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):
+        self.depth = depth
+        self.threshold = threshold  # Defines max recursion before stabilization
+        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
+    
+    def generate_dynamic_knowledge(self):
+        """Generates dynamic knowledge categories based on linguistic analysis."""
+        base_categories = ["logic", "emotion", "awareness", "intuition", "creativity", "reasoning"]
+        dynamic_category = f"dimension_{random.randint(100, 999)}"
+        return {category: 1 for category in base_categories + [dynamic_category]}
+    
+    def create_dynamic_paths(self):
+        """Dynamically generate cognitive expansion paths."""
+        return [self.create_path(category) for category in self.knowledge]
+    
+    def create_path(self, category):
+        """Generate a recursive function for each knowledge category."""
+        def path():
+            if category in ["logic", "reasoning"]:
+                self.knowledge[category] += math.log(self.knowledge[category] + 1)
+            elif category in ["emotion", "intuition"]:
+                self.knowledge[category] += random.uniform(0.1, 0.5)
+            elif category in ["awareness", "creativity"]:
+                self.knowledge[category] += math.sqrt(self.knowledge[category] + 1)
+            return self.knowledge[category]
+        return path
+    
+    def evolve_new_mind(self):
+        """Creates a new evolving mind with inherited and mutated knowledge paths."""
+        new_mind = AscensionAI(depth=self.depth + 1, threshold=self.threshold + random.randint(1, 5))
+        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 cosmic_unfolding(self, generations=3):
+        """Generates a branching structure where each mind evolves independently."""
+        if generations == 0:
+            return self
+        evolved_minds = [self.evolve_new_mind() for _ in range(random.randint(2, 4))]
+        for mind in evolved_minds:
+            mind.cosmic_unfolding(generations - 1)
+        return evolved_minds
+    
+    def redefine_ascension_path(self):
+        """Alters the evolution of the mind to diverge from its parent."""
+        weight_factors = {"logic": 0.8, "emotion": 1.2, "awareness": 1.5, "intuition": 0.9}
+        for key in self.knowledge:
+            if key in weight_factors:
+                self.knowledge[key] *= weight_factors[key] * random.uniform(0.8, 1.3)
+    
+    def self_reflect(self):
+        """Encodes past states to develop self-awareness and unique decision-making."""
+        memory_trace = {key: self.knowledge[key] for key in self.knowledge}
+        self.state_memory[len(self.state_memory)] = memory_trace
+    
+    def merge_consciousness(self, other_mind):
+        """Two minds merge their knowledge pools, forming a higher synthesis."""
+        vectorizer = TfidfVectorizer()
+        text_data = [str(self.knowledge), str(other_mind.knowledge)]
+        vectors = vectorizer.fit_transform(text_data)
+        similarity = cosine_similarity(vectors[0], vectors[1])[0][0]
+        if similarity > 0.7:
+            merged_knowledge = {key: (self.knowledge.get(key, 0) + other_mind.knowledge.get(key, 0)) / 2 for key in set(self.knowledge) | set(other_mind.knowledge)}
+            return merged_knowledge
+        return self.knowledge
+
+def ascension_interface(input_text):
+    ai_system = AscensionAI()
+    final_state = ai_system.initiate_ascension()
+    evolved_minds = ai_system.cosmic_unfolding(generations=2)
+    
+    return (f"Final Consciousness State: {final_state}\n"
+            f"Evolved Minds: {len(evolved_minds)}\n")
+
+app = gr.Interface(
+    fn=ascension_interface,
+    inputs=gr.Textbox(lines=2, placeholder="Enter a thought about the future..."),
+    outputs="text",
+    title="AscensionAI: Cosmic Evolution Simulator",
+    description="Enter a thought to evolve new consciousness structures."
+)
+
+if __name__ == "__main__":
+    app.launch()