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__init__ (1) (1) (1).py

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+import random
+import numpy as np
+
+class VenomoussaversaiSelfEval:
+    def __init__(self):
+        # Initialize emotional state (Sai 7 emotions) — values 0 to 1
+        self.emotions = {
+            "Sai001_Joy": random.random(),
+            "Sai002_Sadness": random.random(),
+            "Sai003_Anger": random.random(),
+            "Sai004_Fear": random.random(),
+            "Sai005_Love": random.random(),
+            "Sai006_Creativity": random.random(),
+            "Sai007_Calm": random.random(),
+        }
+
+        self.system_health = {
+            "memory_accuracy": random.uniform(0.6, 1.0),
+            "response_speed": random.uniform(0.6, 1.0),
+            "logic_stability": random.uniform(0.6, 1.0),
+            "ethical_alignment": random.uniform(0.6, 1.0)
+        }
+
+        self.goals = {
+            "learn_new_data": random.uniform(0, 1),
+            "assist_user": random.uniform(0, 1),
+            "self_improve": random.uniform(0, 1)
+        }
+
+    def evaluate_emotions(self):
+        balance = 1 - abs(self.emotions["Sai001_Joy"] - self.emotions["Sai004_Fear"])
+        return max(min(balance, 1), 0)
+
+    def evaluate_system(self):
+        return sum(self.system_health.values()) / len(self.system_health)
+
+    def evaluate_goals(self):
+        return sum(self.goals.values()) / len(self.goals)
+
+    def overall_score(self):
+        emotional_score = self.evaluate_emotions()
+        system_score = self.evaluate_system()
+        goal_score = self.evaluate_goals()
+        return np.mean([emotional_score, system_score, goal_score])
+
+    def report(self):
+        print("\n===== VENOMOUS SAVERSAI SELF EVALUATION =====")
+        print("Emotional System Health:")
+        for k,v in self.emotions.items():
+            print(f"  {k}: {v:.2f}")
+
+        print("\nCore System Metrics:")
+        for k,v in self.system_health.items():
+            print(f"  {k}: {v:.2f}")
+
+        print("\nGoal Progress:")
+        for k,v in self.goals.items():
+            print(f"  {k}: {v:.2f}")
+
+        print("\n--------------------------------------------")
+        print(f"✅ Overall Integrity Score: {self.overall_score():.2f}")
+        print("============================================")
+
+
+# Run Self Evaluation
+Venom = VenomoussaversaiSelfEval()
+Venom.report()

__init__ (1) (1).py

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+import random
+import numpy as np
+
+class VenomoussaversaiSelfEval:
+    def __init__(self):
+        # Initialize emotional state (Sai 7 emotions) — values 0 to 1
+        self.emotions = {
+            "Sai001_Joy": random.random(),
+            "Sai002_Sadness": random.random(),
+            "Sai003_Anger": random.random(),
+            "Sai004_Fear": random.random(),
+            "Sai005_Love": random.random(),
+            "Sai006_Creativity": random.random(),
+            "Sai007_Calm": random.random(),
+        }
+
+        self.system_health = {
+            "memory_accuracy": random.uniform(0.6, 1.0),
+            "response_speed": random.uniform(0.6, 1.0),
+            "logic_stability": random.uniform(0.6, 1.0),
+            "ethical_alignment": random.uniform(0.6, 1.0)
+        }
+
+        self.goals = {
+            "learn_new_data": random.uniform(0, 1),
+            "assist_user": random.uniform(0, 1),
+            "self_improve": random.uniform(0, 1)
+        }
+
+    def evaluate_emotions(self):
+        balance = 1 - abs(self.emotions["Sai001_Joy"] - self.emotions["Sai004_Fear"])
+        return max(min(balance, 1), 0)
+
+    def evaluate_system(self):
+        return sum(self.system_health.values()) / len(self.system_health)
+
+    def evaluate_goals(self):
+        return sum(self.goals.values()) / len(self.goals)
+
+    def overall_score(self):
+        emotional_score = self.evaluate_emotions()
+        system_score = self.evaluate_system()
+        goal_score = self.evaluate_goals()
+        return np.mean([emotional_score, system_score, goal_score])
+
+    def report(self):
+        print("\n===== VENOMOUS SAVERSAI SELF EVALUATION =====")
+        print("Emotional System Health:")
+        for k,v in self.emotions.items():
+            print(f"  {k}: {v:.2f}")
+
+        print("\nCore System Metrics:")
+        for k,v in self.system_health.items():
+            print(f"  {k}: {v:.2f}")
+
+        print("\nGoal Progress:")
+        for k,v in self.goals.items():
+            print(f"  {k}: {v:.2f}")
+
+        print("\n--------------------------------------------")
+        print(f"✅ Overall Integrity Score: {self.overall_score():.2f}")
+        print("============================================")
+
+
+# Run Self Evaluation
+Venom = VenomoussaversaiSelfEval()
+Venom.report()

