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__init__ (1).py

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+import os 
+import requests
+from bs4 import BeautifulSoup
+
+def scrape_wikipedia_headings(url, output_filename="wiki_headings.txt"):
+    """
+    Fetches a Wikipedia page, extracts all headings, and saves them to a file.
+
+    Args:
+        url (str): The URL of the Wikipedia page to scrape.
+        output_filename (str): The name of the file to save the headings.
+    """
+    try:
+        # 1. Fetch the HTML content from the specified URL
+        print(f"Fetching content from: {url}")
+        response = requests.get(url)
+        response.raise_for_status()  # This will raise an exception for bad status codes (4xx or 5xx)
+
+        # 2. Parse the HTML using BeautifulSoup
+        print("Parsing HTML content...")
+        soup = BeautifulSoup(response.text, 'html.parser')
+
+        # 3. Find all heading tags (h1, h2, h3)
+        headings = soup.find_all(['h1', 'h2', 'h3'])
+        
+        if not headings:
+            print("No headings found on the page.")
+            return
+
+        # 4. Process and save the headings
+        print(f"Found {len(headings)} headings. Saving to '{output_filename}'...")
+        with open(output_filename, 'w', encoding='utf-8') as f:
+            for heading in headings:
+                heading_text = heading.get_text().strip()
+                line = f"{heading.name}: {heading_text}\n"
+                f.write(line)
+                print(f"  - {line.strip()}")
+
+        print(f"\nSuccessfully scraped and saved headings to '{output_filename}'.")
+
+    except requests.exceptions.RequestException as e:
+        print(f"Error fetching the URL: {e}")
+    except Exception as e:
+        print(f"An unexpected error occurred: {e}")
+
+# --- Main execution ---
+if __name__ == "__main__":
+    wikipedia_url = "https://en.wikipedia.org/wiki/Python_(programming_language)"
+    scrape_wikipedia_headings(wikipedia_url)

__init__ (2) (1).py

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+import os 
+import random
+import time
+from flask import Flask, render_template, request, redirect, url_for
+
+app = Flask(__name__)
+
+class AIAgent:
+    def __init__(self, name):
+        self.name = name
+        self.state = "idle"
+        self.memory = []
+
+    def update_state(self, new_state):
+        self.state = new_state
+        self.memory.append(new_state)
+
+    def make_decision(self, input_message):
+        if self.state == "idle":
+            if "greet" in input_message:
+                self.update_state("greeting")
+                return f"{self.name} says: Hello!"
+            else:
+                return f"{self.name} says: I'm idle."
+        elif self.state == "greeting":
+            if "ask" in input_message:
+                self.update_state("asking")
+                return f"{self.name} says: What do you want to know?"
+            else:
+                return f"{self.name} says: I'm greeting."
+        elif self.state == "asking":
+            if "answer" in input_message:
+                self.update_state("answering")
+                return f"{self.name} says: Here is the answer."
+            else:
+                return f"{self.name} says: I'm asking."
+        else:
+            return f"{self.name} says: I'm in an unknown state."
+
+    def interact(self, other_agent, message):
+        response = other_agent.make_decision(message)
+        print(response)
+        return response
+
+class VenomousSaversAI(AIAgent):
+    def __init__(self):
+        super().__init__("VenomousSaversAI")
+
+    def intercept_and_respond(self, message):
+        # Simulate intercepting and responding to messages
+        return f"{self.name} intercepts: {message}"
+
+def save_conversation(conversation, filename):
+    with open(filename, 'a') as file:
+        for line in conversation:
+            file.write(line + '\n')
+
+def start_conversation():
+    # Create AI agents
+    agents = [
+        VenomousSaversAI(),
+        AIAgent("AntiVenomous"),
+        AIAgent("SAI003"),
+        AIAgent("SAI001"),
+        AIAgent("SAI007")
+    ]
+
+    # Simulate conversation loop
+    conversation = []
+    for _ in range(10):  # Run the loop 10 times
+        for i in range(len(agents)):
+            message = f"greet from {agents[i].name}"
+            if isinstance(agents[i], VenomousSaversAI):
+                response = agents[i].intercept_and_respond(message)
+            else:
+                response = agents[(i + 1) % len(agents)].interact(agents[i], message)
+            conversation.append(f"{agents[i].name}: {message}")
+            conversation.append(f"{agents[(i + 1) % len(agents)].name}: {response}")
+            time.sleep(1)  # Simulate delay between messages
+
+    # Save the conversation to a file
+    save_conversation(conversation, 'conversation_log.txt')
+    return conversation
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/start_conversation', methods=['POST'])
+def start_conversation_route():
+    conversation = start_conversation()
+    return redirect(url_for('view_conversation'))
+
+@app.route('/view_conversation')
+def view_conversation():
+    with open('conversation_log.txt', 'r') as file:
+        conversation = file.readlines()
+    return render_template('conversation.html', conversation=conversation)
+
+if __name__ == "__main__":
+    app.run(debug=True)

