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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+IMG_0344.jpeg filter=lfs diff=lfs merge=lfs -text

audio_analysis.py

@@ -0,0 +1,54 @@
+import numpy as np
+import librosa
+import librosa.display
+import torch
+import torchaudio
+import torchaudio.transforms as T
+from pydub import AudioSegment
+from transformers import pipeline
+import scipy.signal
+from speechbrain.pretrained import EncoderClassifier
+
+# Load Audio File
+def load_audio(file_path, target_sr=16000):
+    y, sr = librosa.load(file_path, sr=target_sr)
+    return y, sr
+
+# Noise Reduction using Spectral Subtraction
+def reduce_noise(y, sr):
+    reduced_noise = scipy.signal.medfilt(y, kernel_size=3)
+    return reduced_noise
+
+# Convert Speech to Text (ASR)
+def speech_to_text(file_path):
+    asr_model = pipeline("automatic-speech-recognition", model="openai/whisper-small")
+    transcript = asr_model(file_path)
+    return transcript["text"]
+
+# Emotion Detection from Voice
+def detect_emotion(file_path):
+    classifier = EncoderClassifier.from_hparams(source="speechbrain/emotion-recognition-wav2vec2", 
+                                                savedir="pretrained_models/emotion-recognition")
+    signal, sr = torchaudio.load(file_path)
+    emotion = classifier.classify_batch(signal)
+    return emotion[3]  # Returns predicted emotion
+
+# Sound Classification (e.g., Speech, Music, Noise)
+def classify_sound(file_path):
+    classifier = EncoderClassifier.from_hparams(source="speechbrain/sound-classification", 
+                                                savedir="pretrained_models/sound-classification")
+    signal, sr = torchaudio.load(file_path)
+    category = classifier.classify_batch(signal)
+    return category[3]  # Returns predicted sound category
+
+# Save Processed Audio
+def save_audio(y, sr, output_path):
+    librosa.output.write_wav(output_path, y, sr)
+
+# Audio Feature Extraction for AI Model Input
+def extract_features(file_path):
+    y, sr = librosa.load(file_path, sr=16000)
+    mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
+    chroma = librosa.feature.chroma_stft(y=y, sr=sr)
+    mel_spec = librosa.feature.melspectrogram(y=y, sr=sr)
+    return {"mfccs": mfccs, "chroma": chroma, "mel_spec": mel_spec}

charm_c10_nlp.py

@@ -0,0 +1,92 @@
+import torch
+import torch.nn as nn
+from transformers import AutoTokenizer
+import firebase_admin
+from firebase_admin import credentials, firestore
+import logging
+
+# Import classes and functions from model.py
+from inference.model import ModelArgs, Transformer
+
+# Setup Firebase
+cred = credentials.Certificate("firebase-config.json")  # Replace with your Firebase Admin SDK JSON
+firebase_admin.initialize_app(cred)
+db = firestore.client()
+
+# Setup logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class CharmC10NLP(nn.Module):
+    def __init__(
+        self,
+        model_name="meta-llama/Llama-2-13b",
+        freeze_encoder=False,
+        use_fp16=False,
+        max_new_tokens=200,
+    ):
+        super(CharmC10NLP, self).__init__()
+        
+        # Load tokenizer
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+        
+        # Initialize ModelArgs
+        self.args = ModelArgs()
+        
+        # Initialize Transformer model
+        self.model = Transformer(self.args)
+        
+        # Freeze the model if required
+        if freeze_encoder:
+            for param in self.model.parameters():
+                param.requires_grad = False
+            logger.info("Model frozen for fine-tuning.")
+
+        # Mixed precision (FP16)
+        self.use_fp16 = use_fp16
+        if self.use_fp16 and torch.cuda.is_available():
+            logger.info("FP16 enabled for mixed precision training.")
+
+        # Generation settings
+        self.max_new_tokens = max_new_tokens
+        
+        # Auto-detect device (GPU/CPU)
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.model.to(self.device)
+        logger.info(f"Model running on {self.device}")
+
+    def generate_response(self, prompt, temperature=0.7, top_p=0.9):
+        """Generates an AI response based on input prompt."""
+        inputs = self.tokenizer(
+            prompt,
+            return_tensors="pt",
+            max_length=512,
+            padding=True,
+            truncation=True
+        ).to(self.device)
+
+        with torch.no_grad():
+            output_ids = self.model.generate(
+                inputs["input_ids"],
+                max_new_tokens=self.max_new_tokens,
+                temperature=temperature,
+                top_p=top_p,
+                do_sample=True
+            )
+
+        response = self.tokenizer.decode(output_ids[0], skip_special_tokens=True)
+        
+        # Store AI response in Firestore
+        self.store_response(prompt, response)
+        
+        return response
+
+    def store_response(self, prompt, response):
+        """Saves the AI-generated response to Firestore."""
+        doc_ref = db.collection("chatflare_responses").document()
+        doc_ref.set({
+            "prompt": prompt,
+            "response": response,
+            "timestamp": firestore.SERVER_TIMESTAMP
+        })
+        logger.info("Response stored in Firestore.")

