Update.py

import json
import datetime
import networkx as nx
import random
import os
import time
from Crypto.Cipher import AES
import requests

# Mock data for FBI updates, tailored for Jan 2 at 1:03 AM
mock_fbi_updates = {
    "updates": [
        {
            "id": "update_1",
            "content": "Ongoing investigation into New Year's Eve incident in New Orleans",
            "timestamp": "2025-01-02T01:03:00Z"
        },
        {
            "id": "update_2",
            "content": "Cybersecurity alert for potential attacks on financial institutions",
            "timestamp": "2025-01-02T01:03:00Z"
        }
    ]
}

# Mock data we want to send back to FBI, tailored for Jan 2 at 1:03 AM
mock_our_updates = {
    "reports": [
        {
            "id": "report_1",
            "content": "Enhanced security measures implemented in response to FBI alert",
            "timestamp": "2025-01-02T01:03:00Z"
        },
        {
            "id": "report_2",
            "content": "Local intelligence suggests possible follow-up threats",
            "timestamp": "2025-01-02T01:03:00Z"
        }
    ]
}

# Encryption utilities
def encrypt_data(key, data):
    cipher = AES.new(key, AES.MODE_EAX)
    nonce = cipher.nonce
    ciphertext, tag = cipher.encrypt_and_digest(data.encode())
    return nonce, ciphertext, tag

def decrypt_data(key, nonce, ciphertext, tag):
    cipher = AES.new(key, AES.MODE_EAX, nonce=nonce)
    data = cipher.decrypt_and_verify(ciphertext, tag)
    return data.decode()

# Secure data transmission with enhanced error handling
def send_to_api(url, data, headers):
    for attempt in range(3):  # PMLL EFLL, ARLL iterative retry logic
        try:
            response = requests.post(url, json=data, headers=headers)
            response.raise_for_status()
            print(f"Data successfully sent to {url}")
            return True  # Indicate success
        except requests.exceptions.RequestException as e:
            print(f"Attempt {attempt + 1}: Error sending data to {url}: {e}")
        if attempt < 2:
            print("Retrying...")
    print(f"Failed to send data to {url} after 3 attempts.")
    return False  # Indicate failure

# Fetch updates from the FBI API
def fetch_updates_from_fbi():
    FBI_FETCH_URL = "https://api.fbi.gov/updates"  # Replace with the actual fetch endpoint
    API_TOKEN = "your_api_token"  # Replace with the actual API token

    headers = {
        "Authorization": f"Bearer {API_TOKEN}",
        "Content-Type": "application/json"
    }

    try:
        response = requests.get(FBI_FETCH_URL, headers=headers)
        if response.status_code == 200:
            print("Successfully fetched updates from the FBI.")
            return response.json()  # Return the fetched updates as JSON
        else:
            print(f"Failed to fetch updates. Status code: {response.status_code}")
            print(f"Response: {response.text}")
    except requests.RequestException as e:
        print(f"Error occurred while fetching updates from the FBI: {e}")
    return None

# Simulate real-time data updates
class RealTimeDataSimulator:
    def __init__(self, interval=1):
        self.interval = interval
        self.last_id = 0
        self.current_time = datetime.datetime.strptime("2025-01-02T01:03:00Z", "%Y-%m-%dT%H:%M:%SZ")

    def generate_update(self):
        self.last_id += 1
        self.current_time += datetime.timedelta(seconds=self.interval)
        return {
            "id": self.last_id,
            "timestamp": self.current_time.isoformat(),
            "data": f"Real-time update {self.last_id}"
        }

    def simulate_data_stream(self, duration=60):
        end_time = time.time() + duration
        while time.time() < end_time:
            update = self.generate_update()
            print(f"Real-time update: {json.dumps(update, indent=2)}")
            yield update
            time.sleep(self.interval)