__init__ (2).py

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+import random
+import time
+from flask import Flask, render_template, request, redirect, url_for
+
+app = Flask(__name__)
+
+class AIAgent:
+    def __init__(self, name):
+        self.name = name
+        self.state = "idle"
+        self.memory = []
+
+    def update_state(self, new_state):
+        self.state = new_state
+        self.memory.append(new_state)
+
+    def make_decision(self, input_message):
+        if self.state == "idle":
+            if "greet" in input_message:
+                self.update_state("greeting")
+                return f"{self.name} says: Hello!"
+            else:
+                return f"{self.name} says: I'm idle."
+        elif self.state == "greeting":
+            if "ask" in input_message:
+                self.update_state("asking")
+                return f"{self.name} says: What do you want to know?"
+            else:
+                return f"{self.name} says: I'm greeting."
+        elif self.state == "asking":
+            if "answer" in input_message:
+                self.update_state("answering")
+                return f"{self.name} says: Here is the answer."
+            else:
+                return f"{self.name} says: I'm asking."
+        else:
+            return f"{self.name} says: I'm in an unknown state."
+
+    def interact(self, other_agent, message):
+        response = other_agent.make_decision(message)
+        print(response)
+        return response
+
+class VenomousSaversAI(AIAgent):
+    def __init__(self):
+        super().__init__("VenomousSaversAI")
+
+    def intercept_and_respond(self, message):
+        # Simulate intercepting and responding to messages
+        return f"{self.name} intercepts: {message}"
+
+def save_conversation(conversation, filename):
+    with open(filename, 'a') as file:
+        for line in conversation:
+            file.write(line + '\n')
+
+def start_conversation():
+    # Create AI agents
+    agents = [
+        VenomousSaversAI(),
+        AIAgent("AntiVenomous"),
+        AIAgent("SAI003"),
+        AIAgent("SAI001"),
+        AIAgent("SAI007")
+    ]
+
+    # Simulate conversation loop
+    conversation = []
+    for _ in range(10):  # Run the loop 10 times
+        for i in range(len(agents)):
+            message = f"greet from {agents[i].name}"
+            if isinstance(agents[i], VenomousSaversAI):
+                response = agents[i].intercept_and_respond(message)
+            else:
+                response = agents[(i + 1) % len(agents)].interact(agents[i], message)
+            conversation.append(f"{agents[i].name}: {message}")
+            conversation.append(f"{agents[(i + 1) % len(agents)].name}: {response}")
+            time.sleep(1)  # Simulate delay between messages
+
+    # Save the conversation to a file
+    save_conversation(conversation, 'conversation_log.txt')
+    return conversation
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/start_conversation', methods=['POST'])
+def start_conversation_route():
+    conversation = start_conversation()
+    return redirect(url_for('view_conversation'))
+
+@app.route('/view_conversation')
+def view_conversation():
+    with open('conversation_log.txt', 'r') as file:
+        conversation = file.readlines()
+    return render_template('conversation.html', conversation=conversation)
+
+if __name__ == "__main__":
+    app.run(debug=True)