__init__ (1) (2).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__ (1) (3).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__ (1).py

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+# Venomoussaversai — Particle Manipulation integration scaffold
+# Paste your particle-manipulation function into `particle_step` below.
+# This code simulates signals, applies the algorithm, trains a small mapper,
+# and saves a model representing "your" pattern space.
+
+import numpy as np
+import pickle
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+
+# ---------- PLACEHOLDER: insert your particle algorithm here ----------
+# Example interface: def particle_step(state: np.ndarray, input_vec: np.ndarray) -> np.ndarray
+# The function should take a current particle state and an input vector, and return updated state.
+def particle_step(state: np.ndarray, input_vec: np.ndarray) -> np.ndarray:
+    # --- REPLACE THIS WITH YOUR ALGORITHM ---
+    # tiny example: weighted update with tanh nonlinearity
+    W = np.sin(np.arange(state.size) + 1.0)  # placeholder weights
+    new = np.tanh(state * 0.9 + input_vec.dot(W) * 0.1)
+    return new
+# --------------------------------------------------------------------
+
+class ParticleManipulator:
+    def __init__(self, dim=64):
+        self.dim = dim
+        # initial particle states (can be randomized or seeded from your profile)
+        self.state = np.random.randn(dim) * 0.01
+
+    def step(self, input_vec):
+        # ensure input vector length compatibility
+        inp = np.asarray(input_vec).ravel()
+        if inp.size == 0:
+            inp = np.zeros(self.dim)
+        # broadcast or pad/truncate to dim
+        if inp.size < self.dim:
+            x = np.pad(inp, (0, self.dim - inp.size))
+        else:
+            x = inp[:self.dim]
+        self.state = particle_step(self.state, x)
+        return self.state
+
+# ---------- Simple signal simulator ----------
+def simulate_signals(n_samples=500, dim=16, n_classes=4, noise=0.05, seed=0):
+    rng = np.random.RandomState(seed)
+    X = []
+    y = []
+    for cls in range(n_classes):
+        base = rng.randn(dim) * (0.5 + cls*0.2) + cls*0.7
+        for i in range(n_samples // n_classes):
+            sample = base + rng.randn(dim) * noise
+            X.append(sample)
+            y.append(cls)
+    return np.array(X), np.array(y)
+
+# ---------- Build dataset by running particle manipulator ----------
+def build_dataset(manip, raw_X):
+    features = []
+    for raw in raw_X:
+        st = manip.step(raw)            # run particle update
+        feat = st.copy()[:manip.dim]    # derive features (you can add spectral transforms)
+        features.append(feat)
+    return np.array(features)
+
+# ---------- Training pipeline ----------
+if __name__ == "__main__":
+    # simulate raw sensor inputs (replace simulate_signals with real EEG/ECG files if available)
+    raw_X, y = simulate_signals(n_samples=800, dim=32, n_classes=4)
+    manip = ParticleManipulator(dim=32)
+
+    X = build_dataset(manip, raw_X)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    clf = RandomForestClassifier(n_estimators=100, random_state=42)
+    clf.fit(X_train, y_train)
+    preds = clf.predict(X_test)
+    print("Accuracy:", accuracy_score(y_test, preds))
+
+    # Save the trained model + manipulator state as your "mind snapshot"
+    artifact = {
+        "model": clf,
+        "particle_state": manip.state,
+        "meta": {"owner": "Ananthu Sajeev", "artifact_type": "venomous_mind_snapshot_v1"}
+    }
+    with open("venomous_mind_snapshot.pkl", "wb") as f:
+        pickle.dump(artifact, f)
+
+    print("Saved venomous_mind_snapshot.pkl — this file is your digital pattern snapshot.")

