Create app.py

app.py

@@ -0,0 +1,51 @@
+import streamlit as st
+import numpy as np
+import pandas as pd
+import os
+import torch
+import torch.nn as nn
+from transformers.activations import get_activation
+from transformers import AutoTokenizer, AutoModelWithLMHead
+
+
+st.title('GPT2:')
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+@st.cache(allow_output_mutation=True)
+def get_model():
+    tokenizer = AutoTokenizer.from_pretrained("BigSalmon/MASKGPT2")
+    model = AutoModelWithLMHead.from_pretrained("BigSalmon/MASKGPT2")
+    return model, tokenizer
+    
+model, tokenizer = get_model()
+
+g = """
+***
+
+original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick. 
+infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick. 
+
+***
+
+original:"""
+
+with st.form(key='my_form'):
+    prompt = st.text_area(label='Enter sentence', value=g)
+    submit_button = st.form_submit_button(label='Submit')
+
+    if submit_button:
+      with torch.no_grad():
+        text = tokenizer.encode(prompt)
+        myinput, past_key_values = torch.tensor([text]), None
+        myinput = myinput
+        myinput= myinput.to(device)
+        logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
+        logits = logits[0,-1]
+        probabilities = torch.nn.functional.softmax(logits)
+        best_logits, best_indices = logits.topk(300)
+        best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
+        text.append(best_indices[0].item())
+        best_probabilities = probabilities[best_indices].tolist()
+        words = []              
+        st.write(best_words)