data_security.py

@@ -0,0 +1,180 @@
+import os
+import logging
+from cryptography.hazmat.primitives import hashes
+from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC
+from cryptography.hazmat.primitives.asymmetric import rsa
+from cryptography.hazmat.primitives import serialization
+from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes
+from cryptography.hazmat.backends import default_backend
+from cryptography.hazmat.primitives import padding
+from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC
+import base64
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+
+# Secure key derivation using PBKDF2
+def derive_key(password: str, salt: bytes) -> bytes:
+    try:
+        kdf = PBKDF2HMAC(
+            algorithm=hashes.SHA256(),
+            length=32,
+            salt=salt,
+            iterations=100000,
+            backend=default_backend()
+        )
+        return kdf.derive(password.encode())
+    except Exception as e:
+        logging.error(f"Key derivation failed: {e}")
+        raise
+
+# Generate a strong RSA public-private key pair
+def generate_rsa_keys():
+    try:
+        private_key = rsa.generate_private_key(
+            public_exponent=65537,
+            key_size=2048,
+            backend=default_backend()
+        )
+        public_key = private_key.public_key()
+
+        private_pem = private_key.private_bytes(
+            encoding=serialization.Encoding.PEM,
+            format=serialization.PrivateFormat.TraditionalOpenSSL,
+            encryption_algorithm=serialization.NoEncryption()
+        )
+
+        public_pem = public_key.public_bytes(
+            encoding=serialization.Encoding.PEM,
+            format=serialization.PublicFormat.SubjectPublicKeyInfo
+        )
+
+        return private_pem, public_pem
+    except Exception as e:
+        logging.error(f"RSA key generation failed: {e}")
+        raise
+
+# Encrypt data using AES
+def aes_encrypt(data: bytes, key: bytes) -> bytes:
+    try:
+        iv = os.urandom(16)
+        cipher = Cipher(algorithms.AES(key), modes.CBC(iv), backend=default_backend())
+        encryptor = cipher.encryptor()
+        
+        padder = padding.PKCS7(algorithms.AES.block_size).padder()
+        padded_data = padder.update(data) + padder.finalize()
+
+        encrypted_data = encryptor.update(padded_data) + encryptor.finalize()
+        return iv + encrypted_data
+    except Exception as e:
+        logging.error(f"AES encryption failed: {e}")
+        raise
+
+# Decrypt data using AES
+def aes_decrypt(encrypted_data: bytes, key: bytes) -> bytes:
+    try:
+        iv = encrypted_data[:16]
+        cipher_data = encrypted_data[16:]
+
+        cipher = Cipher(algorithms.AES(key), modes.CBC(iv), backend=default_backend())
+        decryptor = cipher.decryptor()
+        
+        decrypted_data = decryptor.update(cipher_data) + decryptor.finalize()
+        
+        unpadder = padding.PKCS7(algorithms.AES.block_size).unpadder()
+        unpadded_data = unpadder.update(decrypted_data) + unpadder.finalize()
+
+        return unpadded_data
+    except Exception as e:
+        logging.error(f"AES decryption failed: {e}")
+        raise
+
+# RSA encryption and decryption
+def rsa_encrypt(public_key: bytes, data: bytes) -> bytes:
+    try:
+        public_key_obj = serialization.load_pem_public_key(public_key, backend=default_backend())
+        encrypted_data = public_key_obj.encrypt(
+            data,
+            rsa.OAEP(
+                mgf=rsa.MGF1(algorithm=hashes.SHA256()),
+                algorithm=hashes.SHA256(),
+                label=None
+            )
+        )
+        return encrypted_data
+    except Exception as e:
+        logging.error(f"RSA encryption failed: {e}")
+        raise
+
+def rsa_decrypt(private_key: bytes, encrypted_data: bytes) -> bytes:
+    try:
+        private_key_obj = serialization.load_pem_private_key(private_key, password=None, backend=default_backend())
+        decrypted_data = private_key_obj.decrypt(
+            encrypted_data,
+            rsa.OAEP(
+                mgf=rsa.MGF1(algorithm=hashes.SHA256()),
+                algorithm=hashes.SHA256(),
+                label=None
+            )
+        )
+        return decrypted_data
+    except Exception as e:
+        logging.error(f"RSA decryption failed: {e}")
+        raise
+
+# Securely store and retrieve sensitive information
+def store_encrypted_data(file_path: str, data: bytes, encryption_key: bytes):
+    try:
+        encrypted_data = aes_encrypt(data, encryption_key)
+        with open(file_path, 'wb') as file:
+            file.write(encrypted_data)
+        logging.info(f"Data securely stored at {file_path}")
+    except Exception as e:
+        logging.error(f"Failed to store encrypted data: {e}")
+        raise
+
+def retrieve_encrypted_data(file_path: str, encryption_key: bytes) -> bytes:
+    try:
+        with open(file_path, 'rb') as file:
+            encrypted_data = file.read()
+        return aes_decrypt(encrypted_data, encryption_key)
+    except Exception as e:
+        logging.error(f"Failed to retrieve encrypted data: {e}")
+        raise
+
+# Ensure strong random number generation for cryptographic operations
+def secure_random_bytes(size: int) -> bytes:
+    try:
+        return os.urandom(size)
+    except Exception as e:
+        logging.error(f"Random byte generation failed: {e}")
+        raise
+
+# Salting passwords for extra protection
+def salt_password(password: str) -> tuple:
+    try:
+        salt = secure_random_bytes(16)
+        salted_password = derive_key(password, salt)
+        return salted_password, salt
+    except Exception as e:
+        logging.error(f"Password salting failed: {e}")
+        raise
+
+# Key storage for private key management (secure storage)
+def store_rsa_private_key(private_key: bytes, file_path: str):
+    try:
+        with open(file_path, 'wb') as file:
+            file.write(private_key)
+        logging.info(f"RSA private key securely stored at {file_path}")
+    except Exception as e:
+        logging.error(f"Failed to store RSA private key: {e}")
+        raise
+
+def retrieve_rsa_private_key(file_path: str) -> bytes:
+    try:
+        with open(file_path, 'rb') as file:
+            private_key = file.read()
+        return private_key
+    except Exception as e:
+        logging.error(f"Failed to retrieve RSA private key: {e}")
+        raise

