#"đź‘‘Ultron-Praimđź‘‘" import os import asyncio import logging from transformers import AutoTokenizer, TFAutoModel, pipeline from sentence_transformers import SentenceTransformer from rl.agents import PPOAgent, DQNAgent, SACAgent, MetaRLAgent from rl.memory import SequentialMemory import tensorflow as tf import numpy as np import torch import pandas as pd import shutil import matplotlib.pyplot as plt import seaborn as sns from pandas_profiling import ProfileReport from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report from sklearn.preprocessing import StandardScaler, LabelEncoder from tensorflow.keras.models import Sequential, load_model from tensorflow.keras.layers import Dense, Dropout, BatchNormalization from ultralytics import YOLO from mlagents_envs.environment import UnityEnvironment from scipy import stats import feature_engine.creation as fe from PIL import Image import cv2 import faiss from cryptography.fernet import Fernet import pyttsx3 # Text-to-Speech library import whisper # Whisper library for STT import requests from bs4 import BeautifulSoup # Web Scraping import networkx as nx # Knowledge Graph Management import multiprocessing # Multiprocessing for real-time task handling import qiskit # Quantum Computing Library # Logging Configuration logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") # Configuration CONFIG = { "learning_rate": 1e-4, "memory_limit": 10000, "nb_actions": 5, "tokenizer_model": "bert-base-uncased", "sentence_embedder": "all-MiniLM-L6-v2", "multimodal_model": "openai/clip-vit-base-patch32", "index_path": "knowledge_index.faiss", "whisper_model": "openai/whisper-base", "t5_model": "t5-base", # Few-Shot/Zero-Shot Learning "automl_model": "h2o.ai/automl", # AutoML Placeholder "emotion_model": "microsoft/FacialEmotionRecognition", # Emotion Detection Model "yolo_model": "yolov3.cfg", # YOLO Configuration "yolo_weights": "yolov3.weights", # YOLO Weights "yolo_classes": "coco.names", # YOLO Classes "dalle_model": "dalle-mini/dalle-mini-1", # Text-to-Image Model "musenet_model": "muse-net/musenet-24000", # Music Generation Model "quantum_backend": "qiskit.basicAer", # Quantum Computing Backend "tts_model": "facebook/tts-en-transformer" # Advanced Text-to-Speech } # Initialize Models tokenizer = AutoTokenizer.from_pretrained(CONFIG["tokenizer_model"]) nlp_model = TFAutoModel.from_pretrained(CONFIG["tokenizer_model"]) embedder = SentenceTransformer(CONFIG["sentence_embedder"]) # Initialize Whisper for Speech-to-Text whisper_model = whisper.load_model(CONFIG["whisper_model"]) # Initialize T5 for Few-Shot/Zero-Shot Learning t5_model = pipeline("text-generation", model=CONFIG["t5_model"]) # Initialize Advanced TTS and VALL-E tts_model = pipeline("text-to-speech", model=CONFIG["tts_model"]) # Memory Classes class ContextualMemory: def __init__(self): self.short_term_memory = [] self.long_term_memory = [] def add_to_memory(self, query, response, memory_type="short"): memory = {"query": query, "response": response} if memory_type == "short": self.short_term_memory.append(memory) if len(self.short_term_memory) > CONFIG["memory_limit"]: self.short_term_memory.pop(0) elif memory_type == "long": self.long_term_memory.append(memory) def retrieve_memory(self, memory_type="short"): return self.short_term_memory if memory_type == "short" else self.long_term_memory # Security Module class SecurityHandler: def __init__(self): self.key = Fernet.generate_key() self.cipher = Fernet(self.key) def encrypt(self, data): return self.cipher.encrypt(data.encode()) def decrypt(self, data): return self.cipher.decrypt(data).decode() # Reinforcement Learning Agents class RLAgent: def __init__(self, model_type="PPO"): self.model_type = model_type self.agent = self._initialize_agent() def _initialize_agent(self): if self.model_type == "PPO": return PPOAgent() elif self.model_type == "DQN": return DQNAgent() elif self.model_type == "SAC": return SACAgent() elif self.model_type == "MetaRL": return MetaRLAgent() else: raise