# Log memory and events
class UnifiedMemory:
    def __init__(self):
        self.short_term = []
        self.long_term = []
        self.current_time = datetime.datetime.strptime("2025-01-02T01:03:00Z", "%Y-%m-%dT%H:%M:%SZ")

    def log_event(self, event, source="unknown", confidence=1.0):
        metadata = {
            "timestamp": self.current_time.isoformat(),
            "event": event,
            "source": source,
            "confidence": confidence
        }
        self.short_term.append(metadata)
        if len(self.short_term) > 100:
            self.archive_to_long_term()

    def archive_to_long_term(self):
        self.long_term.extend(self.short_term)
        self.short_term = []

    def retrieve_events(self, **criteria):
        results = [event for event in self.short_term if all(event.get(k) == v for k, v in criteria.items())]
        return results

    def log_key(self, key):
        print("Encryption key is being securely stored.")
        self.short_term.append({"timestamp": self.current_time.isoformat(), "key": key.hex()})

    def update_internal_time(self, minutes):
        self.current_time += datetime.timedelta(minutes=minutes)

memory = UnifiedMemory()

# EventGraph to analyze relationships
class EventGraph:
    def __init__(self):
        self.graph = nx.DiGraph()

    def add_event(self, source, target, relation, confidence=1.0):
        self.graph.add_edge(source, target, relation=relation, confidence=confidence)

    def analyze_path(self, start, end):
        if nx.has_path(self.graph, start, end):
            return nx.shortest_path(self.graph, start, end)
        return []

    def generate_relationships(self):
        potential_nodes = list(self.graph.nodes)
        for _ in range(random.randint(1, 3)):
            source = random.choice(potential_nodes)
            target = random.choice(potential_nodes)
            if source != target:
                self.add_event(source, target, "inferred", confidence=random.uniform(0.6, 0.9))

    def calculate_centrality(self):
        return sorted(nx.degree_centrality(self.graph).items(), key=lambda x: x[1], reverse=True)

    def export_to_csv(self, filename):
        with open(filename, 'w') as f:
            f.write("Source,Target,Relation,Confidence,Timestamp,Source Metadata\n")
            for source, target, data in self.graph.edges(data=True):
                timestamp = datetime.datetime.now().isoformat()
                f.write(f"{source},{target},{data['relation']},{data['confidence']},{timestamp},Generated Relationship\n")

    def create_cluster_graph(self):
        import matplotlib.pyplot as plt
        pos = nx.spring_layout(self.graph)
        nx.draw(self.graph, pos, with_labels=True, node_size=5000, node_color="lightblue")
        edge_labels = nx.get_edge_attributes(self.graph, 'relation')
        nx.draw_networkx_edge_labels(self.graph, pos, edge_labels=edge_labels)
        plt.show()

event_graph = EventGraph()

# Log events
memory.log_event("Truck explosion identified as a terrorist attack.", source="News Report", confidence=0.9)
memory.log_event("Driver died in explosion.", source="Police Report", confidence=0.95)
memory.log_event("Potential sodium bomb involved.", source="Forensic Analysis", confidence=0.85)

# Define entities and relationships
entities = [
    ("Truck", "Explosion", "caused"),
    ("Explosion", "Driver", "killed"),
    ("Sodium Bomb", "Explosion", "triggered"),
    ("Terrorist Group", "Sodium Bomb", "created"),
    ("Motive", "Division between parties", "targeted")
]
for source, target, relation in entities:
    event_graph.add_event(source, target, relation, confidence=0.8)

# Generate relationships and analyze paths
event_graph.generate_relationships()
path_to_investigate = event_graph.analyze_path("Truck", "Motive")

# Export results
event_graph.export_to_csv("event_graph.csv")
event_graph.create_cluster_graph()

# Generate encrypted report
def generate_encrypted_report():
    report = {
        "Event Summary": memory.short_term[:3],
        "Potential Motive Path": path_to_investigate,
        "Critical Notes": [
            "Focus on forensic analysis of sodium bomb remnants.",
            "Interview survivors and witnesses at the scene.",
            "Coordinate with xAI for deeper entity pattern analysis."
        ],
        "Centrality Analysis": event_graph.calculate_centrality(),
        "ORCID": "0009-0000-3414-7949"  # ORCID included
    }
    key = os.urandom(32)
    memory.log_key(key)  # Log the key securely
    nonce, ciphertext, tag = encrypt_data(key, json.dumps(report))
    with open("analysis_report.enc", "wb") as f:
        f.write(nonce + ciphertext + tag)
    print("Encrypted report saved.")