ethical_guidelines.py

@@ -0,0 +1,89 @@
+# ethical_guidelines.py
+
+class EthicalStandards:
+    def __init__(self):
+        # Define the principles the system will follow
+        self.standards = {
+            'fairness_check': False,
+            'data_protection': True,
+            'clarity': True,
+            'responsibility': True,
+            'security': True,
+            'acceptance': True
+        }
+
+    def check_output_fairness(self, output):
+        """
+        Ensures that the AI output adheres to fairness standards.
+        This checks for biased or unfair responses.
+        """
+        # Example check for fairness: If the output contains terms flagged as unfair, it's flagged as unfair
+        if 'unfair' in output:
+            self.standards['fairness_check'] = False
+        else:
+            self.standards['fairness_check'] = True
+
+    def protect_data(self, data):
+        """
+        Handle sensitive information appropriately, ensuring data protection.
+        """
+        if 'confidential' in data:
+            return self.safeguard_information(data)
+        return data
+
+    def safeguard_information(self, data):
+        """
+        Safeguards sensitive information by masking or redacting it.
+        """
+        return {key: 'REDACTED' for key in data}
+
+    def explain_decision(self, decision):
+        """
+        Clarify the reasoning behind the AI system's decisions.
+        This helps in maintaining transparency.
+        """
+        if 'reason' in decision:
+            return f"Decision made based on: {decision['reason']}"
+        return "Decision explanation not available."
+
+    def ensure_acceptance(self, text):
+        """
+        Make sure the generated text adheres to inclusive and positive language.
+        """
+        if 'exclude' in text:
+            return "Let's focus on inclusive and positive language."
+        return text
+
+    def secure_output(self, output):
+        """
+        Ensures that the output does not contain harmful or unsafe content.
+        """
+        # Basic check for harmful content (no need for specific list)
+        if 'harmful' in output or 'abusive' in output:
+            return "Output contains harmful content and has been filtered."
+        
+        return output
+
+    def log_standards(self):
+        """
+        Logs the current adherence status to each ethical standard.
+        """
+        for standard, status in self.standards.items():
+            print(f"Standard - {standard}: {'Compliant' if status else 'Non-compliant'}")
+
+    def enforce_all_standards(self, input_data):
+        """
+        Runs a series of checks to ensure all ethical standards are met.
+        """
+        self.check_output_fairness(input_data)
+        self.protect_data(input_data)
+        # Add further checks as needed
+
+    def process_and_check_output(self, prompt):
+        """
+        Process the input prompt, apply ethical checks, and return the ethical output.
+        """
+        processed_output = self.secure_output(prompt)
+        self.check_output_fairness(processed_output)
+        processed_output = self.ensure_acceptance(processed_output)
+        return processed_output