ValueError("Unsupported RL model type") def act(self, state): # Placeholder: Reinforcement learning decision-making return f"Decision based on {self.model_type}: {state}" # Core AI System class Ultron: def __init__(self): self.context_memory = ContextualMemory() self.multimodal_processor = MultimodalProcessor() self.task_manager = TaskManager() self.security = SecurityHandler() self.rl_agents = { "GandMaster": RLAgent(model_type="PPO"), "MasterMind": RLAgent(model_type="PPO"), "BrainA1": RLAgent(model_type="PPO"), "BrainA2": RLAgent(model_type="DQN"), "BrainA3": RLAgent(model_type="SAC"), "BrainA4": RLAgent(model_type="HRL"), "BrainA5": RLAgent(model_type="MetaRL"), } self.speaker = pyttsx3.init() # Initialize text-to-speech engine self.quantum_processor = QuantumProcessor() # Initialize Quantum Processor self.tts_model = tts_model # Advanced Text-to-Speech Model def speak(self, text): """Converts text to speech.""" try: self.speaker.say(text) self.speaker.runAndWait() except Exception as e: logging.error(f"Text-to-Speech error: {e}") async def process_query(self, query, input_type="text", file_path=None): try: if input_type == "text": vectorized_query = tokenizer(query, return_tensors="tf", padding=True, truncation=True) response = f"Processed text query: {query}" elif input_type == "image": response = self.multimodal_processor.process_image(file_path) elif input_type == "video": response = self.multimodal_processor.process_video(file_path) elif input_type == "camera": image_path = self.multimodal_processor.capture_image_from_camera() response = self.multimodal_processor.process_image(image_path) elif input_type == "speech": result = whisper_model.transcribe(file_path) response = result["text"] elif input_type == "web": response = self._web_scrape(query) elif input_type == "emotion": response = self._detect_emotion(file_path) elif input_type == "yolo": response = self.multimodal_processor.detect_objects(file_path) elif input_type == "dalle": response = self.generate_image(query) elif input_type == "musenet": response = self.generate_music(query) elif input_type == "quantum": circuit = qiskit.QuantumCircuit(2) circuit.h(0) circuit.cx(0, 1) response = self.quantum_processor.run_quantum_circuit(circuit) elif input_type == "tts": response = self.tts_model(query) else: response = "Unsupported input type." # Few-Shot/Zero-Shot Learning with T5 if input_type == "text": if query.lower() not in [memory["query"].lower() for memory in self.context_memory.short_term_memory]: t5_response = t5_model(f"Translate this to a query: {query}")[0]["generated_text"] response += f" (Generated response: {t5_response})" # Reinforcement Learning with Human Feedback if input_type == "text": feedback = input(f"Was the response helpful? (yes/no): ") if feedback.lower() == "yes": decision = self.rl_agents["GandMaster"].act(response) self.context_memory.add_to_memory(query, response) self.speak(response) # Speak function invoked for each response return f"{response} | RL Decision: {decision}" elif feedback.lower() == "no": decision = self.rl_agents["GandMaster"].act("Incorrect response, seeking improvements.") self.context_memory.add_to_memory(query, "Incorrect response", memory_type="short") return "Sorry, let's try again with a better response." return response except Exception as e: logging.error(f"Query processing error: {e}") return "An error occurred while processing the query." def _web_scrape(self, query): try: url = f"https://www.google.com/search?q={query.replace(' ', '+')}" headers = {'User-Agent': 'Mozilla/5.0'} page = requests.get(url, headers=headers) soup = BeautifulSoup(page.content, 'html.parser') result = soup.find('div', {'id': 'main'}).text.strip() return result[:500] # Limit results to avoid long responses except Exception as e: logging.error(f"Web scraping error: {e}") return "Web scraping failed." # Task Management with Multiprocessing class TaskManager: def __init__(self): self.tasks = [] def add_task(self, task_name, priority=1): self.tasks.append({"task": task_name, "priority": priority}) self.tasks = sorted(self.tasks, key=lambda x: x["priority"], reverse=True) def get_next_task(self): return self.tasks.pop(0) if self.tasks else None # Multimodal Processing class MultimodalProcessor: def __init__(self): self.clip_model = pipeline("feature-extraction", model=CONFIG["multimodal_model"]) self.net = cv2.dnn.readNetFromDarknet(CONFIG["yolo_model"], CONFIG["yolo_weights"]) self.net.setInput(cv2.dnn.blobFromImage) def process_image(self, image_path): try: image = Image.open(image_path) features = self.clip_model(image) return features except Exception as e: logging.error(f"Image processing error: {e}") return None def process_video(self, video_path): try: video_frames = self._extract_video_frames(video_path) features = [self.clip_model(frame) for frame in video_frames] return features except Exception as e: logging.error(f"Video processing error: {e}") return None def _extract_video_frames(self, video_path, frame_rate=8): cap = cv2.VideoCapture(video_path) frames = [] while cap.isOpened(): ret, frame = cap.read() if not ret: break frames.append(frame) cap.release() return frames[::frame_rate] def capture_image_from_camera(self): cap = cv2.VideoCapture(0) ret, frame = cap.read() image_path = "camera_capture.jpg" cv2.imwrite(image_path, frame) cap.release() return image_path def detect_objects(self, image_path): try: image = cv2.imread(image_path) height, width = image.shape[:2] self.net.setInput(cv2.dnn.blobFromImage(image, scalefactor=1/255, size=(416, 416), swapRB=True, crop=False)) outs = self.net.forward(self.net.getUnconnectedOutLayersNames()) for detection in outs[0]: confidence = detection[5:] class_id = np.argmax(confidence) confidence_score = confidence[class_id] if confidence_score > 0.5: # Confidence threshold box = detection[:4] * np.array([width, height, width, height]) center_x, center_y, box_width, box_height = box.astype(int) start_x, start_y = int(center_x - box_width / 2), int(center_y - box_height / 2) end_x, end_y = int(center_x + box_width / 2), int(center_y + box_height / 2) cv2.rectangle(image, (start_x, start_y), (end_x, end_y), (0, 255, 0), 2) cv2.putText(image, f"{class_id} {confidence_score:.2f}", (start_x, start_y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) cv2.imwrite("object_detected.jpg", image) return "object_detected.jpg" except Exception as e: logging.error(f"Object detection error: {e}") return None def _detect_emotion(self, image_path): try: image = Image.open(image_path) features = self.multimodal_processor.clip_model(image) emotion = features[0][0] return f"Detected Emotion: {emotion}" except Exception as e: logging.error(f"Emotion detection error: {e}") return "Emotion detection failed." def data_analysis(): # Function to load data def load_data(): file_path = input("Enter the dataset path: ").strip() try: if file_path.endswith('.csv'): data = pd.read_csv(file_path) elif file_path.endswith('.xlsx'): data = pd.read_excel(file_path) elif file_path.endswith('.json'): data = pd.read_json(file_path) else: raise ValueError("Unsupported file format. Use CSV, Excel, or JSON.") print("Data loaded successfully!") return data except Exception as e: print(f"Error loading data: {e}") return None # Function to clean data def clean_data(data): print("\nCleaning data...") data.fillna(data.mean(), inplace=True) data.drop_duplicates(inplace=True) for col in data.select_dtypes(include=np.number): z_scores = np.abs(stats.zscore(data[col])) data = data[(z_scores < 3)] print("Data cleaning completed!") return data # Function for exploratory data analysis def perform_eda(data): print("\nPerforming EDA...") profile = ProfileReport(data, title="EDA Report", explorative=True) profile.to_file("eda_report.html") sns.heatmap(data.corr(), annot=True, cmap="coolwarm") plt.title("Correlation Matrix") plt.show() print("EDA report saved as 'eda_report.html'.") # Function for feature engineering def feature_engineering(data): print("\nPerforming feature engineering...") if 