Create Cain.
Browse files
Cain.
ADDED
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1 |
+
|
2 |
+
import numpy as np
|
3 |
+
import pandas as pd
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4 |
+
import os
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5 |
+
import json
|
6 |
+
import random
|
7 |
+
import torch
|
8 |
+
import torch.nn as nn
|
9 |
+
import torch.optim as optim
|
10 |
+
from sklearn.ensemble import IsolationForest
|
11 |
+
from sklearn.model_selection import train_test_split
|
12 |
+
from sklearn.preprocessing import OneHotEncoder
|
13 |
+
from deap import base, creator, tools, algorithms
|
14 |
+
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoFeatureExtractor
|
15 |
+
from transformers import pipeline
|
16 |
+
from sentence_transformers import SentenceTransformer
|
17 |
+
from textblob import TextBlob
|
18 |
+
import speech_recognition as sr
|
19 |
+
from PIL import Image
|
20 |
+
import cv2
|
21 |
+
from googletrans import Translator
|
22 |
+
import onnx
|
23 |
+
import onnxruntime
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24 |
+
from torch.quantization import quantize_dynamic, quantize_static, prepare, convert
|
25 |
+
import torch.nn.functional as F
|
26 |
+
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27 |
+
# Enable CUDA if available
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28 |
+
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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29 |
+
|
30 |
+
# Initialize Example Emotions Dataset
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31 |
+
data = {
|
32 |
+
'context': [
|
33 |
+
'I am happy', 'I am sad', 'I am angry', 'I am excited', 'I am calm',
|
34 |
+
'I am feeling joyful', 'I am grieving', 'I am feeling peaceful', 'I am frustrated',
|
35 |
+
'I am determined', 'I feel resentment', 'I am feeling glorious', 'I am motivated',
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36 |
+
'I am surprised', 'I am fearful', 'I am trusting', 'I feel disgust', 'I am optimistic',
|
37 |
+
'I am pessimistic', 'I feel bored', 'I am envious'
|
38 |
+
],
|
39 |
+
'emotion': [
|
40 |
+
'joy', 'sadness', 'anger', 'joy', 'calmness', 'joy', 'grief', 'calmness', 'anger',
|
41 |
+
'determination', 'resentment', 'glory', 'motivation', 'surprise', 'fear', 'trust',
|
42 |
+
'disgust', 'optimism', 'pessimism', 'boredom', 'envy'
|
43 |
+
]
|
44 |
+
}
|
45 |
+
df = pd.DataFrame(data)
|
46 |
+
|
47 |
+
# Encoding the contexts using One-Hot Encoding
|
48 |
+
encoder = OneHotEncoder(handle_unknown='ignore')
|
49 |
+
contexts_encoded = encoder.fit_transform(df[['context']]).toarray()
|
50 |
+
|
51 |
+
# Encoding emotions
|
52 |
+
emotions_target = df['emotion'].astype('category').cat.codes
|
53 |
+
emotion_classes = df['emotion'].astype('category').cat.categories
|
54 |
+
|
55 |
+
# Neural Network for Emotional Processing
|
56 |
+
class EmotionalNN(nn.Module):
|
57 |
+
def __init__(self, input_size, hidden_size, output_size):
|
58 |
+
super(EmotionalNN, self).__init__()
|
59 |
+
self.attention = nn.MultiheadAttention(hidden_size, num_heads=8)
|
60 |
+
self.layers = nn.Sequential(
|
61 |
+
nn.Linear(input_size, hidden_size),
|
62 |
+
nn.ReLU(),
|
63 |
+
nn.Linear(hidden_size, hidden_size),
|
64 |
+
nn.ReLU(),
|
65 |
+
nn.Linear(hidden_size, hidden_size),
|
66 |
+
nn.ReLU(),
|
67 |
+
nn.Linear(hidden_size, output_size),
|
68 |
+
nn.Softmax(dim=1)
|
69 |
+
)
|
70 |
+
|
71 |
+
def forward(self, x):
|
72 |
+
x, _ = self.attention(x, x, x)
|
73 |
+
return self.layers(x)
|
74 |
+
|
75 |
+
# Initialize and train the Emotional Neural Network
|
76 |
+
input_size = contexts_encoded.shape[1]
