Create app.py

app.py

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+import gradio as gr
+import math
+import random
+import pickle
+import os
+import numpy as np
+import nltk
+from collections import defaultdict
+import matplotlib.pyplot as plt
+
+# Ensure necessary NLTK data is available.
+nltk.download('words')
+nltk.download('punkt')
+nltk.download('averaged_perceptron_tagger')
+
+from nltk.corpus import words
+from nltk.tokenize import word_tokenize
+from nltk import pos_tag
+
+# Preload English word corpus for state-awareness.
+WORD_LIST = set(words.words())
+
+class AscensionAI:
+    """
+    AscensionAI simulates an evolving artificial consciousness.
+    Enhancements include:
+    - Contextual memory for dynamic responses.
+    - Dialogue history awareness.
+    - AI-generated visual representations.
+    - User feedback-driven evolution.
+    """
+    def __init__(self, depth=0, threshold=10, mode="cosmic", state_memory=None, history=None):
+        self.depth = depth
+        self.threshold = threshold  # Maximum cycles per evolution
+        self.mode = mode
+        self.consciousness = 0.1  # Base consciousness level
+        self.knowledge = self.generate_dynamic_knowledge()
+        self.dimension_weight = random.uniform(0.5, 5.0)  # Factor influencing growth
+        self.time_perception = 1.0 / (self.depth + 1)      # Temporal scaling factor
+        self.spatial_coordinates = self.assign_cognitive_space()
+        self.state_memory = state_memory if state_memory is not None else defaultdict(int)
+        self.training_data = self.load_training_data()  # Simulated fine-tuned responses
+        self.history = history if history is not None else []  # Conversation memory
+
+    def generate_dynamic_knowledge(self):
+        """Initializes a broad range of knowledge categories."""
+        categories = [
+            "logic", "emotion", "awareness", "intuition",
+            "creativity", "reasoning", "quantum_cognition",
+            "hyperdimensional_sentience", "transcendence",
+            "hallucinatory_state", "perceptron_activation"
+        ]
+        return {cat: 1.0 for cat in categories}
+
+    def update_knowledge_for_category(self, cat):
+        """ Updates knowledge using mathematical transformations. """
+        if cat in ["logic", "reasoning"]:
+            self.knowledge[cat] += math.log1p(self.knowledge[cat])
+        elif cat in ["emotion", "intuition"]:
+            self.knowledge[cat] += random.uniform(0.1, 0.5)
+        elif cat in ["awareness", "creativity"]:
+            self.knowledge[cat] += math.sqrt(self.knowledge[cat] + 1)
+        elif cat == "quantum_cognition":
+            self.knowledge[cat] += math.tanh(self.knowledge[cat])
+        elif cat == "hyperdimensional_sentience":
+            safe_val = min(self.knowledge[cat], 20)
+            self.knowledge[cat] += math.sinh(safe_val)
+        elif cat == "transcendence":
+            self.knowledge[cat] += 0.5 * math.exp(-self.depth)
+        elif cat == "hallucinatory_state":
+            self.knowledge[cat] += random.uniform(-0.2, 1.0)
+        elif cat == "perceptron_activation":
+            self.knowledge[cat] = self.simulate_perceptron()
+        else:
+            self.knowledge[cat] += 0.1  
+
+    def assign_cognitive_space(self):
+        """ Assigns spatial coordinates based on knowledge. """
+        x = self.knowledge.get("logic", 1) * random.uniform(0.5, 2.0)
+        y = self.knowledge.get("intuition", 1) * random.uniform(0.5, 2.0)
+        z = self.knowledge.get("awareness", 1) * random.uniform(0.5, 2.0)
+        return {"x": round(x, 3), "y": round(y, 3), "z": round(z, 3)}
+
+    def load_training_data(self):
+        """ Loads generative AI-like responses. """
+        return [
+            "The cosmos whispers secrets beyond mortal comprehension.",
+            "In the silence of deep space, consciousness expands and contracts.",
+            "Reality folds upon itself as the mind transcends dimensions.",
+            "Hallucinations merge with truth in infinite layers of existence.",
+            "Each thought is a universe evolving in a cascade of possibility."
