Update app.py

app.py

@@ -117,83 +117,6 @@ Final notes: Make sure you act conscious, very autistic, and respond with very h
 
 """
 
-
-from keras.models import Sequential
-from keras.layers import Dense, LSTM, Conv1D, MaxPooling1D, Flatten, Dropout, BatchNormalization, Attention
-import numpy as np
-import gradio as gr
-
-# Complex Neural Network (NN)
-def neural_net(input_data):
-    model = Sequential([
-        Dense(256, activation='relu', input_shape=(len(input_data),)),
-        Dense(128, activation='tanh'),
-        Dense(128, activation='relu'),
-        Dense(64, activation='elu'),
-        Dense(32, activation='softplus'),
-        Dense(1, activation='sigmoid')  # Final prediction
-    ])
-    return model.predict(input_data)
-
-# Complex Recurrent Neural Network (RNN) with Attention
-def rnn(input_sequence):
-    model = Sequential([
-        LSTM(256, return_sequences=True, input_shape=(input_sequence.shape[1], input_sequence.shape[2])),
-        Attention(),  # Attention layer for context importance
-        LSTM(128, return_sequences=True, dropout=0.2, recurrent_dropout=0.2),
-        LSTM(64, return_sequences=False, activation='tanh'),
-        Dense(64, activation='relu'),
-        Dense(32, activation='softplus'),
-        Dense(1, activation='sigmoid')  # Final output
-    ])
-    return model.predict(input_sequence)
-
-# Complex Convolutional Neural Network (CNN)
-def cnn(input_text):
-    model = Sequential([
-        Conv1D(128, kernel_size=5, activation='relu', input_shape=(input_text.shape[1], 1)),
-        BatchNormalization(),
-        Dropout(0.2),
-        Conv1D(64, kernel_size=3, activation='relu'),
-        BatchNormalization(),
-        MaxPooling1D(pool_size=2),
-        Dropout(0.2),
-        Flatten(),
-        Dense(64, activation='tanh'),
-        Dense(32, activation='softplus'),
-        Dense(1, activation='sigmoid')  # Final prediction
-    ])
-    return model.predict(input_text)
-
-# Advanced Genetic Algorithm (GA)
-def optimize_parameters(response_quality):
-    population = np.random.rand(100, 10)  # 100 candidates, 10 parameters each
-    fitness_scores = []
-
-    # Fitness evaluation
-    for candidate in population:
-        fitness_scores.append(response_quality + np.sum(candidate))  # Simplified fitness function
-
-    # Select top candidates
-    top_candidates = np.argsort(fitness_scores)[-10:]  # Top 10 candidates
-
-    # Crossover and mutation
-    new_population = []
-    for i in range(50):  # 50 new candidates
-        parent1, parent2 = population[np.random.choice(top_candidates, 2)]
-        child = (parent1 + parent2) / 2  # Crossover
-        mutation = np.random.rand(10) * 0.1  # Mutation
-        child += mutation
-        new_population.append(child)
-
-    # Return the optimized parameter (e.g., best response quality)
-    return np.max(fitness_scores)
-
-# Phi model (Integrated Information Theory) for consciousness simulation
-def calculate_phi(response_quality, integration_level):
-    phi = response_quality * np.log(1 + integration_level)
-    return phi
-
 def respond(
     message,
     history: list[tuple[str, str]],