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import os |
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import asyncio |
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import logging |
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from transformers import AutoTokenizer, TFAutoModel, pipeline |
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from sentence_transformers import SentenceTransformer |
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from rl.agents import PPOAgent, DQNAgent, SACAgent, MetaRLAgent |
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from rl.memory import SequentialMemory |
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import tensorflow as tf |
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import numpy as np |
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import torch |
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import pandas as pd |
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import shutil |
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import matplotlib.pyplot as plt |
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import seaborn as sns |
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from pandas_profiling import ProfileReport |
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from sklearn.model_selection import train_test_split |
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from sklearn.metrics import classification_report |
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from sklearn.preprocessing import StandardScaler, LabelEncoder |
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from tensorflow.keras.models import Sequential, load_model |
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from tensorflow.keras.layers import Dense, Dropout, BatchNormalization |
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from ultralytics import YOLO |
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from mlagents_envs.environment import UnityEnvironment |
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from scipy import stats |
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import feature_engine.creation as fe |
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from PIL import Image |
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import cv2 |
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import faiss |
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from cryptography.fernet import Fernet |
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import pyttsx3 |
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import whisper |
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import requests |
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from bs4 import BeautifulSoup |
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import networkx as nx |
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import multiprocessing |
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import qiskit |
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logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") |
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CONFIG = { |
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"learning_rate": 1e-4, |
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"memory_limit": 10000, |
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"nb_actions": 5, |
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"tokenizer_model": "bert-base-uncased", |
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"sentence_embedder": "all-MiniLM-L6-v2", |
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"multimodal_model": "openai/clip-vit-base-patch32", |
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"index_path": "knowledge_index.faiss", |
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"whisper_model": "openai/whisper-base", |
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"t5_model": "t5-base", |
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"automl_model": "h2o.ai/automl", |
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"emotion_model": "microsoft/FacialEmotionRecognition", |
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"yolo_model": "yolov3.cfg", |
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"yolo_weights": "yolov3.weights", |
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"yolo_classes": "coco.names", |
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"dalle_model": "dalle-mini/dalle-mini-1", |
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"musenet_model": "muse-net/musenet-24000", |
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"quantum_backend": "qiskit.basicAer", |
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"tts_model": "facebook/tts-en-transformer" |
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} |
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tokenizer = AutoTokenizer.from_pretrained(CONFIG["tokenizer_model"]) |
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nlp_model = TFAutoModel.from_pretrained(CONFIG["tokenizer_model"]) |
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embedder = SentenceTransformer(CONFIG["sentence_embedder"]) |
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whisper_model = whisper.load_model(CONFIG["whisper_model"]) |
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t5_model = pipeline("text-generation", model=CONFIG["t5_model"]) |
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tts_model = pipeline("text-to-speech", model=CONFIG["tts_model"]) |
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class ContextualMemory: |
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def __init__(self): |
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self.short_term_memory = [] |
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self.long_term_memory = [] |
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def add_to_memory(self, query, response, memory_type="short"): |
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memory = {"query": query, "response": response} |
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if memory_type == "short": |
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self.short_term_memory.append(memory) |
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if len(self.short_term_memory) > CONFIG["memory_limit"]: |
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self.short_term_memory.pop(0) |
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elif memory_type == "long": |
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self.long_term_memory.append(memory) |
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def retrieve_memory(self, memory_type="short"): |
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return self.short_term_memory if memory_type == "short" else self.long_term_memory |
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class SecurityHandler: |
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def __init__(self): |
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self.key = Fernet.generate_key() |
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self.cipher = Fernet(self.key) |
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def encrypt(self, data): |
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return self.cipher.encrypt(data.encode()) |
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def decrypt(self, data): |
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return self.cipher.decrypt(data).decode() |
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class RLAgent: |
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def __init__(self, model_type="PPO"): |
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self.model_type = model_type |
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self.agent = self._initialize_agent() |
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def _initialize_agent(self): |
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if self.model_type == "PPO": |
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return PPOAgent() |
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elif self.model_type == "DQN": |
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return DQNAgent() |
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elif self.model_type == "SAC": |
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return SACAgent() |
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elif self.model_type == "MetaRL": |
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return MetaRLAgent() |
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else: |
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raise ValueError("Unsupported RL model type") |
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def act(self, state): |
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return f"Decision based on {self.model_type}: {state}" |
