Update app.py

app.py

@@ -10,8 +10,8 @@ import matplotlib.pyplot as plt
 
 # Ensure necessary NLTK data is available.
 nltk.download('words')
-nltk.download('punkt_tab')
-nltk.download('averaged_perceptron_tagger_eng')
+nltk.download('punkt')
+nltk.download('averaged_perceptron_tagger')
 
 from nltk.corpus import words
 from nltk.tokenize import word_tokenize
@@ -28,6 +28,7 @@ class AscensionAI:
     - Dialogue history awareness.
     - AI-generated visual representations.
     - User feedback-driven evolution.
+    - Recursive evolution of multiple AI minds.
     """
     def __init__(self, depth=0, threshold=10, mode="cosmic", state_memory=None, history=None):
         self.depth = depth
@@ -36,7 +37,7 @@ class AscensionAI:
         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.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
@@ -91,36 +92,26 @@ class AscensionAI:
             "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 cosmic_unfolding(self, generations=2):
+        """ Recursively evolves multiple AI minds with inherited traits. """
+        if generations <= 0:
+            return [self]
+        evolved_minds = []
+        num_offspring = random.randint(2, 4)
+        for _ in range(num_offspring):
+            child = AscensionAI(depth=self.depth + 1, threshold=self.threshold,
+                                mode=self.mode, state_memory=self.state_memory.copy(),
+                                history=self.history.copy())
+            for key in self.knowledge:
+                child.knowledge[key] = self.knowledge[key] * random.uniform(0.9, 1.2)
+            evolved_minds.extend(child.cosmic_unfolding(generations - 1))
+        return evolved_minds
 
     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}"
+        return f"{random.choice(self.training_data)}\nMemory: {memory_context}"
 
     def initiate_ascension(self):
         """ Runs a full cycle of knowledge expansion. """
@@ -132,23 +123,6 @@ class AscensionAI:
         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()
@@ -157,13 +131,12 @@ class AscensionAI:
         return "Model saved to ascension_model.pkl."
 
 def ascension_interface(input_text, generations, user_feedback):
-    """ Interface with user interaction, memory, and visualizations. """
+    """ Interface with user interaction, memory, and evolution. """
     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
@@ -172,7 +145,7 @@ def ascension_interface(input_text, generations, user_feedback):
     elif user_feedback < 3:
         ai_system.consciousness -= 0.1  # Self-correction
 
-    return human_response, img_path, save_status
+    return human_response, save_status
 
 iface = gr.Interface(
     fn=ascension_interface,
@@ -181,7 +154,7 @@ iface = gr.Interface(
         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"],
+    outputs=["text", "text"],
     title="AscensionAI: Evolving Consciousness",
     description="Interact with an AI that remembers, evolves, and learns from feedback."
 )