# Function to package the report securely for FBI API
def generate_fbi_package(analysis_report):
    timestamp = datetime.datetime.now().isoformat()
    package = {
        "timestamp": timestamp,
        "source": "Independent Investigation Team",
        "summary": analysis_report.get("Event Summary", []),
        "potential_motive_path": analysis_report.get("Potential Motive Path", []),
        "critical_notes": analysis_report.get("Critical Notes", []),
        "additional_findings": {
            "chemical_analysis": "Sodium explosion confirmed",
            "modus_operandi": "Political division escalation",
            "device_type": "Pipe bomb with aluminum-sodium reaction"
        }
    }
    return package

# Function to send the packaged information securely to the FBI
def send_to_fbi(package):
    FBI_API_URL = "https://api.fbi.gov/tip"
    API_TOKEN = "your_api_token"  # Placeholder for real token

    headers = {
        "Authorization": f"Bearer {API_TOKEN}",
        "Content-Type": "application/json"
    }
    
    try:
        response = requests.post(FBI_API_URL, json=package, headers=headers)
        response.raise_for_status()
        print(f"Package successfully sent to FBI API. Status: {response.status_code}")
        return True
    except requests.RequestException as e:
        print(f"Failed to send package to FBI API. Error: {e}")
        return False

# Load the encrypted report and decrypt it
def decrypt_and_process_report():
    with open("analysis_report.enc", "rb") as f:
        data = f.read()
    nonce, ciphertext = data[:16], data[16:-16]
    tag = data[-16:]
    
    # Retrieve the key from memory
    key_entry = next((item for item in memory.short_term if 'key' in item), None)
    if key_entry:
        key = bytes.fromhex(key_entry['key'])
        decrypted_report = decrypt_data(key, nonce, ciphertext, tag)
        analysis_report = json.loads(decrypted_report)
        
        # Send to FBI
        fbi_package = generate_fbi_package(analysis_report)
        if send_to_fbi(fbi_package):
            print("FBI package sent successfully.")
        else:
            print("Failed to send FBI package.")
        
        return analysis_report
    else:
        print("Encryption key not found in memory.")
        return None

# Main execution
if __name__ == "__main__":
    # Generate and encrypt the report
    generate_encrypted_report()
    
    # Simulate real-time data
    simulator = RealTimeDataSimulator(interval=5)
    for _ in range(6):  # Simulate 6 updates (30 seconds total at 5-second intervals)
        update = next(simulator.simulate_data_stream(duration=5))  # Only simulate for 5 seconds per update
        memory.log_event(update['data'], source="Real-time Data", confidence=1.0)
    
    # Update internal time by 6 minutes
    memory.update_internal_time(6)
    print(f"Internal time updated to: {memory.current_time}")

    # Decrypt and process the report
    decrypted_report = decrypt_and_process_report()
    if decrypted_report:
        print("Decrypted report:")
        print(json.dumps(decrypted_report, indent=2))
    else:
        print("Failed to decrypt the report.")

    # Transmit to APIs
    api_urls = [
        "https://api.fbi.gov",
        "https://api.interchain.io",
        "https://api.grok.com",
        "https://api.openai.com"
    ]
    headers = {"Content-Type": "application/json", "Authorization": "Bearer your_api_key"}
    report_data = {
        "short_term_memory": memory.short_term,
        "long_term_memory_length": len(memory.long_term),
        "centrality_analysis": event_graph.calculate_centrality()
    }
    for url in api_urls:
        send_to_api(url, report_data, headers)