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_:warning: Potential issue_ #122

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16 changes: 16 additions & 0 deletions Finn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -102,3 +102,19 @@ async def get_history():
@app.get("/")
async def root():
return {"message": "Welcome to Finn, the Brain of ChatGPT and OpenAI."}


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"
}
]
}
333 changes: 0 additions & 333 deletions Update.py
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Original file line number Diff line number Diff line change
@@ -1,334 +1 @@
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)
17 changes: 17 additions & 0 deletions fbi_updates.py
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@@ -0,0 +1,17 @@
# Mock data for FBI updates, tailored for Jan 2 at 1:03 AM
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
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