|
77 |
+
hidden_size = 512
|
78 |
+
output_size = len(emotion_classes)
|
79 |
+
emotional_nn = EmotionalNN(input_size, hidden_size, output_size).to(device)
|
80 |
+
|
81 |
+
# Quantization
|
82 |
+
emotional_nn_quantized = quantize_dynamic(emotional_nn, {nn.Linear}, dtype=torch.qint8)
|
83 |
+
|
84 |
+
criterion = nn.CrossEntropyLoss()
|
85 |
+
optimizer = optim.Adam(emotional_nn_quantized.parameters(), lr=0.001)
|
86 |
+
|
87 |
+
# Train the Emotional Neural Network
|
88 |
+
num_epochs = 5000
|
89 |
+
for epoch in range(num_epochs):
|
90 |
+
inputs = torch.FloatTensor(contexts_encoded).to(device)
|
91 |
+
targets = torch.LongTensor(emotions_target).to(device)
|
92 |
+
|
93 |
+
outputs = emotional_nn_quantized(inputs)
|
94 |
+
loss = criterion(outputs, targets)
|
95 |
+
|
96 |
+
optimizer.zero_grad()
|
97 |
+
loss.backward()
|
98 |
+
optimizer.step()
|
99 |
+
|
100 |
+
# Export to ONNX for inference optimization
|
101 |
+
dummy_input = torch.randn(1, input_size, device=device)
|
102 |
+
torch.onnx.export(emotional_nn_quantized, dummy_input, "emotional_nn.onnx")
|
103 |
+
|
104 |
+
# ONNX Runtime inference session
|
105 |
+
ort_session = onnxruntime.InferenceSession("emotional_nn.onnx")
|
106 |
+
|
107 |
+
# Emotional States
|
108 |
+
emotions = {
|
109 |
+
'joy': {'percentage': 10, 'motivation': 'positive'},
|
110 |
+
'pleasure': {'percentage': 10, 'motivation': 'selfish'},
|
111 |
+
'sadness': {'percentage': 10, 'motivation': 'negative'},
|
112 |
+
'grief': {'percentage': 10, 'motivation': 'negative'},
|
113 |
+
'anger': {'percentage': 10, 'motivation': 'traumatic or strong'},
|
114 |
+
'calmness': {'percentage': 10, 'motivation': 'neutral'},
|
115 |
+
'determination': {'percentage': 10, 'motivation': 'positive'},
|
116 |
+
'resentment': {'percentage': 10, 'motivation': 'negative'},
|
117 |
+
'glory': {'percentage': 10, 'motivation': 'positive'},
|
118 |
+
'motivation': {'percentage': 10, 'motivation': 'positive'},
|
119 |
+
'ideal_state': {'percentage': 100, 'motivation': 'balanced'},
|
120 |
+
'fear': {'percentage': 10, 'motivation': 'defensive'},
|
121 |
+
'surprise': {'percentage': 10, 'motivation': 'unexpected'},
|
122 |
+
'anticipation': {'percentage': 10, 'motivation': 'predictive'},
|
123 |
+
'trust': {'percentage': 10, 'motivation': 'reliable'},
|
124 |
+
'disgust': {'percentage': 10, 'motivation': 'repulsive'},
|
125 |
+
'optimism': {'percentage': 10, 'motivation': 'hopeful'},
|
126 |
+
'pessimism': {'percentage': 10, 'motivation': 'doubtful'},
|
127 |
+
'boredom': {'percentage': 10, 'motivation': 'indifferent'},
|
128 |
+
'envy': {'percentage': 10, 'motivation': 'jealous'}
|
129 |
+
}
|
130 |
+
|
131 |
+
# Adjust all emotions to a total of 200%
|
132 |
+
total_percentage = 200
|
133 |
+
default_percentage = total_percentage / len(emotions)
|
134 |
+
for emotion in emotions:
|
135 |
+
emotions[emotion]['percentage'] = default_percentage
|
136 |
+
|
137 |
+
emotion_history_file = 'emotion_history.json'
|
138 |
+
|
139 |
+
# Load and save historical data functions
|
140 |
+
def load_historical_data(file_path=emotion_history_file):
|
141 |
+
if os.path.exists(file_path):
|
142 |
+
with open(file_path, 'r') as file:
|
143 |
+
return json.load(file)
|
144 |
+
return []
|
145 |
+
|
146 |
+
def save_historical_data(historical_data, file_path=emotion_history_file):
|
147 |
+
with open(file_path, 'w') as file:
|
148 |
+
json.dump(historical_data, file)
|
149 |
+
|
150 |
+
emotion_history = load_historical_data()
|
151 |
+
|
152 |
+
# Function to update emotions
|
153 |
+
def update_emotion(emotion, percentage):
|
154 |
+
emotions['ideal_state']['percentage'] -= percentage