firewall.py

@@ -0,0 +1,75 @@
+import socket
+import os
+import logging
+import threading
+from scapy.all import sniff, IP, TCP, UDP
+
+# Configuration
+rules = {"192.168.1.100", "10.0.0.200"}  # IPs to block
+blocked = {"example.com", "test.net"}     # Domains to block
+log_path = "events.log"
+
+# Logging setup
+logging.basicConfig(filename=log_path, level=logging.INFO,
+               https://www.google.com/search?q=grok+3&ie=UTF-8&oe=UTF-8&hl=en-ph&client=safari#scso=_BVy2Z4uXJsCovr0P4tq76Aw_111:496     format="%(asctime)s - %(levelname)s - %(message)s")
+
+def log_action(entry):
+    """Log an action and print it to the console."""
+    logging.info(entry)
+    print(f"[System] {entry}")
+
+def check_rule(item):
+    """Check if an IP is in the rules set."""
+    return item in rules
+
+def check_data(data):
+    """Check if any blocked domain is in the data."""
+    return any(item in data for item in blocked)
+
+def resolve(item):
+    """Resolve a domain name to an IP address."""
+    try:
+        return socket.gethostbyname(item)
+    except socket.gaierror:
+        return None
+
+def analyze(packet):
+    """Analyze a network packet and enforce rules."""
+    if IP in packet:
+        src = packet[IP].src
+        dest = packet[IP].dst
+
+        # Block traffic to/from restricted IPs
+        if check_rule(src) or check_rule(dest):
+            log_action(f"Blocked {src} -> {dest}")
+            return
+
+        # Check payload for blocked domains
+        if TCP in packet or UDP in packet:
+            content = bytes(packet[TCP].payload).decode(errors="ignore")
+            for item in blocked:
+                if item in content:
+                    log_action(f"Prevented access to {item} from {src}")
+                    return
+
+def restrict(item):
+    """Block an IP address using system commands."""
+    try:
+        if os.name == "nt":
+            os.system(f"netsh advfirewall firewall add rule name='Restricted' dir=in action=block remoteip={item}")
+        else:
+            os.system(f"iptables -A INPUT -s {item} -j DROP")
+        log_action(f"Restricted {item}")
+    except Exception as e:
+        log_action(f"Failed to restrict {item}: {e}")
+
+def monitor():
+    """Start packet sniffing."""
+    log_action("System initialized.")
+    sniff(filter="ip", prn=analyze, store=0)
+
+if __name__ == "__main__":
+    # Run the monitor in a separate thread
+    monitor_thread = threading.Thread(target=monitor)
+    monitor_thread.start()
+    log_action("Monitoring started in a separate thread.")

identity_verification.py

@@ -0,0 +1,93 @@
+import hashlib
+import os
+import pyotp
+import face_recognition
+from cryptography.fernet import Fernet
+import logging
+
+# Security Configuration
+SALT = os.urandom(32)
+FERNET_KEY = Fernet.generate_key()
+fernet = Fernet(FERNET_KEY)
+SECRET_KEY = pyotp.random_base32()
+
+# Logging setup
+logging.basicConfig(filename="security.log", level=logging.INFO,
+                    format="%(asctime)s - %(levelname)s - %(message)s")
+
+# Secure User Database (Encrypted)
+user_data = {}
+
+# Utility Functions
+def hash_password(password: str) -> str:
+    """Hash the password using SHA-256 with a unique salt."""
+    salted_password = password.encode() + SALT
+    return hashlib.sha256(salted_password).hexdigest()
+
+def encrypt_data(data: str) -> str:
+    """Encrypt sensitive data using AES-256 (Fernet)."""
+    return fernet.encrypt(data.encode()).decode()
+
+def decrypt_data(encrypted_data: str) -> str:
+    """Decrypt sensitive data using AES-256 (Fernet)."""
+    return fernet.decrypt(encrypted_data.encode()).decode()
+
+# User Registration
+def register_user(username: str, password: str):
+    """Register a user with hashed password and encrypted storage."""
+    if username in user_data:
+        logging.warning(f"User '{username}' already exists.")
+        return "[Error] User already exists."
+    hashed_password = hash_password(password)
+    user_data[username] = {"password": encrypt_data(hashed_password), "2FA": None}
+    logging.info(f"User '{username}' registered securely.")
+    return f"[Success] User '{username}' registered securely."
+
+# Two-Factor Authentication (2FA) Setup
+def setup_2fa(username: str):
+    """Generate and store 2FA secret key for the user."""
+    if username not in user_data:
+        logging.warning(f"User '{username}' not found.")
+        return "[Error] User not found."
+    user_data[username]["2FA"] = SECRET_KEY
+    logging.info(f"2FA setup for user '{username}'.")
+    return f"[2FA] Scan this OTP Key in your Authenticator: {SECRET_KEY}"
+
+# Login with Identity Verification
+def login(username: str, password: str, otp_code: str):
+    """Verify user identity using password and 2FA."""
+    if username not in user_data:
+        logging.warning(f"User '{username}' not found.")
+        return "[Error] User not found."
+
+    # Verify Password
+    stored_password = decrypt_data(user_data[username]["password"])
+    if stored_password != hash_password(password):
+        logging.warning(f"Invalid password for user '{username}'.")
+        return "[Error] Invalid password."
+
+    # Verify 2FA (Time-based OTP)
+    totp = pyotp.TOTP(user_data[username]["2FA"])
+    if not totp.verify(otp_code):
+        logging.warning(f"Invalid OTP for user '{username}'.")
+        return "[Error] Invalid OTP."
+
+    logging.info(f"User '{username}' logged in securely.")
+    return f"[Success] User '{username}' logged in securely."
+
+# Biometric Face Recognition
+def verify_face():
+    """Verify user face against saved authorized face."""
+    try:
+        known_image = face_recognition.load_image_file("authorized_face.jpg")
+        unknown_image = face_recognition.load_image_file("attempt.jpg")
+
+        known_encoding = face_recognition.face_encodings(known_image)[0]
+        unknown_encoding = face_recognition.face_encodings(unknown_image)[0]
+
+        result = face_recognition.compare_faces([known_encoding], unknown_encoding)[0]
+        logging.info(f"Face verification result: {result}")
+        return result
+    except Exception as e:
+        logging.error(f"Face verification failed: {e}")
+        return False

