Update app.py

app.py

@@ -1,3 +1,138 @@
+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, 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.Embedding(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.long())
+
+# Memory-efficient dataset
+class MemoryEfficientDataset(IterableDataset):
+    def __init__(self, X, y, batch_size):
+        self.X = X
+        self.y = torch.LongTensor(y)  # Convert labels to long tensors
+        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), 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_workers=4, pin_memory=True)
+
+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, non_blocking=True), batch_y.to(device, non_blocking=True)
+        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
 emotion_history_file = 'emotion_history.json'
 
 def load_historical_data(file_path=emotion_history_file):
@@ -142,7 +277,7 @@ def process_input(text):
         
         predicted_emotion = emotion_classes[rf_prediction]
         sentiment_score = isolation_score
-       distilgpt3_generated_text = generate_text(normalized_text, model_type='distilgpt3')
+    distilgpt3_generated_text = generate_text(normalized_text, model_type='distilgpt3')
         bloom_generated_text = generate_text(normalized_text, model_type='bloom')
 
         historical_data = load_historical_data()