Create app.py

app.py

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+import torch
+import streamlit as st
+import numpy as np
+import pandas as pd
+import os
+import torch
+import torch.nn as nn
+from transformers import ElectraModel, AutoConfig, GPT2LMHeadModel
+from transformers.activations import get_activation
+from transformers import AutoTokenizer
+
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+artist_name = st.text_input("Model", "roberta-large")
+tokenizer = AutoTokenizer.from_pretrained("roberta-large")
+model = AutoModelForMaskedLM.from_pretrained(artist_name)
+
+
+
+with st.form(key='my_form'):
+    prompt = st.text_area(label='Enter Text. Put <mask> where you want the model to fill in the blank. You can use more than one at a time.')
+    submit_button = st.form_submit_button(label='Submit')
+
+    if submit_button:
+      a_list = []
+      token_ids = tokenizer.encode(prompt, return_tensors='pt')
+      token_ids_tk = tokenizer.tokenize(prompt, return_tensors='pt')
+      masked_position = (token_ids.squeeze() == tokenizer.mask_token_id).nonzero()
+      masked_pos = [mask.item() for mask in masked_position ]
+      with torch.no_grad():
+        output = model(token_ids)
+      last_hidden_state = output[0].squeeze()
+      for mask_index in masked_pos:
+        mask_hidden_state = last_hidden_state[mask_index]
+        idx = torch.topk(mask_hidden_state, k=100, dim=0)[1]
+        words = [tokenizer.decode(i.item()).strip() for i in idx]
+        st.text_area(label = 'Infill:', value=words)