corrected the page seq

Name_Generator.py

@@ -0,0 +1,59 @@
+import os
+from pathlib import Path
+import streamlit as st
+import torch 
+from network.network import NeuralNetwork
+import torch.nn.functional as F
+
+# Page title
+st.set_page_config(page_title='Name Generator')
+st.title('Name Generator')
+
+# Select Model - drop down
+model_list = [
+    'Random model',
+  'Bigram model'
+]
+model_name = st.selectbox('Select an example query:', model_list)
+
+# Number of outputs - input field
+num_results = st.number_input("Number of Names to be Generated", min_value=1, max_value=50) 
+
+# Process
+# get weights
+with st.form('myform', clear_on_submit=True):
+
+    submitted = st.form_submit_button('Submit')
+
+    if submitted:
+        # get current path
+        get_cwd = os.getcwd()
+        project_dir = get_cwd
+        models_path = os.path.join(project_dir, 'models')
+
+        if model_name == 'Bigram model':
+            w = torch.load(os.path.join(models_path, 'bigram-USA.pt'))
+        elif model_name == 'Random model':
+            w = torch.ones(27,27) * 0.01
+
+        for i in range(num_results):
+            ix = 0
+            name=""
+            y = torch.Generator().manual_seed(2147483647)
+            while True:
+                nn = NeuralNetwork(50, 2147483647)
+
+                xenc = F.one_hot(torch.tensor([ix]), num_classes=27).float() # input to the network one hot encodding
+                logits = xenc @ w
+                counts = logits.exp()
+                probs = counts / counts.sum(1, keepdims=True)
+
+                ix = torch.multinomial(probs, num_samples=1, replacement=True).item()
+                name += nn.itos[ix]
+                if nn.itos[ix] ==".":
+                    break
+            st.write(name)
+
+
+
+