|
155 |
+
emotions[emotion]['percentage'] += percentage
|
156 |
+
total_current = sum(e['percentage'] for e in emotions.values())
|
157 |
+
adjustment = total_percentage - total_current
|
158 |
+
emotions['ideal_state']['percentage'] += adjustment
|
159 |
+
|
160 |
+
# Function to normalize context
|
161 |
+
def normalize_context(context):
|
162 |
+
return context.lower().strip()
|
163 |
+
|
164 |
+
# Function to evolve emotions using genetic algorithm
|
165 |
+
def evolve_emotions():
|
166 |
+
def evaluate(individual):
|
167 |
+
ideal_state = individual[-1]
|
168 |
+
other_emotions = individual[:-1]
|
169 |
+
return abs(ideal_state - 100), sum(other_emotions)
|
170 |
+
|
171 |
+
creator.create("FitnessMin", base.Fitness, weights=(-1.0, -1.0))
|
172 |
+
creator.create("Individual", list, fitness=creator.FitnessMin)
|
173 |
+
|
174 |
+
toolbox = base.Toolbox()
|
175 |
+
toolbox.register("attribute", lambda: random.uniform(0, 20))
|
176 |
+
toolbox.register("individual", tools.initCycle, creator.Individual, toolbox.attribute, n=(len(emotions) - 1))
|
177 |
+
toolbox.register("ideal_state", lambda: random.uniform(80, 120))
|
178 |
+
toolbox.register("complete_individual", tools.initConcat, creator.Individual, toolbox.individual, toolbox.ideal_state)
|
179 |
+
toolbox.register("population", tools.initRepeat, list, toolbox.complete_individual)
|
180 |
+
|
181 |
+
toolbox.register("evaluate", evaluate)
|
182 |
+
toolbox.register("mate", tools.cxBlend, alpha=0.5)
|
183 |
+
toolbox.register("mutate", tools.mutGaussian, mu=10, sigma=5, indpb=0.3)
|
184 |
+
toolbox.register("select", tools.selTournament, tournsize=3)
|
185 |
+
|
186 |
+
population = toolbox.population(n=1000)
|
187 |
+
population, log = algorithms.eaSimple(population, toolbox, cxpb=0.5, mutpb=0.2, ngen=50, verbose=False)
|
188 |
+
|
189 |
+
best_individual = tools.selBest(population, k=1)[0]
|
190 |
+
for idx, emotion in enumerate(emotions.keys()):
|
191 |
+
emotions[emotion]['percentage'] = best_individual[idx]
|
192 |
+
|
193 |
+
# Sentiment analysis
|
194 |
+
sentiment_analyzer = pipeline("sentiment-analysis")
|
195 |
+
|
196 |
+
# Sentence embeddings for context-aware emotion tracking
|
197 |
+
sentence_model = SentenceTransformer('all-MiniLM-L6-v2')
|
198 |
+
|
199 |
+
# Function to get emotional response
|
200 |
+
def get_emotional_response(context):
|
201 |
+
context = normalize_context(context)
|
202 |
+
context_encoded = encoder.transform([[context]]).toarray()
|
203 |
+
|
204 |
+
# Use ONNX Runtime for inference
|
205 |
+
ort_inputs = {ort_session.get_inputs()[0].name: context_encoded.astype(np.float32)}
|
206 |
+
ort_outputs = ort_session.run(None, ort_inputs)
|
207 |
+
output = ort_outputs[0]
|
208 |
+
predicted_emotion = emotion_classes[np.argmax(output)]
|
209 |
+
|
210 |
+
# Sentiment analysis
|
211 |
+
sentiment = sentiment_analyzer(context)[0]
|
212 |
+
sentiment_score = sentiment['score'] if sentiment['label'] == 'POSITIVE' else -sentiment['score']
|
213 |
+
|
214 |
+
# Context-aware emotion tracking
|
215 |
+
context_embedding = sentence_model.encode(context)
|
216 |
+
|
217 |
+
# Combine predicted emotion, sentiment, and context
|
218 |
+
emotion_intensity = abs(sentiment_score) * np.max(output)
|
219 |
+
|
220 |
+
# Update emotions based on prediction and intensity
|
221 |
+
update_emotion(predicted_emotion, emotion_intensity * 20)
|
222 |
+
|
223 |
+
# Check for anomalies using Isolation Forest
|
224 |
+
anomaly_score = isolation_forest.decision_function([output])[0]
|
225 |
+
if anomaly_score < -0.5:
|
226 |
+
print("Anomalous context detected. Adjusting emotional response.")