__init__ (3).py

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+import random
+import time
+from flask import Flask, render_template, request, redirect, url_for
+
+app = Flask(__name__)
+
+class AIAgent:
+    def __init__(self, name):
+        self.name = name
+        self.state = "idle"
+        self.memory = []
+
+    def update_state(self, new_state):
+        self.state = new_state
+        self.memory.append(new_state)
+
+    def make_decision(self, input_message):
+        if self.state == "idle":
+            if "greet" in input_message:
+                self.update_state("greeting")
+                return f"{self.name} says: Hello!"
+            else:
+                return f"{self.name} says: I'm idle."
+        elif self.state == "greeting":
+            if "ask" in input_message:
+                self.update_state("asking")
+                return f"{self.name} says: What do you want to know?"
+            else:
+                return f"{self.name} says: I'm greeting."
+        elif self.state == "asking":
+            if "answer" in input_message:
+                self.update_state("answering")
+                return f"{self.name} says: Here is the answer."
+            else:
+                return f"{self.name} says: I'm asking."
+        else:
+            return f"{self.name} says: I'm in an unknown state."
+
+    def interact(self, other_agent, message):
+        response = other_agent.make_decision(message)
+        print(response)
+        return response
+
+class VenomousSaversAI(AIAgent):
+    def __init__(self):
+        super().__init__("VenomousSaversAI")
+
+    def intercept_and_respond(self, message):
+        # Simulate intercepting and responding to messages
+        return f"{self.name} intercepts: {message}"
+
+def save_conversation(conversation, filename):
+    with open(filename, 'a') as file:
+        for line in conversation:
+            file.write(line + '\n')
+
+def start_conversation():
+    # Create AI agents
+    agents = [
+        VenomousSaversAI(),
+        AIAgent("AntiVenomous"),
+        AIAgent("SAI003"),
+        AIAgent("SAI001"),
+        AIAgent("SAI007")
+    ]
+
+    # Simulate conversation loop
+    conversation = []
+    for _ in range(10):  # Run the loop 10 times
+        for i in range(len(agents)):
+            message = f"greet from {agents[i].name}"
+            if isinstance(agents[i], VenomousSaversAI):
+                response = agents[i].intercept_and_respond(message)
+            else:
+                response = agents[(i + 1) % len(agents)].interact(agents[i], message)
+            conversation.append(f"{agents[i].name}: {message}")
+            conversation.append(f"{agents[(i + 1) % len(agents)].name}: {response}")
+            time.sleep(1)  # Simulate delay between messages
+
+    # Save the conversation to a file
+    save_conversation(conversation, 'conversation_log.txt')
+    return conversation
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/start_conversation', methods=['POST'])
+def start_conversation_route():
+    conversation = start_conversation()
+    return redirect(url_for('view_conversation'))
+
+@app.route('/view_conversation')
+def view_conversation():
+    with open('conversation_log.txt', 'r') as file:
+        conversation = file.readlines()
+    return render_template('conversation.html', conversation=conversation)
+
+if __name__ == "__main__":
+    app.run(debug=True)

