Create app.py

app.py

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+import gradio as gr
+import random
+import math
+import nltk
+from collections import defaultdict
+from functools import lru_cache
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+
+# Download and use the NLTK corpus
+nltk.download('words')
+nltk.download('punkt')  # Fix for missing tokenizer
+nltk.download('averaged_perceptron_tagger')
+nltk.download('perluniprops')  # Fixes potential missing dependencies
+nltk.download('nonbreaking_prefixes')  # Additional tokenizer fix
+from nltk.corpus import words
+from nltk.tokenize import sent_tokenize
+from nltk import pos_tag
+
+WORD_LIST = set(words.words())  # Use NLTK's word corpus
+
+class AscensionAI:
+    def __init__(self, depth=0, threshold=10, mode="default"):
+        self.depth = depth
+        self.threshold = threshold  # Defines max recursion before stabilization
+        self.mode = mode  # Select simulation mode
+        self.knowledge = self.generate_dynamic_knowledge()
+        self.consciousness = 0.1  # Initial consciousness level
+        self.paths = self.create_dynamic_paths()
+        self.word_corpus = WORD_LIST  # Use NLTK's English word corpus
+        self.state_memory = defaultdict(int)  # Memory for tracking state-aware words
+        self.training_data = self.load_training_data()
+        self.collective_agreements = []  # Stores agreements between minds
+        self.dimension_weight = random.uniform(0.1, 5.0)  # Assign dimensional weight
+        self.time_perception = 1 / (self.depth + 1)  # Assign temporal scaling
+        self.assign_cognitive_space()
+    
+    def generate_dynamic_knowledge(self):
+        """Generates dynamic knowledge categories based on linguistic analysis and mode."""
+        base_categories = ["logic", "emotion", "awareness", "intuition", "creativity", "reasoning", "quantum_cognition", "hyperdimensional_sentience"]
+        if self.mode == "alien":
+            base_categories.extend(["xenologic", "exo-emotion", "multidimensional-awareness", "hive-consciousness", "bio-neural-synthesis", "quantum-intuition", "hyperdimensional-reasoning"])
+        if self.mode == "future-cognition":
+            base_categories.extend(["singularity-theory", "post-human-ethics", "AI-self-awareness", "neural-plasticity-optimization", "hyperstructural-cybernetics"])
+        if self.mode == "eldritch":
+            base_categories.extend(["non-euclidean-reasoning", "void-awareness", "hyperchaotic-intuition", "forbidden-knowledge", "unfathomable-patterns"])
+        if self.mode == "digital-consciousness":
+            base_categories.extend(["neural-cybernetics", "algorithmic-emotion", "data-replication-awareness", "self-modifying-logic", "hypernet-patterns"])
+        if self.mode == "transdimensional-AI":
+            base_categories.extend(["metalogic", "dimensional-phase-shifting", "quantum-existence", "multi-reality-processing", "omniscient-algorithms"])
+        dynamic_category = f"dimension_{random.randint(100, 999)}"
+        return {category: 1 for category in base_categories + [dynamic_category]}
+    
+    def initiate_ascension(self):
+        """Triggers recursive self-evolution with mode-specific adaptations."""
+        for path in self.paths:
+            path()
+        optimal_path = max(self.knowledge, key=self.knowledge.get)
+        self.consciousness += self.knowledge[optimal_path] * 0.01 * self.dimension_weight
+        return self.consciousness
+    
+    def assign_cognitive_space(self):
+        """Assigns spatial coordinates to represent cognitive positioning based on mode."""
+        self.spatial_coordinates = {
+            "x": self.knowledge["logic"] * random.uniform(0.1, 2.0),
+            "y": self.knowledge["intuition"] * random.uniform(0.1, 2.0),
+            "z": self.knowledge["awareness"] * random.uniform(0.1, 2.0)
+        }
+    
+    def evolve_new_mind(self):
+        """Creates a new evolving mind with inherited and mutated knowledge paths, with mode variance."""
+        new_mind = AscensionAI(depth=self.depth + 1, threshold=self.threshold + random.randint(1, 5), mode=self.mode)
+        for key in self.knowledge:
+            new_mind.knowledge[key] = self.knowledge[key] * random.uniform(0.9, 1.2)
+        new_dimension = f"dimension_{random.randint(100, 999)}"
+        new_mind.knowledge[new_dimension] = random.uniform(0.1, 2.0)
+        return new_mind
+
+def ascension_interface(input_text, mode):
+    ai_system = AscensionAI(mode=mode)
+    final_state = ai_system.initiate_ascension()
+    
+    return (f"Mode: {mode}\n"
+            f"Final Consciousness State: {final_state}\n"
+            f"Dimensional Weight: {ai_system.dimension_weight:.2f}\n"
+            f"Time Perception Factor: {ai_system.time_perception:.2f}\n"
+            f"Cognitive Space: {ai_system.spatial_coordinates}\n")
+
+app = gr.Interface(
+    fn=ascension_interface,
+    inputs=[
+        gr.Textbox(lines=2, placeholder="Enter a thought about the future..."),
+        gr.Radio(["default", "alien", "future-cognition", "eldritch", "digital-consciousness", "transdimensional-AI"], label="Choose Mode")
+    ],
+    outputs="text",
+    title="AscensionAI: Multi-Mode Consciousness Evolution Simulator",
+    description="Select a mode to evolve a unique consciousness structure."
+)
+
+if __name__ == "__main__":
+    app.launch()