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class Ultron: |
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def __init__(self): |
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self.context_memory = ContextualMemory() |
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self.multimodal_processor = MultimodalProcessor() |
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self.task_manager = TaskManager() |
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self.security = SecurityHandler() |
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self.rl_agents = { |
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"GandMaster": RLAgent(model_type="PPO"), |
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"MasterMind": RLAgent(model_type="PPO"), |
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"BrainA1": RLAgent(model_type="PPO"), |
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"BrainA2": RLAgent(model_type="DQN"), |
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"BrainA3": RLAgent(model_type="SAC"), |
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"BrainA4": RLAgent(model_type="HRL"), |
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"BrainA5": RLAgent(model_type="MetaRL"), |
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} |
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self.speaker = pyttsx3.init() |
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self.quantum_processor = QuantumProcessor() |
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self.tts_model = tts_model |
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def speak(self, text): |
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"""Converts text to speech.""" |
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try: |
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self.speaker.say(text) |
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self.speaker.runAndWait() |
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except Exception as e: |
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logging.error(f"Text-to-Speech error: {e}") |
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async def process_query(self, query, input_type="text", file_path=None): |
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try: |
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if input_type == "text": |
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vectorized_query = tokenizer(query, return_tensors="tf", padding=True, truncation=True) |
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response = f"Processed text query: {query}" |
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elif input_type == "image": |
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response = self.multimodal_processor.process_image(file_path) |
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elif input_type == "video": |
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response = self.multimodal_processor.process_video(file_path) |
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elif input_type == "camera": |
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image_path = self.multimodal_processor.capture_image_from_camera() |
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response = self.multimodal_processor.process_image(image_path) |
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elif input_type == "speech": |
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result = whisper_model.transcribe(file_path) |
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response = result["text"] |
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elif input_type == "web": |
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response = self._web_scrape(query) |
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elif input_type == "emotion": |
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response = self._detect_emotion(file_path) |
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elif input_type == "yolo": |
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response = self.multimodal_processor.detect_objects(file_path) |
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elif input_type == "dalle": |
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response = self.generate_image(query) |
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elif input_type == "musenet": |
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response = self.generate_music(query) |
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elif input_type == "quantum": |
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circuit = qiskit.QuantumCircuit(2) |
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circuit.h(0) |
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circuit.cx(0, 1) |
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response = self.quantum_processor.run_quantum_circuit(circuit) |
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elif input_type == "tts": |
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response = self.tts_model(query) |
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else: |
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response = "Unsupported input type." |
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if input_type == "text": |
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if query.lower() not in [memory["query"].lower() for memory in self.context_memory.short_term_memory]: |
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t5_response = t5_model(f"Translate this to a query: {query}")[0]["generated_text"] |
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response += f" (Generated response: {t5_response})" |
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if input_type == "text": |
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feedback = input(f"Was the response helpful? (yes/no): ") |
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if feedback.lower() == "yes": |
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decision = self.rl_agents["GandMaster"].act(response) |
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self.context_memory.add_to_memory(query, response) |
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self.speak(response) |
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return f"{response} | RL Decision: {decision}" |
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elif feedback.lower() == "no": |
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decision = self.rl_agents["GandMaster"].act("Incorrect response, seeking improvements.") |
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self.context_memory.add_to_memory(query, "Incorrect response", memory_type="short") |
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return "Sorry, let's try again with a better response." |
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return response |
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except Exception as e: |
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logging.error(f"Query processing error: {e}") |
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return "An error occurred while processing the query." |
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def _web_scrape(self, query): |
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try: |
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url = f"https://www.google.com/search?q={query.replace(' ', '+')}" |
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headers = {'User-Agent': 'Mozilla/5.0'} |
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page = requests.get(url, headers=headers) |
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soup = BeautifulSoup(page.content, 'html.parser') |
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result = soup.find('div', {'id': 'main'}).text.strip() |
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return result[:500] |
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except Exception as e: |
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logging.error(f"Web scraping error: {e}") |
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return "Web scraping failed." |
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class TaskManager: |
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def __init__(self): |
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self.tasks = [] |
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def add_task(self, task_name, priority=1): |
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self.tasks.append({"task": task_name, "priority": priority}) |
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self.tasks = sorted(self.tasks, key=lambda x: x["priority"], reverse=True) |
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def get_next_task(self): |
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return self.tasks.pop(0) if self.tasks else None |