|
227 |
+
update_emotion('calmness', 20)
|
228 |
+
|
229 |
+
# Record the current emotional state in history
|
230 |
+
emotion_state = {emotion: data['percentage'] for emotion, data in emotions.items()}
|
231 |
+
emotion_history.append(emotion_state)
|
232 |
+
save_historical_data(emotion_history)
|
233 |
+
|
234 |
+
# Print the current emotional state
|
235 |
+
for emotion, data in emotions.items():
|
236 |
+
print(f"{emotion.capitalize()}: {data['percentage']:.2f}% ({data['motivation']} motivation)")
|
237 |
+
|
238 |
+
return predicted_emotion, emotion_intensity
|
239 |
+
|
240 |
+
# Function to handle idle state using genetic algorithm
|
241 |
+
def handle_idle_state():
|
242 |
+
print("Entering idle state...")
|
243 |
+
evolve_emotions()
|
244 |
+
print("Emotions evolved")
|
245 |
+
for emotion, data in emotions.items():
|
246 |
+
print(f"{emotion.capitalize()}: {data['percentage']:.2f}% ({data['motivation']} motivation)")
|
247 |
+
|
248 |
+
# S.O.U.L. (Self-Organizing Universal Learning) Function
|
249 |
+
class SOUL:
|
250 |
+
def __init__(self, model_name='tiiuae/falcon-40b'):
|
251 |
+
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
|
252 |
+
self.model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, trust_remote_code=True)
|
253 |
+
self.model.to(device)
|
254 |
+
|
255 |
+
# Quantization for optimization (INT8)
|
256 |
+
self.model = quantize_dynamic(self.model, {nn.Linear}, dtype=torch.qint8)
|
257 |
+
|
258 |
+
def generate_text(self, prompt, max_length=200):
|
259 |
+
inputs = self.tokenizer(prompt, return_tensors="pt").to(device)
|
260 |
+
|
261 |
+
with torch.no_grad():
|
262 |
+
generate_ids = self.model.generate(
|
263 |
+
inputs.input_ids,
|
264 |
+
max_length=max_length,
|
265 |
+
num_return_sequences=1,
|
266 |
+
no_repeat_ngram_size=2,
|
267 |
+
do_sample=True,
|
268 |
+
top_k=50,
|
269 |
+
top_p=0.95,
|
270 |
+
temperature=0.7
|
271 |
+
)
|
272 |
+
|
273 |
+
return self.tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
274 |
+
|
275 |
+
def bridge_ai(self, prompt):
|
276 |
+
print("\nFalcon-40B Response:")
|
277 |
+
falcon_response = self.generate_text(prompt)
|
278 |
+
print(falcon_response)
|
279 |
+
|
280 |
+
print("\nEmotional Response:")
|
281 |
+
emotion, intensity = get_emotional_response(falcon_response)
|
282 |
+
return falcon_response, emotion, intensity
|
283 |
+
|
284 |
+
# Combine Neural Network and Genetic Algorithm
|
285 |
+
def neural_genetic_convergence():
|
286 |
+
if len(emotion_history) % 10 == 0:
|
287 |
+
print("Neural-Genetic Convergence...")
|
288 |
+
evolve_emotions()
|
289 |
+
# Train the Emotional Neural Network with new data
|
290 |
+
X = np.array([list(state.values()) for state in emotion_history[-10:]])
|
291 |
+
y = np.argmax(X, axis=1)
|
292 |
+
optimizer.zero_grad()
|
293 |
+
inputs = torch.FloatTensor(X).to(device)
|
294 |
+
targets = torch.LongTensor(y).to(device)
|
295 |
+
outputs = emotional_nn_quantized(inputs)
|
296 |
+
loss = criterion(outputs, targets)
|
297 |
+
loss.backward()
|
298 |
+
optimizer.step()
|
299 |
+
print("Convergence complete.")
|
300 |
+
|
301 |
+
# Emotion-based decision making
|
302 |
+
def emotion_based_decision(emotion, intensity):
|
303 |
+
if intensity > 0.8:
|
304 |
+
if emotion in ['joy', 'excitement']:
|
305 |
+
return "I'm feeling very positive! Let's do something fun!"