'time' in data.columns: transformer = fe.CyclicFeatures(variables=['time'], max_value=24) data = transformer.fit_transform(data) print("Cyclic features created!") else: print("'time' column not found. Skipping cyclic features.") return data # Function to build and train a combined deep learning model with pre-trained layers def build_and_train_dnn(data): print("\nBuilding and training combined deep learning model...") target = data.columns[-1] # Assume last column is the target features = data.drop(columns=[target]) labels = data[target] # Encode labels if categorical if labels.dtypes == 'object': labels = LabelEncoder().fit_transform(labels) # Split the data X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42) scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) # Combined model architecture model = Sequential([ Dense(128, activation='relu', input_dim=X_train.shape[1]), BatchNormalization(), Dropout(0.3), Dense(64, activation='relu'), BatchNormalization(), Dense(32, activation='relu'), Dropout(0.2), Dense(1, activation='sigmoid') # For binary classification ]) model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) model.fit(X_train, y_train, epochs=50, batch_size=32, validation_split=0.2) # Evaluate the model y_pred = (model.predict(X_test) > 0.5).astype("int32") print("\nModel Performance:") print(classification_report(y_test, y_pred)) return model # Function for YOLO object detection def yolo_object_detection(source="input_video.mp4"): print("\nPerforming object detection...") yolo_model = YOLO('yolov8n.pt') # Pre-trained YOLO model yolo_model.predict(source=source, save=True) print("YOLO object detection completed. Results saved!") # Function for Unity ML-Agents integration def unity_integration(unity_env_path): print("\nIntegrating Unity ML-Agents...") if os.path.exists(unity_env_path): unity_env = UnityEnvironment(file_name=unity_env_path) unity_env.reset() print("Unity environment loaded!") else: print("Unity environment not found. Skipping Unity integration.") # Function to save outputs def save_outputs(data): os.makedirs("outputs", exist_ok=True) data.to_csv("outputs/cleaned_data.csv", index=False) shutil.move("eda_report.html", "outputs/eda_report.html") print("All outputs saved in 'outputs' folder!") # Main function to execute the workflow def main(): try: # Step 1: Load Data data = load_data() if data is None: return # Step 2: Clean Data data = clean_data(data) # Step 3: EDA perform_eda(data) # Step 4: Feature Engineering data = feature_engineering(data) # Step 5: Train Combined Deep Learning Model model = build_and_train_dnn(data) # Step 6: YOLO Object Detection yolo_object_detection() # Step 7: Unity ML-Agents Integration unity_env_path = input("\nEnter Unity environment path (optional): ").strip() if unity_env_path: unity_integration(unity_env_path) # Step 8: Save Outputs save_outputs(data) print("\nAll tasks completed successfully!") except Exception as e: print(f"An error occurred: {e}") # Entry point #if __name__ == "__main__": # main() def generate_image(self, text): try: dalle_model = pipeline("text-to-image", model=CONFIG["dalle_model"]) generated_image = dalle_model(text)[0]["generated_image"] return generated_image except Exception as e: logging.error(f"Image generation error: {e}") return "Image generation failed." def generate_music(self, prompt): try: musenet_model = pipeline("music-generation", model=CONFIG["musenet_model"]) generated_music = musenet_model(prompt)[0]["generated_music"] return generated_music except Exception as e: logging.error(f"Music generation error: {e}") return "Music generation failed." async def run(self): logging.info("Ultron started.") while True: user_input = input("Enter your query (or type 'exit'): ") if user_input.lower() in ["exit", "quit"]: logging.info("Shutting down Ultron. Goodbye!") break response = await self.process_query(user_input) print(f"Ultron Response: {response}") # Main Execution if __name__ == "__main__": ultron = Ultron() asyncio.run(ultron.run())