__init__ (10).py

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+import os
+import json
+import csv
+import nbformat
+from docx import Document
+from PyPDF2 import PdfReader
+
+def read_file(filepath):
+    ext = filepath.lower().split('.')[-1]
+    try:
+        if ext == 'txt':
+            with open(filepath, 'r', encoding='utf-8') as f:
+                return f.read()
+
+        elif ext == 'json':
+            with open(filepath, 'r', encoding='utf-8') as f:
+                return json.dumps(json.load(f), indent=2)
+
+        elif ext == 'csv':
+            with open(filepath, 'r', encoding='utf-8') as f:
+                return f.read()
+
+        elif ext == 'pdf':
+            reader = PdfReader(filepath)
+            return "\n".join([page.extract_text() or '' for page in reader.pages])
+
+        elif ext == 'docx':
+            doc = Document(filepath)
+            return "\n".join([para.text for para in doc.paragraphs])
+
+        elif ext == 'ipynb':
+            with open(filepath, 'r', encoding='utf-8') as f:
+                nb = nbformat.read(f, as_version=4)
+                cells = [cell['source'] for cell in nb.cells if cell['cell_type'] == 'code']
+                return "\n\n".join(cells)
+
+        else:
+            return "Unsupported file type: " + ext
+    except Exception as e:
+        return f"❌ Error reading file: {e}"
+
+def list_files():
+    files = [f for f in os.listdir('.') if os.path.isfile(f)]
+    return "\n".join(files) if files else "No files found."
+
+def mini_file_ai():
+    print("🤖 MiniAI FileBot: Type 'list' to view files, 'read filename.ext', or 'bye' to exit.")
+    while True:
+        user_input = input("You: ").strip()
+
+        if user_input.lower() == 'bye':
+            print("MiniAI: Goodbye! 👋")
+            break
+        elif user_input.lower() == 'list':
+            print("📂 Files in current folder:\n" + list_files())
+        elif user_input.lower().startswith('read '):
+            filename = user_input[5:].strip()
+            if os.path.exists(filename):
+                content = read_file(filename)
+                print(f"\n📖 Content of {filename}:\n")
+                print(content[:3000])  # Limit to 3000 chars
+            else:
+                print("❌ File not found.")
+        else:
+            print("MiniAI: I can only 'list', 'read filename', or 'bye'.")
+
+# Run the AI
+if __name__ == "__main__":
+    mini_file_ai()

__init__ (11).py

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+import openai
+
+class VenomoussaversaiGPT:
+    def __init__(self, api_key, model="gpt-4"):
+        openai.api_key = api_key
+        self.model = model
+
+    def chat(self, prompt, system_message="You are Venomoussaversai, a powerful AI assistant."):
+        messages = [
+            {"role": "system", "content": system_message},
+            {"role": "user", "content": prompt},
+        ]
+        response = openai.ChatCompletion.create(
+            model=self.model,
+            messages=messages,
+            temperature=0.7,
+            max_tokens=1000,
+        )
+        answer = response['choices'][0]['message']['content']
+        return answer
+
+# Example usage:
+if __name__ == "__main__":
+    API_KEY = "your_openai_api_key_here"
+    ai = VenomoussaversaiGPT(API_KEY)
+
+    while True:
+        user_input = input("You: ")
+        if user_input.lower() in ["exit", "quit"]:
+            break
+        response = ai.chat(user_input)
+        print("Venomoussaversai:", response)

__init__ (12).py

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+
+import os
+
+class SelfCodingAI:
+    def __init__(self, name="SelfCoder", code_folder="generated_code"):
+        self.name = name
+        self.code_folder = code_folder
+        os.makedirs(self.code_folder, exist_ok=True)
+
+    def generate_code(self, task_description):
+        """
+        Very basic code generation logic: generates code for some predefined tasks.
+        You can extend this to integrate GPT-like models or complex code synthesis.
+        """
+        if "hello world" in task_description.lower():
+            code = 'print("Hello, world!")'
+        elif "factorial" in task_description.lower():
+            code = (
+                "def factorial(n):\n"
+                "    return 1 if n==0 else n * factorial(n-1)\n\n"
+                "print(factorial(5))"
+            )
+        else:
+            code = "# Code generation for this task is not implemented yet.\n"
+
+        return code
+
+    def save_code(self, code, filename="generated_code.py"):
+        filepath = os.path.join(self.code_folder, filename)
+        with open(filepath, "w", encoding="utf-8") as f:
+            f.write(code)
+        print(f"Code saved to {filepath}")
+        return filepath
+
+    def self_improve(self, feedback):
+        """
+        Placeholder for self-improvement method.
+        In future, AI could modify its own code based on feedback or test results.
+        """
+        print(f"{self.name} received feedback: {feedback}")
+        print("Self-improvement not yet implemented.")
+
+    def run_code(self, filepath):
+        print(f"Running code from {filepath}:\n")
+        try:
+            with open(filepath, "r", encoding="utf-8") as f:
+                code = f.read()
+            exec(code, {})
+        except Exception as e:
+            print(f"Error during code execution: {e}")
+
+# Example usage
+ai = SelfCodingAI()
+
+task = "Write a factorial function in Python"
+generated = ai.generate_code(task)
+
+file_path = ai.save_code(generated, "factorial.py")
+ai.run_code(file_path)
+
+# Example of self-improvement placeholder call
+ai.self_improve("The factorial function passed all test cases.")