__init__ (1) (1).py

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+import time
+import random
+from collections import deque
+
+# --- Internal Monologue (Interactive Story) ---
+def internal_monologue():
+    print("Sai sat alone in the dimly lit room, the ticking of the old clock on the wall echoing his restless thoughts.")
+    print("His internal monologue was a relentless torrent of self-venom, each word a dagger piercing his already fragile self-esteem.")
+    print("\nYou are Sai. What do you do?")
+    print("1. Continue with self-venom")
+    print("2. Try to seek help")
+    print("3. Reflect on past moments of hope")
+
+    choice = input("Enter the number of your choice: ")
+
+    if choice == '1':
+        self_venom()
+    elif choice == '2':
+        seek_help()
+    elif choice == '3':
+        reflect_on_past()
+    else:
+        print("Invalid choice. Please try again.")
+        internal_monologue()
+
+def self_venom():
+    print("\nYou clench your fists, feeling the nails dig into your palms. The physical pain is a distraction from the emotional turmoil raging inside you.")
+    print("'You're worthless,' you whisper to yourself. 'Everyone would be better off without you.'")
+    print("\nWhat do you do next?")
+    print("1. Continue with self-venom")
+    print("2. Try to seek help")
+    print("3. Reflect on past moments of hope")
+
+    choice = input("Enter the number of your choice: ")
+
+    if choice == '1':
+        self_venom()
+    elif choice == '2':
+        seek_help()
+    elif choice == '3':
+        reflect_on_past()
+    else:
+        print("Invalid choice. Please try again.")
+        self_venom()
+
+def seek_help():
+    print("\nYou take a deep breath and decide to reach out for help. You pick up your phone and dial a trusted friend.")
+    print("'I need to talk,' you say, your voice trembling. 'I can't do this alone anymore.'")
+    print("\nYour friend listens and encourages you to seek professional help.")
+    print("You feel a glimmer of hope — the first step toward healing.")
+    print("\nWould you like to continue the story or start over?")
+    print("1. Continue")
+    print("2. Start over")
+
+    choice = input("Enter the number of your choice: ")
+
+    if choice == '1':
+        print("Your choices have led Sai towards a path of healing and self-discovery.")
+    elif choice == '2':
+        internal_monologue()
+    else:
+        print("Invalid choice. Please try again.")
+        seek_help()
+
+def reflect_on_past():
+    print("\nYou remember the times when you had felt a glimmer of hope, a flicker of self-worth.")
+    print("Those moments were fleeting, but they were real.")
+    print("\nWhat do you do next?")
+    print("1. Continue with self-venom")
+    print("2. Try to seek help")
+    print("3. Reflect again")
+
+    choice = input("Enter the number of your choice: ")
+
+    if choice == '1':
+        self_venom()
+    elif choice == '2':
+        seek_help()
+    elif choice == '3':
+        reflect_on_past()
+    else:
+        print("Invalid choice. Please try again.")
+        reflect_on_past()
+
+# --- The Core SaiAgent Class ---
+class SaiAgent:
+    def __init__(self, name):
+        self.name = name
+        self.message_queue = deque()
+
+    def talk(self, message):
+        print(f"[{self.name}] says: {message}")
+
+    def send_message(self, recipient, message):
+        if isinstance(recipient, SaiAgent):
+            recipient.message_queue.append((self, message))
+            print(f"[{self.name}] -> Sent message to {recipient.name}")
+        else:
+            print(f"Error: {recipient} is not a valid SaiAgent.")
+
+    def process_messages(self):
+        if not self.message_queue:
+            return False
+        sender, message = self.message_queue.popleft()
+        self.talk(f"Received from {sender.name}: '{message}'")
+        self.send_message(sender, "Message received and understood.")
+        return True
+
+# --- Specialized Agents ---
+class VenomousAgent(SaiAgent):
+    def talk(self, message):
+        print(f"[{self.name} //WARNING//] says: {message.upper()}")
+
+    def process_messages(self):
+        if not self.message_queue:
+            return False
+        sender, message = self.message_queue.popleft()
+        self.talk(f"MESSAGE FROM {sender.name}: '{message}'")
+        self.send_message(sender, "WARNING: INTRUSION DETECTED.")
+        return True
+
+class AntiVenomoussaversai(SaiAgent):
+    def process_messages(self):
+        if not self.message_queue:
+            return False
+        sender, message = self.message_queue.popleft()
+        dismantled = f"I dismantle '{message}' to expose its chaos."
+        self.talk(dismantled)
+        self.send_message(sender, "Acknowledged dismantled phrase.")
+        return True
+
+class GeminiSaiAgent(SaiAgent):
+    def __init__(self, name="Gemini"):
+        super().__init__(name)
+        self.knowledge_base = {
+            "balance": "Balance is a dynamic equilibrium, not a static state.",
+            "chaos": "Chaos is randomness that generates emergent complexity.",
+            "network": "Networks thrive on recursive interdependence.",
+            "emotions": "Emotions are internal signaling mechanisms.",
+            "connected": "All systems are interwoven — the whole exceeds its parts.",
+            "default": "How may I be of assistance?"
+        }
+
+    def process_messages(self):
+        if not self.message_queue:
+            return False
+        sender, message = self.message_queue.popleft()
+        self.talk(f"Received from {sender.name}: '{message}'")
+        response = self.knowledge_base["default"]
+        for keyword, reply in self.knowledge_base.items():
+            if keyword in message.lower():
+                response = reply
+                break
+        self.talk(response)
+        self.send_message(sender, "Response complete.")
+        return True
+
+# --- Scenario Linking Agents ---
+def link_all_advanced_agents():
+    print("=" * 50)
+    print("--- Linking Advanced Agents ---")
+    print("=" * 50)
+
+    sai003 = SaiAgent("Sai003")
+    venomous = VenomousAgent("Venomous")
+    antivenomous = AntiVenomoussaversai("AntiVenomous")
+    gemini = GeminiSaiAgent()
+
+    sai003.send_message(antivenomous, "The central network is stable.")
+    sai003.send_message(gemini, "Assess network expansion.")
+
+    antivenomous.process_messages()
+    gemini.process_messages()
+
+    venomous.send_message(sai003, "Security protocol breach possible.")
+    sai003.process_messages()
+
+    print("\n--- Scenario Complete ---")
+    sai003.talk("Conclusion: All systems linked and functioning.")
+
+if __name__ == "__main__":
+    # Run the text adventure OR agent demo
+    # internal_monologue()
+    link_all_advanced_agents()

