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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)
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