app.py

#"👑Ultron-Praim👑"
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())