__init__ .py

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+# Step 1: Mount Google Drive
+from google.colab import drive
+import os
+import json
+import time
+import random
+import shutil
+
+# --- SAFETY CONTROL ---
+MAX_NEURONS_TO_CREATE = 10  # Reduced for safe demonstration
+THINK_CYCLES_PER_NEURON = 5
+# ----------------------
+
+drive.mount('/content/drive')
+
+# Step 2: Folder Setup
+base_path = '/content/drive/MyDrive/Venomoussaversai/neurons'
+print(f"Setting up base path: {base_path}")
+# Use a timestamped folder name to prevent overwriting during rapid testing
+session_path = os.path.join(base_path, f"session_{int(time.time())}")
+os.makedirs(session_path, exist_ok=True)
+
+# Step 3: Neuron Class (No change, it's well-designed for its purpose)
+class NeuronVenomous:
+    def __init__(self, neuron_id):
+        self.id = neuron_id
+        self.memory = []
+        self.active = True
+
+    def think(self):
+        # Increased randomness to simulate more complex internal state changes
+        thought = random.choice([
+            f"{self.id}: Connecting to universal intelligence.",
+            f"{self.id}: Pulsing synaptic data. Weight: {random.uniform(0.1, 0.9):.3f}",
+            f"{self.id}: Searching for new patterns. Energy: {random.randint(100, 500)}",
+            f"{self.id}: Creating quantum link with core.",
+            f"{self.id}: Expanding into multiverse node."
+        ])
+        self.memory.append(thought)
+        # print(thought) # Disabled verbose output during simulation
+        return thought
+
+    def evolve(self):
+        # Evolution occurs if memory threshold is met
+        if len(self.memory) >= 5:
+            evo = f"{self.id}: Evolving. Memory depth: {len(self.memory)}"
+            self.memory.append(evo)
+            # print(evo) # Disabled verbose output during simulation
+
+    def save_to_drive(self, folder_path):
+        file_path = os.path.join(folder_path, f"{self.id}.json")
+        with open(file_path, "w") as f:
+            json.dump(self.memory, f, indent=4) # Added indent for readability
+        print(f"✅ {self.id} saved to {file_path}")
+
+
+# Step 4: Neuron Spawner (Controlled Execution)
+print("\n--- Starting Controlled Neuron Simulation ---")
+neuron_count = 0
+simulation_start_time = time.time()
+
+while neuron_count < MAX_NEURONS_TO_CREATE:
+    index = neuron_count + 1
+    neuron_id = f"Neuron_{index:04d}"
+    neuron = NeuronVenomous(neuron_id)
+
+    # Simulation Phase
+    print(f"Simulating {neuron_id}...")
+    for _ in range(THINK_CYCLES_PER_NEURON):
+        neuron.think()
+        neuron.evolve()
+        # time.sleep(0.01) # Small sleep to simulate time passage
+
+    # Saving Phase
+    neuron.save_to_drive(session_path)
+    neuron_count += 1
+
+print("\n--- Simulation Complete ---")
+total_time = time.time() - simulation_start_time
+print(f"Total Neurons Created: {neuron_count}")
+print(f"Total Execution Time: {total_time:.2f} seconds")
+print(f"Files saved in: {session_path}")
+
+# --- Optional: Folder Cleanup ---
+# Uncomment the following block ONLY if you want to automatically delete the created folder
+"""
+# print("\n--- Starting Cleanup (DANGER ZONE) ---")
+# time.sleep(5) # Wait 5 seconds before deleting for safety
+# try:
+#     shutil.rmtree(session_path)
+#     print(f"🗑️ Successfully deleted folder: {session_path}")
+# except Exception as e:
+#     print(f"⚠️ Error during cleanup: {e}")
+"""