changed the page seq

pages/Name_Generator.py

@@ -1,59 +0,0 @@
-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)
-
-
-
-