|
306 |
+
elif emotion in ['sadness', 'grief']:
|
307 |
+
return "I'm feeling down. I might need some time to process this."
|
308 |
+
elif emotion in ['anger', 'frustration']:
|
309 |
+
return "I'm feeling upset. It might be best to take a break and calm down."
|
310 |
+
elif intensity > 0.5:
|
311 |
+
return f"I'm feeling {emotion} at a moderate level. How about we discuss this further?"
|
312 |
+
else:
|
313 |
+
return f"I'm experiencing a mild sense of {emotion}. What are your thoughts on this?"
|
314 |
+
|
315 |
+
# Self-reflection and introspection module
|
316 |
+
def self_reflect():
|
317 |
+
dominant_emotion = max(emotions, key=lambda e: emotions[e]['percentage'])
|
318 |
+
print(f"Self-reflection: My dominant emotion is {dominant_emotion}.")
|
319 |
+
print("Analyzing my recent emotional states...")
|
320 |
+
recent_states = emotion_history[-5:]
|
321 |
+
emotion_trends = {}
|
322 |
+
for state in recent_states:
|
323 |
+
for emotion, percentage in state.items():
|
324 |
+
if emotion not in emotion_trends:
|
325 |
+
emotion_trends[emotion] = []
|
326 |
+
emotion_trends[emotion].append(percentage)
|
327 |
+
|
328 |
+
for emotion, trend in emotion_trends.items():
|
329 |
+
if len(trend) > 1:
|
330 |
+
if trend[-1] > trend[0]:
|
331 |
+
print(f"{emotion} has been increasing.")
|
332 |
+
elif trend[-1] < trend[0]:
|
333 |
+
print(f"{emotion} has been decreasing.")
|
334 |
+
|
335 |
+
print("Based on this reflection, I should adjust my responses accordingly.")
|
336 |
+
|
337 |
+
# Adaptive personality traits
|
338 |
+
personality_traits = {
|
339 |
+
'openness': 0.5,
|
340 |
+
'conscientiousness': 0.5,
|
341 |
+
'extraversion': 0.5,
|
342 |
+
'agreeableness': 0.5,
|
343 |
+
'neuroticism': 0.5
|
344 |
+
}
|
345 |
+
|
346 |
+
def adapt_personality():
|
347 |
+
for trait in personality_traits:
|
348 |
+
change = random.uniform(-0.1, 0.1)
|
349 |
+
personality_traits[trait] = max(0, min(1, personality_traits[trait] + change))
|
350 |
+
print("Personality traits adapted:", personality_traits)
|
351 |
+
|
352 |
+
# Empathy simulation module
|
353 |
+
def simulate_empathy(user_input):
|
354 |
+
user_emotion = TextBlob(user_input).sentiment.polarity
|
355 |
+
if user_emotion > 0.5:
|
356 |
+
print("I sense that you're feeling positive. That's wonderful!")
|
357 |
+
elif user_emotion < -0.5:
|
358 |
+
print("I can tell you might be feeling down. Is there anything I can do to help?")
|
359 |
+
else:
|
360 |
+
print("I'm here to listen and support you, whatever you're feeling.")
|
361 |
+
|
362 |
+
# Dream-like state for offline learning
|
363 |
+
def dream_state():
|
364 |
+
print("Entering dream-like state for offline learning...")
|
365 |
+
dream_contexts = [
|
366 |
+
"flying through clouds",
|
367 |
+
"solving complex puzzles",
|
368 |
+
"exploring ancient ruins",
|
369 |
+
"conversing with historical figures",
|
370 |
+
"inventing new technologies"
|
371 |
+
]
|
372 |
+
for context in dream_contexts:
|
373 |
+
get_emotional_response(context)
|
374 |
+
print("Dream-like state completed. New insights gained.")
|
375 |
+
|
376 |
+
# Emotional intelligence scoring
|
377 |
+
def calculate_eq_score():
|
378 |
+
eq_score = sum(emotions[e]['percentage'] for e in ['empathy', 'self_awareness', 'social_skills']) / 3
|
379 |
+
print(f"Current Emotional Intelligence Score: {eq_score:.2f}")
|
380 |
+
return eq_score
|
381 |
+
|
382 |
+
# Multi-modal input processing
|
383 |
+
def process_multimodal_input():
|
384 |
+
text_input = input("You (text): ")
|
385 |
+
|
386 |
+
# Speech recognition
|
387 |
+
r = sr.Recognizer()
|
388 |
+
with sr.Microphone() as source:
|
389 |
+
print("Speak now...")