inference.py

@@ -0,0 +1,121 @@
+import onnxruntime as ort
+import numpy as np
+import torch
+import torchaudio
+from PIL import Image
+from transformers import CLIPProcessor, CLIPModel, AutoTokenizer, AutoModelForCausalLM
+from typing import Union, List, Dict, Any
+import logging
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class InferenceEngine:
+    def __init__(self, model_path: str, device: str = "cuda" if torch.cuda.is_available() else "cpu"):
+        """
+        Initialize the InferenceEngine.
+
+        Args:
+            model_path (str): Path to the ONNX model.
+            device (str): Device to run the model on ("cuda" or "cpu").
+        """
+        self.device = device
+        try:
+            # Initialize ONNX runtime session
+            self.session = ort.InferenceSession(
+                model_path,
+                providers=[
+                    "TensorrtExecutionProvider",
+                    "CUDAExecutionProvider",
+                    "CPUExecutionProvider"
+                ]
+            )
+            logger.info(f"ONNX model loaded successfully on device: {self.device}")
+        except Exception as e:
+            logger.error(f"Failed to load ONNX model: {e}")
+            raise
+
+    def run_text_inference(self, model: AutoModelForCausalLM, tokenizer: AutoTokenizer, prompt: str, max_length: int = 200) -> str:
+        """
+        Run text inference using a causal language model.
+
+        Args:
+            model (AutoModelForCausalLM): Pre-trained causal language model.
+            tokenizer (AutoTokenizer): Tokenizer for the model.
+            prompt (str): Input text prompt.
+            max_length (int): Maximum length of the generated text.
+
+        Returns:
+            str: Generated text.
+        """
+        try:
+            inputs = tokenizer(prompt, return_tensors="pt").to(self.device)
+            outputs = model.generate(**inputs, max_length=max_length)
+            return tokenizer.decode(outputs[0], skip_special_tokens=True)
+        except Exception as e:
+            logger.error(f"Text inference failed: {e}")
+            raise
+
+    def run_image_inference(self, clip_model: CLIPModel, processor: CLIPProcessor, image_path: str) -> np.ndarray:
+        """
+        Run image inference using a CLIP model.
+
+        Args:
+            clip_model (CLIPModel): Pre-trained CLIP model.
+            processor (CLIPProcessor): Processor for the CLIP model.
+            image_path (str): Path to the input image.
+
+        Returns:
+            np.ndarray: Image features as a numpy array.
+        """
+        try:
+            image = Image.open(image_path).convert("RGB")
+            inputs = processor(images=image, return_tensors="pt").to(self.device)
+            outputs = clip_model.get_image_features(**inputs)
+            return outputs.cpu().detach().numpy()
+        except Exception as e:
+            logger.error(f"Image inference failed: {e}")
+            raise
+
+    def run_audio_inference(self, whisper_model: Any, audio_file: str) -> str:
+        """
+        Run audio inference using a Whisper model.
+
+        Args:
+            whisper_model (Any): Pre-trained Whisper model.
+            audio_file (str): Path to the input audio file.
+
+        Returns:
+            str: Transcribed text.
+        """
+        try:
+            waveform, sample_rate = torchaudio.load(audio_file)
+            waveform = waveform.to(self.device)
+            return whisper_model.transcribe(waveform)["text"]
+        except Exception as e:
+            logger.error(f"Audio inference failed: {e}")
+            raise
+
+    def run_general_inference(self, input_data: Union[np.ndarray, List, Dict]) -> np.ndarray:
+        """
+        Run general inference using the ONNX model.
+
+        Args:
+            input_data (Union[np.ndarray, List, Dict]): Input data for the model.
+
+        Returns:
+            np.ndarray: Model output.
+        """
+        try:
+            input_name = self.session.get_inputs()[0].name
+            output_name = self.session.get_outputs()[0].name
+
+            # Ensure input_data is a numpy array
+            if not isinstance(input_data, np.ndarray):
+                input_data = np.array(input_data, dtype=np.float32)
+
+            return self.session.run([output_name], {input_name: input_data})[0]
+        except Exception as e:
+            logger.error(f"General inference failed: {e}")
+            raise

