats=None, halloffame=None, verbose=False)

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

def predict_emotion(context):
    load_models()
    inputs = emotion_prediction_tokenizer(context, return_tensors="pt", truncation=True, max_length=512)
    with torch.no_grad():
        outputs = emotion_prediction_model(**inputs)
    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_class = torch.argmax(probabilities, dim=-1).item()
    emotion_labels = ["sadness", "joy", "love", "anger", "fear", "surprise"]
    return emotion_labels[predicted_class]

def sentiment_analysis(text):
    sia = SentimentIntensityAnalyzer()
    sentiment_scores = sia.polarity_scores(text)
    return sentiment_scores

def extract_entities(text):
    # Lazy load the necessary NLTK data
    try:
        nltk.data.find('tokenizers/punkt')
    except LookupError:
        nltk.download('punkt', quiet=True, raise_on_error=True)

    try:
        nltk.data.find('taggers/averaged_perceptron_tagger')
    except LookupError:
        nltk.download('averaged_perceptron_tagger', quiet=True, raise_on_error=True)

    try:
        nltk.data.find('chunkers/maxent_ne_chunker')
    except LookupError:
        nltk.download('maxent_ne_chunker', quiet=True, raise_on_error=True)

    chunked = ne_chunk(pos_tag(word_tokenize(text)))
    entities = []
    for chunk in chunked:
        if hasattr(chunk, 'label'):
            entities.append(((' '.join(c[0] for c in chunk)), chunk.label()))
    return entities

def analyze_text_complexity(text):
    blob = TextBlob(text)
    return {
        'word_count': len(blob.words),
        'sentence_count': len(blob.sentences),
        'average_sentence_length': len(blob.words) / len(blob.sentences) if len(blob.sentences) > 0 else 0,
        'polarity': blob.sentiment.polarity,
        'subjectivity': blob.sentiment.subjectivity
    }

def get_ai_emotion(input_text):
    predicted_emotion = predict_emotion(input_text)
    ai_emotion = predicted_emotion
    ai_emotion_percentage = emotions[predicted_emotion]['percentage']
    ai_emotion_intensity = emotions[predicted_emotion]['intensity']
    return ai_emotion, ai_emotion_percentage, ai_emotion_intensity

def generate_emotion_visualization(ai_emotion, ai_emotion_percentage, ai_emotion_intensity):
    # Generate an emotion visualization based on the AI's emotional state
    emotion_visualization_path = 'emotional_state.png'
    try:
        # Generate and save the emotion visualization
        plt.figure(figsize=(8, 6))
        emotions_df = pd.DataFrame([(e, d['percentage'], d['intensity']) for e, d in emotions.items()],
                                   columns=['emotion', 'percentage', 'intensity'])
        sns.barplot(x='emotion', y='percentage', data=emotions_df)
        plt.title(f'Current Emotional State: {ai_emotion.capitalize()} ({ai_emotion_percentage:.2f}%)')
        plt.xlabel('Emotion')
        plt.ylabel('Percentage')
        plt.xticks(rotation=90)
        plt.savefig(emotion_visualization_path)
        plt.close()
    except Exception as e:
        print(f"Error generating emotion visualization: {e}")
        emotion_visualization_path = None
    return emotion_visualization_path

def generate_response(ai_emotion, input_text):
    load_models()
    # Prepare a prompt based on the current emotion
    prompt = f"As an AI assistant, I am currently feeling {ai_emotion}. My response will reflect this emotional state."
    
    # Generate the response
    inputs = response_tokenizer(prompt, return_tensors="pt", padding=True, truncation=True, max_length=8192)
    
    # Adjust generation parameters based on emotion
    temperature = 0.7
    if ai_emotion == 'anger':
        temperature = 0.9  # More randomness for angry responses
    elif ai_emotion == 'joy':
        temperature = 0.5  # More focused responses for joyful state

    with torch.no_grad():
        response_ids = response_model.generate(
            inputs.input_ids,
            attention_mask=inputs.attention_mask,
            max_length=400,
            num_return_sequences=1,
            no_repeat_ngram_size=2,
            do_sample=True,
            top_k=50,
            top_p=0.95,
            temperature=temperature,
            pad_token_id=response_tokenizer.eos_token_id
        )
    response = response_tokenizer.decode(response_ids[0], skip_special_tokens=True)
    
    # Extract only the AI's response
    return response.strip()

def interactive_interface(input_text):
    # Perform your processing logic here
    predicted_emotion = predict_emotion(input_text)
    sentiment_scores = sentiment_analysis(input_text)
    entities = extract_entities(input_text)
    text_complexity = analyze_text_complexity(input_text)
    ai_emotion, ai_emotion_percentage, ai_emotion_intensity = get_ai_emotion(input_text)
    emotion_visualization = generate_emotion_visualization(ai_emotion, ai_emotion_percentage, ai_emotion_intensity)
    response = generate_response(ai_emotion, input_text)

    # Update conversation history
    conversation_history.append({'user': input_text, 'response': response})
    if len(conversation_history) > max_history_length:
        conversation_history.pop(0)

    # Save conversation history to a file
    save_historical_data(conversation_history)

    # Return the expected outputs in the correct order
    return (
        gr.Textbox(value=predicted_emotion, label="Predicted Emotion"),
        gr.Textbox(value=str(sentiment_scores), label="Sentiment Scores"),
        gr.Textbox(value=str(entities), label="Extracted Entities"),
        gr.Textbox(value=str(text_complexity), label="Text Complexity"),
        gr.Textbox(value=response, label="AI Response"),
        gr.Image(value=emotion_visualization, label="Emotion Visualization")
    )

# Create the Gradio interface
iface = gr.Interface(fn=interactive_interface, inputs="text", outputs=[
    "text", "text", "text", "text", "text", "image"
], title="Emotion-Aware AI Assistant")

# Launch the interface
iface.launch()