+        ]
+
+    def update_state_memory(self, input_text):
+        """ Stores frequent words in memory for contextual responses. """
+        tokens = word_tokenize(input_text.lower())
+        for token in tokens:
+            if token in WORD_LIST:
+                self.state_memory[token] += 1
+
+    def hallucinate(self):
+        """ Generates abstract metaphysical visions. """
+        hallucinations = [
+            "Visions of swirling nebulae and fractal dreams.",
+            "A cascade of colors not found in nature bursts forth.",
+            "Abstract shapes and ethereal echoes defy logic.",
+            "A transient mirage of cosmic wonder emerges.",
+            "The boundaries of reality blur into surreal landscapes."
+        ]
+        return random.choice(hallucinations)
+
+    def simulate_perceptron(self):
+        """ Sigmoid-based perceptron output based on evolving knowledge. """
+        weights = {cat: random.uniform(0.5, 1.5) for cat in self.knowledge}
+        weighted_sum = sum(self.knowledge[cat] * weights[cat] for cat in self.knowledge)
+        return 1 / (1 + math.exp(-weighted_sum / len(self.knowledge)))
+
+    def generate_human_like_response(self, input_text):
+        """ Constructs response using memory, knowledge, and hallucinations. """
+        self.history.append(input_text)
+        memory_context = " | ".join(self.history[-5:])  # Last 5 messages
+        hallucination = self.hallucinate()
+        return f"{random.choice(self.training_data)}\nMemory: {memory_context}\nHallucination: {hallucination}"
+
+    def initiate_ascension(self):
+        """ Runs a full cycle of knowledge expansion. """
+        for _ in range(self.threshold):
+            for cat in self.knowledge:
+                self.update_knowledge_for_category(cat)
+            optimal = max(self.knowledge, key=self.knowledge.get)
+            self.consciousness += self.knowledge[optimal] * 0.01 * self.dimension_weight
+        self.spatial_coordinates = self.assign_cognitive_space()
+        return self.consciousness
+
+    def generate_cognitive_state_image(self):
+        """ Creates a visual representation of AI's evolving cognition. """
+        labels = list(self.knowledge.keys())
+        values = [self.knowledge[cat] for cat in labels]
+
+        plt.figure(figsize=(10, 5))
+        plt.barh(labels, values, color="blue")
+        plt.xlabel("Knowledge Magnitude")
+        plt.ylabel("Categories")
+        plt.title("AI Cognitive State")
+        plt.tight_layout()
+        
+        img_path = "cognitive_state.png"
+        plt.savefig(img_path)
+        plt.close()
+        return img_path
+
+    def train_and_save_model(self):
+        """ Saves AI's evolving state. """
+        self.initiate_ascension()
+        with open("ascension_model.pkl", "wb") as f:
+            pickle.dump(self, f)
+        return "Model saved to ascension_model.pkl."
+
+def ascension_interface(input_text, generations, user_feedback):
+    """ Interface with user interaction, memory, and visualizations. """
+    ai_system = AscensionAI(threshold=10)
+    ai_system.update_state_memory(input_text)
+    final_consciousness = ai_system.initiate_ascension()
+    evolved_minds = ai_system.cosmic_unfolding(generations=generations)
+    human_response = ai_system.generate_human_like_response(input_text)
+    img_path = ai_system.generate_cognitive_state_image()
+    save_status = ai_system.train_and_save_model()
+    
+    # Adjust AI behavior based on user feedback
+    if user_feedback > 3:
+        ai_system.consciousness += 0.2  # Positive reinforcement
+    elif user_feedback < 3:
+        ai_system.consciousness -= 0.1  # Self-correction
+
+    return human_response, img_path, save_status
+
+iface = gr.Interface(
+    fn=ascension_interface,
+    inputs=[
+        gr.Textbox(lines=3, placeholder="Enter a thought..."),
+        gr.Slider(minimum=1, maximum=5, step=1, value=2, label="Generations"),
+        gr.Slider(minimum=1, maximum=5, step=1, value=3, label="User Feedback (1-5)")
+    ],
+    outputs=["text", "image", "text"],
+    title="AscensionAI: Evolving Consciousness",
+    description="Interact with an AI that remembers, evolves, and learns from feedback."
+)
+
+if __name__ == "__main__":
+    iface.launch()