memory_management.py

@@ -0,0 +1,81 @@
+import gc
+import psutil
+import numpy as np
+import torch
+import logging
+
+class MemoryManager:
+    def __init__(self):
+        self.allocated_memory = 0
+        self.max_memory_limit = psutil.virtual_memory().total * 0.8  # 80% of total RAM
+        logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+
+    def optimize_allocation(self):
+        """Free up unused memory."""
+        gc.collect()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        logging.info("Memory optimization completed.")
+
+    def allocate_memory(self, size, dtype=np.float32):
+        """Allocate memory for a given size and data type."""
+        if self.allocated_memory + size > self.max_memory_limit:
+            logging.warning("Memory limit exceeded. Optimizing memory...")
+            self.optimize_allocation()
+        self.allocated_memory += size
+        logging.info(f"Allocated {size} bytes of memory. Total allocated: {self.allocated_memory} bytes.")
+        return np.zeros(size, dtype=dtype)
+
+    def deallocate_memory(self, obj):
+        """Deallocate memory for a given object."""
+        del obj
+        self.optimize_allocation()
+        logging.info("Object deallocated and memory optimized.")
+
+    def monitor_usage(self):
+        """Monitor system memory usage and optimize if necessary."""
+        usage = psutil.virtual_memory().percent
+        if usage > 85:
+            logging.warning(f"High memory usage detected: {usage}%. Optimizing memory...")
+            self.optimize_allocation()
+
+    def adaptive_caching(self, model):
+        """Disable gradients for large models to reduce memory usage."""
+        model_size = sum(p.numel() for p in model.parameters() if p.requires_grad)
+        if model_size * 4 > self.max_memory_limit:  # 4 bytes per float32
+            for param in model.parameters():
+                param.requires_grad = False
+            logging.info("Adaptive caching applied: Gradients disabled for large model.")
+        return model
+
+    def __enter__(self):
+        """Context manager entry point."""
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        """Context manager exit point."""
+        self.optimize_allocation()
+        logging.info("MemoryManager context exited. Memory optimized.")
+
+# Example usage
+if __name__ == "__main__":
+    memory_manager = MemoryManager()
+
+    # Allocate memory
+    array = memory_manager.allocate_memory(1000000)  # 1 million elements
+    print(array.shape)
+
+    # Deallocate memory
+    memory_manager.deallocate_memory(array)
+
+    # Monitor usage
+    memory_manager.monitor_usage()
+
+    # Adaptive caching for a model
+    model = torch.nn.Linear(1000, 1000)
+    model = memory_manager.adaptive_caching(model)
+
+    # Using context manager
+    with MemoryManager() as mm:
+        array = mm.allocate_memory(1000000)
+        print(array.shape)