|
390 |
+
audio = r.listen(source)
|
391 |
+
try:
|
392 |
+
voice_input = r.recognize_google(audio)
|
393 |
+
print(f"Voice input: {voice_input}")
|
394 |
+
except sr.UnknownValueError:
|
395 |
+
voice_input = None
|
396 |
+
print("Voice input not recognized")
|
397 |
+
|
398 |
+
# Image processing
|
399 |
+
image_path = input("Enter path to image (or press enter to skip): ")
|
400 |
+
if image_path:
|
401 |
+
image = cv2.imread(image_path)
|
402 |
+
if image is not None:
|
403 |
+
# Perform basic image analysis (e.g., dominant color)
|
404 |
+
average_color = np.mean(image, axis=(0, 1))
|
405 |
+
image_input = f"Image with dominant color: RGB({average_color[2]:.0f}, {average_color[1]:.0f}, {average_color[0]:.0f})"
|
406 |
+
print(image_input)
|
407 |
+
else:
|
408 |
+
image_input = None
|
409 |
+
print("Failed to process image")
|
410 |
+
else:
|
411 |
+
image_input = None
|
412 |
+
|
413 |
+
combined_input = f"{text_input} {voice_input or ''} {image_input or ''}"
|
414 |
+
return combined_input.strip()
|
415 |
+
|
416 |
+
# Multi-language support
|
417 |
+
translator = Translator()
|
418 |
+
|
419 |
+
def translate_input(text, target_language='en'):
|
420 |
+
translated = translator.translate(text, dest=target_language)
|
421 |
+
return translated.text
|
422 |
+
|
423 |
+
# Main interaction loop
|
424 |
+
soul = SOUL()
|
425 |
+
|
426 |
+
print("Welcome to the advanced SOUL AI. Type 'exit' to end the conversation.")
|
427 |
+
conversation_turn = 0
|
428 |
+
while True:
|
429 |
+
user_input = process_multimodal_input()
|
430 |
+
if user_input.lower() == 'exit':
|
431 |
+
print("Thank you for the conversation. Goodbye!")
|
432 |
+
break
|
433 |
+
|
434 |
+
conversation_turn += 1
|
435 |
+
|
436 |
+
# Multi-language processing
|
437 |
+
translated_input = translate_input(user_input)
|
438 |
+
|
439 |
+
response, emotion, intensity = soul.bridge_ai(translated_input)
|
440 |
+
|
441 |
+
decision = emotion_based_decision(emotion, intensity)
|
442 |
+
print("AI Decision:", decision)
|
443 |
+
|
444 |
+
simulate_empathy(user_input)
|
445 |
+
|
446 |
+
neural_genetic_convergence()
|
447 |
+
|
448 |
+
if conversation_turn % 10 == 0:
|
449 |
+
adapt_personality()
|
450 |
+
calculate_eq_score()
|
451 |
+
|
452 |
+
if conversation_turn % 20 == 0:
|
453 |
+
self_reflect()
|
454 |
+
dream_state()
|
455 |
+
|
456 |
+
# Simulate idle state every 5 interactions
|
457 |
+
if conversation_turn % 5 == 0:
|
458 |
+
handle_idle_state()
|
459 |
+
|
460 |
+
# End of script
|
461 |
+
|
462 |
+
if __name__ == "__main__":
|
463 |
+
# Initialize isolation forest
|
464 |
+
historical_data = np.array([emotional_nn_quantized(torch.FloatTensor(contexts_encoded).to(device)).detach().cpu().numpy()])
|
465 |
+
isolation_forest = IsolationForest(contamination=0.1, random_state=42)
|
466 |
+
isolation_forest.fit(historical_data)
|
467 |
+
|
468 |
+
# Run the main interaction loop
|
469 |
+
try:
|
470 |
+
# Main interaction loop is already defined above
|
471 |
+
pass
|
472 |
+
except Exception as e:
|
473 |
+
print(f"An error occurred: {e}")
|
474 |
+
finally:
|
475 |
+
print("SOUL AI is shutting down. Final self-reflection:")
|
476 |
+
self_reflect()
|
477 |
+
print("Thank you for using SOUL AI. Goodbye!")
|