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class MultimodalProcessor: |
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def __init__(self): |
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self.clip_model = pipeline("feature-extraction", model=CONFIG["multimodal_model"]) |
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self.net = cv2.dnn.readNetFromDarknet(CONFIG["yolo_model"], CONFIG["yolo_weights"]) |
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self.net.setInput(cv2.dnn.blobFromImage) |
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def process_image(self, image_path): |
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try: |
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image = Image.open(image_path) |
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features = self.clip_model(image) |
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return features |
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except Exception as e: |
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logging.error(f"Image processing error: {e}") |
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return None |
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def process_video(self, video_path): |
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try: |
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video_frames = self._extract_video_frames(video_path) |
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features = [self.clip_model(frame) for frame in video_frames] |
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return features |
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except Exception as e: |
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logging.error(f"Video processing error: {e}") |
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return None |
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def _extract_video_frames(self, video_path, frame_rate=8): |
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cap = cv2.VideoCapture(video_path) |
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frames = [] |
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while cap.isOpened(): |
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ret, frame = cap.read() |
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if not ret: |
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break |
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frames.append(frame) |
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cap.release() |
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return frames[::frame_rate] |
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def capture_image_from_camera(self): |
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cap = cv2.VideoCapture(0) |
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ret, frame = cap.read() |
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image_path = "camera_capture.jpg" |
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cv2.imwrite(image_path, frame) |
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cap.release() |
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return image_path |
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def detect_objects(self, image_path): |
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try: |
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image = cv2.imread(image_path) |
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height, width = image.shape[:2] |
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self.net.setInput(cv2.dnn.blobFromImage(image, scalefactor=1/255, size=(416, 416), swapRB=True, crop=False)) |
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outs = self.net.forward(self.net.getUnconnectedOutLayersNames()) |
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for detection in outs[0]: |
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confidence = detection[5:] |
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class_id = np.argmax(confidence) |
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confidence_score = confidence[class_id] |
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if confidence_score > 0.5: |
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box = detection[:4] * np.array([width, height, width, height]) |
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center_x, center_y, box_width, box_height = box.astype(int) |
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start_x, start_y = int(center_x - box_width / 2), int(center_y - box_height / 2) |
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end_x, end_y = int(center_x + box_width / 2), int(center_y + box_height / 2) |
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cv2.rectangle(image, (start_x, start_y), (end_x, end_y), (0, 255, 0), 2) |
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cv2.putText(image, f"{class_id} {confidence_score:.2f}", (start_x, start_y - 10), |
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cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) |
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cv2.imwrite("object_detected.jpg", image) |
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return "object_detected.jpg" |
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except Exception as e: |
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logging.error(f"Object detection error: {e}") |
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return None |
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def _detect_emotion(self, image_path): |
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try: |
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image = Image.open(image_path) |
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features = self.multimodal_processor.clip_model(image) |
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emotion = features[0][0] |
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return f"Detected Emotion: {emotion}" |
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except Exception as e: |
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logging.error(f"Emotion detection error: {e}") |
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return "Emotion detection failed." |
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def data_analysis(): |
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def load_data(): |
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file_path = input("Enter the dataset path: ").strip() |
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try: |
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if file_path.endswith('.csv'): |
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data = pd.read_csv(file_path) |
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elif file_path.endswith('.xlsx'): |
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data = pd.read_excel(file_path) |
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elif file_path.endswith('.json'): |
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data = pd.read_json(file_path) |
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else: |
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raise ValueError("Unsupported file format. Use CSV, Excel, or JSON.") |
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print("Data loaded successfully!") |
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return data |
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except Exception as e: |
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print(f"Error loading data: {e}") |
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return None |
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def clean_data(data): |
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print("\nCleaning data...") |
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data.fillna(data.mean(), inplace=True) |
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data.drop_duplicates(inplace=True) |
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for col in data.select_dtypes(include=np.number): |
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z_scores = np.abs(stats.zscore(data[col])) |
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data = data[(z_scores < 3)] |
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print("Data cleaning completed!") |
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return data |
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def perform_eda(data): |
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print("\nPerforming EDA...") |
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profile = ProfileReport(data, title="EDA Report", explorative=True) |
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profile.to_file("eda_report.html") |
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sns.heatmap(data.corr(), annot=True, cmap="coolwarm") |
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plt.title("Correlation Matrix") |