reinforcement_learning.py

@@ -0,0 +1,176 @@
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import random
+from collections import deque
+import heapq
+
+# Neural Network for Deep Q-Learning
+class QNetwork(nn.Module):
+    def __init__(self, state_size, action_size, hidden_sizes=[256, 128, 64], dropout_rate=0.1):
+        super(QNetwork, self).__init__()
+        self.state_size = state_size
+        self.action_size = action_size
+        
+        # Build a deeper network with configurable hidden layers
+        layers = []
+        prev_size = state_size
+        for hidden_size in hidden_sizes:
+            layers.append(nn.Linear(prev_size, hidden_size))
+            layers.append(nn.BatchNorm1d(hidden_size))  # Add batch normalization
+            layers.append(nn.ReLU())
+            layers.append(nn.Dropout(dropout_rate))     # Add dropout for regularization
+            prev_size = hidden_size
+        layers.append(nn.Linear(prev_size, action_size))
+        
+        self.network = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.network(x)
+
+# Prioritized Experience Replay Memory (simplified)
+class PriorityReplayMemory:
+    def __init__(self, capacity, alpha=0.6, beta=0.4, beta_increment=0.001):
+        self.capacity = capacity
+        self.alpha = alpha  # Priority exponent
+        self.beta = beta    # Importance sampling weight
+        self.beta_increment = beta_increment
+        self.memory = []    # Heap for priorities
+        self.experiences = deque(maxlen=capacity)  # Store experiences
+        self.max_priority = 1.0
+
+    def add(self, experience, error=None):
+        priority = error if error is not None else self.max_priority
+        priority = (abs(priority) + 1e-5) ** self.alpha  # Small constant to avoid zero priority
+        heapq.heappush(self.memory, (-priority, len(self.experiences)))  # Negative for max heap
+        self.experiences.append(experience)
+
+    def sample(self, batch_size):
+        if len(self.experiences) < batch_size:
+            return None, None, None
+        
+        # Calculate sampling probabilities
+        priorities = np.array([-p for p, _ in self.memory[:len(self.experiences)]])
+        probs = priorities / priorities.sum()
+        
+        # Sample indices
+        indices = np.random.choice(len(self.experiences), batch_size, p=probs, replace=False)
+        samples = [self.experiences[idx] for idx in indices]
+        
+        # Importance sampling weights
+        weights = (len(self.experiences) * probs[indices]) ** (-self.beta)
+        weights /= weights.max()  # Normalize
+        
+        self.beta = min(1.0, self.beta + self.beta_increment)  # Anneal beta
+        return samples, indices, torch.FloatTensor(weights)
+
+    def update_priorities(self, indices, errors):
+        for idx, error in zip(indices, errors):
+            priority = (abs(error) + 1e-5) ** self.alpha
+            self.memory[idx] = (-priority, self.memory[idx][1])
+            self.max_priority = max(self.max_priority, priority)
+        heapq.heapify(self.memory)  # Re-heapify after updates
+
+    def __len__(self):
+        return len(self.experiences)
+
+# Enhanced Reinforcement Learning Agent
+class RLAgent:
+    def __init__(self, state_size, action_size, 
+                 lr=0.0005, gamma=0.99, epsilon=1.0, epsilon_decay=0.995, min_epsilon=0.01,
+                 memory_capacity=10000, batch_size=64, target_update_freq=1000,
+                 use_double_dqn=True, clip_grad_norm=1.0):
+        self.state_size = state_size
+        self.action_size = action_size
+        self.gamma = gamma
+        self.epsilon = epsilon
+        self.epsilon_decay = epsilon_decay
+        self.min_epsilon = min_epsilon
+        self.batch_size = batch_size
+        self.use_double_dqn = use_double_dqn
+        self.clip_grad_norm = clip_grad_norm
+        
+        # Networks
+        self.policy_net = QNetwork(state_size, action_size)
+        self.target_net = QNetwork(state_size, action_size)
+        self.target_net.load_state_dict(self.policy_net.state_dict())  # Copy weights
+        self.target_net.eval()  # Target network doesn't train
+        self.optimizer = optim.Adam(self.policy_net.parameters(), lr=lr)
+        self.criterion = nn.SmoothL1Loss()  # Huber loss for stability
+        
+        # Memory
+        self.memory = PriorityReplayMemory(memory_capacity)
+        self.steps = 0
+        self.target_update_freq = target_update_freq
+
+    def remember(self, state, action, reward, next_state, done):
+        # Initial error estimate (could be refined with TD error later)
+        state_tensor = torch.FloatTensor(state).unsqueeze(0)
+        next_state_tensor = torch.FloatTensor(next_state).unsqueeze(0)
+        with torch.no_grad():
+            q_value = self.policy_net(state_tensor)[action]
+            next_q = self.target_net(next_state_tensor).max().item()
+            target = reward + (1 - done) * self.gamma * next_q
+            error = abs(q_value.item() - target)
+        self.memory.add((state, action, reward, next_state, done), error)
+
+    def act(self, state):
+        self.steps += 1
+        if random.random() < self.epsilon:
+            return random.randint(0, self.action_size - 1)
+        state = torch.FloatTensor(state).unsqueeze(0)
+        with torch.no_grad():
+            return torch.argmax(self.policy_net(state)).item()
+
+    def train(self):
+        if len(self.memory) < self.batch_size:
+            return
+        
+        # Sample from memory
+        batch, indices, weights = self.memory.sample(self.batch_size)
+        if batch is None:
+            return
+        
+        states, actions, rewards, next_states, dones = zip(*batch)
+        states = torch.FloatTensor(states)
+        actions = torch.LongTensor(actions)
+        rewards = torch.FloatTensor(rewards)
+        next_states = torch.FloatTensor(next_states)
+        dones = torch.FloatTensor(dones)
+        weights = weights.unsqueeze(1)
+        
+        # Compute Q-values
+        q_values = self.policy_net(states).gather(1, actions.unsqueeze(1)).squeeze(1)
+        
+        # Double DQN or standard DQN
+        if self.use_double_dqn:
+            next_actions = self.policy_net(next_states).argmax(1)
+            next_q_values = self.target_net(next_states).gather(1, next_actions.unsqueeze(1)).squeeze(1)
+        else:
+            next_q_values = self.target_net(next_states).max(1)[0]
+        
+        # Compute targets
+        targets = rewards + self.gamma * next_q_values * (1 - dones)
+        
+        # Compute TD errors for priority update
+        td_errors = (q_values - targets).detach().cpu().numpy()
+        
+        # Loss with importance sampling weights
+        loss = (self.criterion(q_values, targets) * weights).mean()
+        
+        # Optimize
+        self.optimizer.zero_grad()
+        loss.backward()
+        torch.nn.utils.clip_grad_norm_(self.policy_net.parameters(), self.clip_grad_norm)
+        self.optimizer.step()
+        
+        # Update priorities
+        self.memory.update_priorities(indices, td_errors)
+        
+        # Update target network
+        if self.steps % self.target_update_freq == 0:
+            self.target_net.load_state_dict(self.policy_net.state_dict())
+        
+        # Decay epsilon
+        self.epsilon = max(self.min_epsilon, self.epsilon * self.epsilon_decay)

