Update app.py

app.py

@@ -1,140 +1,4 @@
-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
-default_percentage = total_percentage / len(emotions)
-for emotion in emotion_history_file = 'emotion_history.json'
+emotion_history_file = 'emotion_history.json'
 
 def load_historical_data(file_path=emotion_history_file):
     if os.path.exists(file_path):