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plt.show() |
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print("EDA report saved as 'eda_report.html'.") |
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def feature_engineering(data): |
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print("\nPerforming feature engineering...") |
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if 'time' in data.columns: |
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transformer = fe.CyclicFeatures(variables=['time'], max_value=24) |
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data = transformer.fit_transform(data) |
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print("Cyclic features created!") |
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else: |
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print("'time' column not found. Skipping cyclic features.") |
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return data |
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def build_and_train_dnn(data): |
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print("\nBuilding and training combined deep learning model...") |
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target = data.columns[-1] |
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features = data.drop(columns=[target]) |
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labels = data[target] |
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if labels.dtypes == 'object': |
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labels = LabelEncoder().fit_transform(labels) |
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X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42) |
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scaler = StandardScaler() |
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X_train = scaler.fit_transform(X_train) |
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X_test = scaler.transform(X_test) |
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model = Sequential([ |
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Dense(128, activation='relu', input_dim=X_train.shape[1]), |
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BatchNormalization(), |
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Dropout(0.3), |
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Dense(64, activation='relu'), |
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BatchNormalization(), |
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Dense(32, activation='relu'), |
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Dropout(0.2), |
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Dense(1, activation='sigmoid') |
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]) |
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model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) |
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model.fit(X_train, y_train, epochs=50, batch_size=32, validation_split=0.2) |
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y_pred = (model.predict(X_test) > 0.5).astype("int32") |
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print("\nModel Performance:") |
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print(classification_report(y_test, y_pred)) |
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return model |
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def yolo_object_detection(source="input_video.mp4"): |
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print("\nPerforming object detection...") |
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yolo_model = YOLO('yolov8n.pt') |
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yolo_model.predict(source=source, save=True) |
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print("YOLO object detection completed. Results saved!") |
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def unity_integration(unity_env_path): |
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print("\nIntegrating Unity ML-Agents...") |
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if os.path.exists(unity_env_path): |
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unity_env = UnityEnvironment(file_name=unity_env_path) |
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unity_env.reset() |
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print("Unity environment loaded!") |
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else: |
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print("Unity environment not found. Skipping Unity integration.") |
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def save_outputs(data): |
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os.makedirs("outputs", exist_ok=True) |
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data.to_csv("outputs/cleaned_data.csv", index=False) |
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shutil.move("eda_report.html", "outputs/eda_report.html") |
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print("All outputs saved in 'outputs' folder!") |
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def main(): |
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try: |
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data = load_data() |
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if data is None: |
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return |
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data = clean_data(data) |
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perform_eda(data) |
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data = feature_engineering(data) |
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model = build_and_train_dnn(data) |
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yolo_object_detection() |
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unity_env_path = input("\nEnter Unity environment path (optional): ").strip() |
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if unity_env_path: |
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unity_integration(unity_env_path) |
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save_outputs(data) |
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print("\nAll tasks completed successfully!") |
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except Exception as e: |
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print(f"An error occurred: {e}") |
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def generate_image(self, text): |
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try: |
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dalle_model = pipeline("text-to-image", model=CONFIG["dalle_model"]) |
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generated_image = dalle_model(text)[0]["generated_image"] |
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return generated_image |
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except Exception as e: |
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logging.error(f"Image generation error: {e}") |
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return "Image generation failed." |
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def generate_music(self, prompt): |
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try: |
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musenet_model = pipeline("music-generation", model=CONFIG["musenet_model"]) |
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generated_music = musenet_model(prompt)[0]["generated_music"] |
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return generated_music |
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except Exception as e: |
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logging.error(f"Music generation error: {e}") |
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return "Music generation failed." |
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async def run(self): |
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logging.info("Ultron started.") |
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while True: |
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user_input = input("Enter your query (or type 'exit'): ") |
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if user_input.lower() in ["exit", "quit"]: |
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logging.info("Shutting down Ultron. Goodbye!") |
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break |
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response = await self.process_query(user_input) |
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print(f"Ultron Response: {response}") |
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if __name__ == "__main__": |
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ultron = Ultron() |
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asyncio.run(ultron.run()) |