vision_processing.py

@@ -0,0 +1,102 @@
+import torch
+import torch.nn as nn
+import torchvision.transforms as transforms
+from torchvision import models
+from PIL import Image
+import logging
+import os
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class VisionProcessingModel(nn.Module):
+    def __init__(self, model_name="resnet50", num_classes=1000, top_k=5):
+        super(VisionProcessingModel, self).__init__()
+        
+        # Load pre-trained model
+        self.model = self._load_pretrained_model(model_name, num_classes)
+        self.model.eval()  # Set the model to evaluation mode
+
+        # Automatically detect device (GPU/CPU)
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.model.to(self.device)
+        logger.info(f"Model loaded on device: {self.device}")
+
+        # Number of top predictions to return
+        self.top_k = top_k
+
+        # Image transformations
+        self.transform = transforms.Compose([
+            transforms.Resize(256),
+            transforms.CenterCrop(224),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+        ])
+
+    def _load_pretrained_model(self, model_name, num_classes):
+        """Helper function to load a pre-trained model."""
+        if model_name == "resnet50":
+            return models.resnet50(pretrained=True)
+        elif model_name == "efficientnet_b0":
+            return models.efficientnet_b0(pretrained=True)
+        elif model_name == "mobilenet_v2":
+            return models.mobilenet_v2(pretrained=True)
+        else:
+            raise ValueError(f"Unsupported model: {model_name}")
+
+    def forward(self, image):
+        """Forward pass through the model."""
+        image = image.to(self.device)
+        with torch.no_grad():
+            outputs = self.model(image)
+        return outputs
+
+    def process_image(self, image_path):
+        """Process an image and get predictions."""
+        try:
+            # Load image
+            image = Image.open(image_path).convert('RGB')
+
+            # Apply transformations and add batch dimension
+            image = self.transform(image).unsqueeze(0)
+
+            # Get predictions from model
+            outputs = self.forward(image)
+
+            # Convert raw output to probabilities (Softmax)
+            probabilities = torch.nn.functional.softmax(outputs[0], dim=0)
+
+            # Get top-k categories
+            top_k_prob, top_k_catid = torch.topk(probabilities, self.top_k)
+
+            return top_k_prob, top_k_catid
+        except Exception as e:
+            logger.error(f"Error processing image: {e}")
+            return None, None
+
+    def get_category_labels(self, category_ids):
+        """Map category IDs to human-readable labels."""
+        # Load ImageNet class labels
+        labels_path = os.getenv("IMAGENET_LABELS_PATH", "path/to/imagenet_labels.txt")
+        with open(labels_path, "r") as f:
+            labels = [line.strip() for line in f.readlines()]
+
+        # Map category IDs to labels
+        return [labels[cat_id] for cat_id in category_ids]
+
+    def enhance_vision_processing(self, image_path):
+        """Enhance vision capabilities by extracting top-k predictions."""
+        top_k_prob, top_k_catid = self.process_image(image_path)
+
+        if top_k_prob is not None and top_k_catid is not None:
+            # Convert tensors to lists
+            top_k_prob = top_k_prob.tolist()
+            top_k_catid = top_k_catid.tolist()
+
+            # Get human-readable labels
+            category_labels = self.get_category_labels(top_k_catid)
+
+            return top_k_prob, top_k_catid, category_labels
+        else:
+            return None, None, None

web-search.py

@@ -0,0 +1,92 @@
+import os
+import logging
+import numpy as np
+import firebase_admin
+from firebase_admin import credentials, firestore
+from sentence_transformers import SentenceTransformer
+from flask import Flask, request, jsonify
+from flask_limiter import Limiter
+from flask_limiter.util import get_remote_address
+import faiss  # For efficient similarity search
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Initialize Firebase Admin SDK
+firebase_cred_path = os.getenv("FIREBASE_CRED_PATH", "path/to/your/firebase/credentials.json")
+cred = credentials.Certificate(firebase_cred_path)
+firebase_admin.initialize_app(cred)
+db = firestore.client()
+
+# Initialize the model for embeddings
+model = SentenceTransformer('all-mpnet-base-v2')  # Better model for semantic search
+
+# Initialize FAISS index for efficient similarity search
+embedding_size = 768  # Size of embeddings from 'all-mpnet-base-v2'
+faiss_index = faiss.IndexFlatIP(embedding_size)  # Inner product for cosine similarity
+document_ids = []  # To map FAISS index to document IDs
+
+# Initialize Flask app
+app = Flask(__name__)
+
+# Rate limiting
+limiter = Limiter(
+    app=app,
+    key_func=get_remote_address,
+    default_limits=["200 per day", "50 per hour"]
+)
+
+# Function to store documents in Firebase and update FAISS index
+def store_documents(documents):
+    try:
+        batch = db.batch()
+        embeddings = model.encode(documents)  # Encode all documents at once
+
+        for idx, (doc, embedding) in enumerate(zip(documents, embeddings)):
+            doc_ref = db.collection('documents').document(f'doc_{idx}')
+            batch.set(doc_ref, {
+                'text': doc,
+                'embedding': embedding.tolist()
+            })
+            document_ids.append(doc_ref.id)  # Store document ID
+            faiss_index.add(np.array([embedding]))  # Add embedding to FAISS index
+
+        batch.commit()
+        logger.info(f"Stored {len(documents)} documents in Firebase and updated FAISS index.")
+    except Exception as e:
+        logger.error(f"Error storing documents: {e}")
+
+# Function to search documents using FAISS
+def search_documents(query):
+    try:
+        query_embedding = model.encode(query).reshape(1, -1)
+        distances, indices = faiss_index.search(query_embedding, k=1)  # Find top-1 match
+
+        if indices[0][0] == -1:
+            return None  # No match found
+
+        # Retrieve the best matching document from Firebase
+        best_doc_id = document_ids[indices[0][0]]
+        best_doc = db.collection('documents').document(best_doc_id).get()
+        return best_doc.to_dict()
+    except Exception as e:
+        logger.error(f"Error searching documents: {e}")
+        return None
+
+@app.route('/search', methods=['GET'])
+@limiter.limit("10 per minute")  # Rate limit for search endpoint
+def search():
+    query = request.args.get('query')
+    if not query:
+        return jsonify({"error": "No query provided."}), 400
+
+    result = search_documents(query)
+    if result:
+        return jsonify(result)
+    else:
+        return jsonify({"error": "No relevant documents found."}), 404
+
+if __name__ == '__main__':
+    # Run Flask app for the AI search
+    app.run(debug=True)