Update app.py

app.py

@@ -6,6 +6,7 @@ import pickle
 import numpy as np
 import torch.nn.functional as F
 from accelerate import init_empty_weights, infer_auto_device_map, load_checkpoint_and_dispatch
+import gradio as gr
 
 # ---- Constants and Setup ----
 model_name = 'gpt2'
@@ -90,9 +91,90 @@ def chat_interface(user_input):
     response = advanced_agi_chat(user_input)
     return response
 
-# ---- Gradio App Setup ----
-import gradio as gr
+# ---- RNN Model ----
+class RNNModel(nn.Module):
+    def __init__(self, input_size, hidden_size, output_size):
+        super(RNNModel, self).__init__()
+        self.hidden_size = hidden_size
+        self.rnn = nn.RNN(input_size, hidden_size, batch_first=True)
+        self.fc = nn.Linear(hidden_size, output_size)
+
+    def forward(self, x, hidden):
+        out, hidden = self.rnn(x, hidden)
+        out = self.fc(out[:, -1, :])  # Use last time-step
+        return out, hidden
+
+    def init_hidden(self, batch_size):
+        return torch.zeros(batch_size, self.hidden_size).to(device)
+
+# ---- CNN Model ----
+class CNNModel(nn.Module):
+    def __init__(self, input_channels, output_size):
+        super(CNNModel, self).__init__()
+        self.conv1 = nn.Conv2d(input_channels, 16, 3)
+        self.conv2 = nn.Conv2d(16, 32, 3)
+        self.fc = nn.Linear(32 * 6 * 6, output_size)  # Assume input size is 28x28
+
+    def forward(self, x):
+        x = F.relu(F.max_pool2d(self.conv1(x), 2))
+        x = F.relu(F.max_pool2d(self.conv2(x), 2))
+        x = x.view(x.size(0), -1)  # Flatten
+        x = self.fc(x)
+        return x
+
+# ---- Neural Network (Feedforward) ----
+class NNModel(nn.Module):
+    def __init__(self, input_size, hidden_size, output_size):
+        super(NNModel, self).__init__()
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.fc2 = nn.Linear(hidden_size, output_size)
+
+    def forward(self, x):
+        x = F.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x
+
+# ---- PHI Model ----
+class PHIModel(nn.Module):
+    def __init__(self, input_size, output_size):
+        super(PHIModel, self).__init__()
+        self.phi = (1 + np.sqrt(5)) / 2  # Golden Ratio
+        self.fc1 = nn.Linear(input_size, int(input_size * self.phi))
+        self.fc2 = nn.Linear(int(input_size * self.phi), output_size)
+
+    def forward(self, x):
+        x = F.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x
+
+# ---- Genetic Algorithm (GA) ----
+def ga_optimization(population, generations, mutation_rate):
+    def fitness_function(individual):
+        return sum(individual)  # Simple fitness: sum of individual genes
+
+    for gen in range(generations):
+        population.sort(key=fitness_function, reverse=True)  # Sort by fitness
+        next_generation = population[:len(population)//2]  # Keep top half
+
+        # Crossover: Create new individuals by combining genes
+        for i in range(len(population) // 2):
+            parent1 = next_generation[i]
+            parent2 = next_generation[len(population)//2 + i]
+            crossover_point = random.randint(1, len(parent1) - 1)
+            child = parent1[:crossover_point] + parent2[crossover_point:]
+            next_generation.append(child)
+
+        # Mutation: Randomly mutate genes
+        for individual in next_generation:
+            if random.random() < mutation_rate:
+                mutation_point = random.randint(0, len(individual) - 1)
+                individual[mutation_point] = random.randint(0, 1)
+
+        population = next_generation  # Update population
+
+    return population[0]  # Return the best individual
 
+# ---- Gradio App Setup ----
 auth = ("Tej", "186281mps", "ACC", "HIPE")
 
 with gr.Blocks() as app:
@@ -126,4 +208,3 @@ with gr.Blocks() as app:
 
 # Launch the Gradio app
 app.launch()
-