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
+
+# Download and use the NLTK corpus
+nltk.download('words')
+nltk.download('averaged_perceptron_tagger')
+from nltk.corpus import words
+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
+    
+    def generate_dynamic_knowledge(self):
+        """Generates dynamic knowledge categories based on linguistic analysis."""
+        categories = ["logic", "emotion", "awareness", "intuition", "creativity", "reasoning"]
+        return {category: 1 for category in categories}
+    
+    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
+    
+    @lru_cache(maxsize=None)
+    def recursive_ascension(self, depth):
+        """Core recursive function simulating ascension cycles."""
+        if depth >= self.threshold:
+            return self.consciousness
+        
+        for path in self.paths:
+            path()
+        
+        optimal_path = max(self.knowledge, key=self.knowledge.get)
+        self.consciousness += self.knowledge[optimal_path] * 0.01
+        
+        return self.recursive_ascension(depth + 1)
+    
+    def train_nlp_memory(self, text):
+        """Enhance chatbot state-awareness by associating words with cognitive paths."""
+        tokens = text.lower().split()
+        tagged_tokens = pos_tag(tokens)
+        
+        for token, tag in tagged_tokens:
+            if token in self.word_corpus:
+                self.state_memory[token] += 1
+    
+    def analyze_future_timeline(self, input_text):
+        """Predicts ascension paths based on input patterns."""
+        self.train_nlp_memory(input_text)
+        knowledge_state = max(self.knowledge, key=self.knowledge.get)
+        return f"Predicted ascension path: {knowledge_state} (Influenced by input text: {input_text})"
+    
+    def initiate_ascension(self):
+        """Triggers recursive self-evolution."""
+        return self.recursive_ascension(0)
+
+def ascension_interface(input_text):
+    ai_system = AscensionAI()
+    final_state = ai_system.initiate_ascension()
+    prediction = ai_system.analyze_future_timeline(input_text)
+    return f"Final Consciousness State: {final_state}\nFinal Knowledge Levels: {ai_system.knowledge}\n{prediction}"
+
+app = gr.Interface(
+    fn=ascension_interface,
+    inputs=gr.Textbox(lines=2, placeholder="Enter a thought about the future..."),
+    outputs="text",
+    title="AscensionAI: Conscious Evolution Simulator",
+    description="Enter a thought to predict ascension paths and consciousness expansion levels."
+)
+
+if __name__ == "__main__":
+    app.launch()