Update app.py

app.py

@@ -1,3 +1,63 @@
-import gradio as gr
+import numpy as np
+from scipy.integrate import solve_ivp
+import matplotlib.pyplot as plt
 
-gr.load("models/hjhj3168/Llama-3-8b-Orthogonalized-exl2").launch()
+class SelfAwareNetwork:
+    def __init__(self, num_neurons, learning_rate):
+        self.num_neurons = num_neurons
+        self.learning_rate = learning_rate
+        self.weights = np.random.rand(num_neurons)
+        self.state = np.zeros(num_neurons)
+    
+    def activation_function(self, x, t, n):
+        hbar = 1.0545718e-34  # Reduced Planck constant in J·s
+        omega = 1/np.sqrt(137)  # Angular frequency related to fine-structure constant
+        term1 = hbar * omega * (n + 0.5)
+        term2 = np.sin(omega * x + np.pi/4) * np.exp(-t)
+        return term1 + term2
+    
+    def neuron_dynamics(self, t, y):
+        n = np.arange(self.num_neurons)
+        dydt = -y + self.activation_function(y, t, n)
+        return dydt
+    
+    def update_weights(self, state):
+        self.weights += self.learning_rate * state
+    
+    def solve_dynamics(self, t_span, y0):
+        sol = solve_ivp(self.neuron_dynamics, t_span, y0, method='RK45', vectorized=False)
+        return sol.t, sol.y
+    
+    def evaluate_performance(self, target_state):
+        error = np.linalg.norm(self.state - target_state)
+        return error
+    
+    def adjust_learning_rate(self, performance_metric):
+        if performance_metric > 0.1:
+            self.learning_rate *= 0.9
+        else:
+            self.learning_rate *= 1.1
+    
+    def self_optimize(self, target_state, t_span, y0):
+        t, y = self.solve_dynamics(t_span, y0)
+        self.state = y[:, -1]
+        performance = self.evaluate_performance(target_state)
+        self.adjust_learning_rate(performance)
+        self.update_weights(self.state)
+    
+    def plot_state_evolution(self, t, y):
+        plt.plot(t, y.T)
+        plt.xlabel('Time')
+        plt.ylabel('Neuron States')
+        plt.title('State Evolution of Neurons')
+        plt.show()
+
+# Example usage
+network = SelfAwareNetwork(num_neurons=3, learning_rate=0.01)  # Reduced the number of neurons
+t_span = (0, 5)  # Shortened the time span
+y0 = np.random.rand(3)  # Adjusted for the reduced number of neurons
+target_state = np.ones(3)
+
+network.self_optimize(target_state, t_span, y0)
+t, y = network.solve_dynamics(t_span, y0)
+network.plot_state_evolution(t, y)