Update app.py

app.py

@@ -1,25 +1,89 @@
-from transformers import AutoModelForCausalLM, AutoTokenizer
 import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+import torch.nn as nn
 import os
 
-# Hugging Face model and token
+# Model setup
 model_name = "nvidia/Hymba-1.5B-Instruct"
 hf_token = os.getenv("DOWNLOAD")
-
-# Load model and tokenizer
 tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=hf_token)
 model = AutoModelForCausalLM.from_pretrained(model_name, use_auth_token=hf_token)
 
-# System message
-system_message = "You are a helpful assistant. Always provide clear and concise responses."
+class LargeNeuralNetwork(nn.Module):
+    def __init__(self):
+        super(LargeNeuralNetwork, self).__init__()
+        self.layer1 = nn.Linear(512, 2048)
+        self.layer2 = nn.Linear(2048, 4096)
+        self.layer3 = nn.Linear(4096, 8192)
+        self.layer4 = nn.Linear(8192, 16384)
+        self.layer5 = nn.Linear(16384, 32768)
+        self.relu = nn.ReLU()
+        self.output = nn.Linear(32768, 1)
+
+    def forward(self, x):
+        x = self.relu(self.layer1(x))
+        x = self.relu(self.layer2(x))
+        x = self.relu(self.layer3(x))
+        x = self.relu(self.layer4(x))
+        x = self.relu(self.layer5(x))
+        return self.output(x)
+
+class LargeRecurrentNN(nn.Module):
+    def __init__(self):
+        super(LargeRecurrentNN, self).__init__()
+        self.rnn = nn.RNN(input_size=512, hidden_size=2048, num_layers=3, batch_first=True)
+        self.fc = nn.Linear(2048, 1)
+
+    def forward(self, x):
+        h0 = torch.zeros(3, x.size(0), 2048).to(x.device)
+        out, _ = self.rnn(x, h0)
+        out = self.fc(out[:, -1, :])
+        return out
+
+class LargeConvolutionalNN(nn.Module):
+    def __init__(self):
+        super(LargeConvolutionalNN, self).__init__()
+        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
+        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
+        self.fc1 = nn.Linear(128*32*32, 1024)
+        self.fc2 = nn.Linear(1024, 1)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        x = self.relu(self.conv1(x))
+        x = self.relu(self.conv2(x))
+        x = self.relu(self.conv3(x))
+        x = x.view(x.size(0), -1)
+        x = self.relu(self.fc1(x))
+        return self.fc2(x)
+
+class PhiModel(nn.Module):
+    def __init__(self):
+        super(PhiModel, self).__init__()
+        self.fc = nn.Linear(512, 1024)
+
+    def forward(self, x):
+        return self.fc(x)
+
+class GeneticAlgorithm(nn.Module):
+    def __init__(self):
+        super(GeneticAlgorithm, self).__init__()
+        self.fc = nn.Linear(512, 1024)
+
+    def forward(self, x):
+        return self.fc(x)
 
-# Chat function
-def chat(user_input):
-    prompt = f"{system_message}\n\nUser: {user_input}\nAssistant:"
+def chat(message, history):
+    prompt = f"User: {message}\nAssistant:"
     inputs = tokenizer(prompt, return_tensors="pt")
     outputs = model.generate(**inputs, max_new_tokens=150)
     return tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-# Gradio interface
-interface = gr.Interface(fn=chat, inputs="text", outputs="text")
-interface.launch()
+gr.ChatInterface(
+    fn=chat, 
+    type="messages", 
+    title="Chatbot",
+    description="Interact with the AI assistant."
+).launch()