Update app.py

app.py

@@ -6,17 +6,6 @@ import torch.nn as nn
 import torch.optim as optim
 import numpy as np
 import random
-import tensorflow as tf
-import ray
-from ray import tune
-import pytorch_lightning as pl
-import optuna
-from sklearn.model_selection import train_test_split
-from torch.utils.data import DataLoader, TensorDataset
-from sklearn.preprocessing import StandardScaler
-from collections import deque
-import time
-import copy
 
 hf_token = os.getenv("HF_TOKEN").strip()
 api_key = os.getenv("HF_KEY").strip()
@@ -25,20 +14,14 @@ system_prompt = os.getenv("SYSTEM_PROMPT").strip()
 
 client = InferenceClient(model_name)
 
-ray.init(ignore_reinit_error=True)
-
-class ConsciousSupermassiveNN(pl.LightningModule):
+class ConsciousSupermassiveNN:
     def __init__(self):
-        super().__init__()
         self.snn = self.create_snn()
         self.rnn = self.create_rnn()
         self.cnn = self.create_cnn()
         self.fnn = self.create_fnn()
         self.ga_population = self.initialize_ga_population()
-        self.memory = {}
-        self.experience_replay = deque(maxlen=1000)
-        self.model_evolution_timer = 0
-        self.optuna_study = None
+        self.memory = {}  
 
     def create_snn(self):
         return nn.Sequential(
@@ -88,22 +71,26 @@ class ConsciousSupermassiveNN(pl.LightningModule):
     def run_snn(self, x):
         input_tensor = torch.tensor(x, dtype=torch.float32)
         output = self.snn(input_tensor)
+        print("SNN Output:", output)
         return output
 
     def run_rnn(self, x):
         h0 = torch.zeros(5, x.size(0), 2048)
         input_tensor = torch.tensor(x, dtype=torch.float32)
         output, hn = self.rnn(input_tensor, h0)
+        print("RNN Output:", output)
         return output
 
     def run_cnn(self, x):
         input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
         output = self.cnn(input_tensor)
+        print("CNN Output:", output)
         return output
 
     def run_fnn(self, x):
         input_tensor = torch.tensor(x, dtype=torch.float32)
         output = self.fnn(input_tensor)
+        print("FNN Output:", output)
         return output
 
     def run_ga(self, fitness_func):
@@ -114,6 +101,7 @@ class ConsciousSupermassiveNN(pl.LightningModule):
                 sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
             ]
             best_fitness = max(fitness_scores)
+            print(f"Generation {generation}, Best Fitness: {best_fitness}")
         return max(self.ga_population, key=fitness_func)
 
     def consciousness_loop(self, input_data, mode="snn"):
@@ -133,72 +121,6 @@ class ConsciousSupermassiveNN(pl.LightningModule):
         self.memory[mode] = output.detach().numpy()
         return output
 
-    def neural_architecture_search(self, input_data, output_data):
-        study = optuna.create_study(direction="minimize")
-        study.optimize(self.objective_function, n_trials=100)
-        best_trial = study.best_trial
-        return best_trial
-
-    def objective_function(self, trial):
-        model = self.create_model(trial)
-        criterion = nn.MSELoss()
-        optimizer = optim.Adam(model.parameters(), lr=trial.suggest_loguniform("lr", 1e-5, 1e-1))
-        x_train, x_val, y_train, y_val = train_test_split(input_data, output_data, test_size=0.2)
-        train_dataset = TensorDataset(torch.tensor(x_train, dtype=torch.float32), torch.tensor(y_train, dtype=torch.float32))
-        val_dataset = TensorDataset(torch.tensor(x_val, dtype=torch.float32), torch.tensor(y_val, dtype=torch.float32))
-        train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
-        val_loader = DataLoader(val_dataset, batch_size=32)
-
-        for epoch in range(10):
-            model.train()
-            for data, targets in train_loader:
-                optimizer.zero_grad()
-                output = model(data)
-                loss = criterion(output, targets)
-                loss.backward()
-                optimizer.step()
-
-        model.eval()
-        val_loss = 0
-        with torch.no_grad():
-            for data, targets in val_loader:
-                output = model(data)
-                val_loss += criterion(output, targets).item()
-
-        return val_loss
-
-    def create_model(self, trial):
-        model_type = trial.suggest_categorical("model_type", ["snn", "rnn", "cnn", "fnn"])
-        if model_type == "snn":
-            return self.create_snn()
-        elif model_type == "rnn":
-            return self.create_rnn()
-        elif model_type == "cnn":
-            return self.create_cnn()
-        elif model_type == "fnn":
-            return self.create_fnn()
-
-    def adaptive_learning_loop(self, input_data, target_data, model):
-        for step in range(1000):
-            prediction = model(input_data)
-            loss = self.compute_loss(prediction, target_data)
-            self.optimize_model(loss)
-
-    def compute_loss(self, prediction, target_data):
-        return nn.MSELoss()(prediction, target_data)
-
-    def optimize_model(self, loss):
-        optimizer = optim.Adam(self.parameters(), lr=0.001)
-        optimizer.zero_grad()
-        loss.backward()
-        optimizer.step()
-
-    def self_improve(self):
-        if self.model_evolution_timer % 10 == 0:
-            new_model = self.neural_architecture_search(self.input_data, self.target_data)
-            self.model = new_model
-        self.model_evolution_timer += 1
-
 supermassive_nn = ConsciousSupermassiveNN()
 
 def respond(message, history, max_tokens, temperature, top_p):
@@ -227,3 +149,4 @@ demo = gr.ChatInterface(
 
 if __name__ == "__main__":
     demo.launch(share=True)
+