Update app.py

app.py

@@ -6,6 +6,17 @@ 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()
@@ -14,14 +25,20 @@ system_prompt = os.getenv("SYSTEM_PROMPT").strip()
 
 client = InferenceClient(model_name)
 
-class ConsciousSupermassiveNN:
+ray.init(ignore_reinit_error=True)
+
+class ConsciousSupermassiveNN(pl.LightningModule):
     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.memory = {}
+        self.experience_replay = deque(maxlen=1000)
+        self.model_evolution_timer = 0
+        self.optuna_study = None
 
     def create_snn(self):
         return nn.Sequential(
@@ -71,26 +88,22 @@ class ConsciousSupermassiveNN:
     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):
@@ -101,7 +114,6 @@ class ConsciousSupermassiveNN:
                 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"):
@@ -121,6 +133,72 @@ class ConsciousSupermassiveNN:
         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):