Create app.py

app.py

@@ -0,0 +1,59 @@
+import torch
+
+import streamlit as st
+import torch.nn as nn
+from transformers import RobertaTokenizer, RobertaModel
+
+@st.cache(suppress_st_warning=True)
+def init_tokenizer():
+    tokenizer = RobertaTokenizer.from_pretrained("roberta-large-mnli")
+    return tokenizer
+
+
+@st.cache(suppress_st_warning=True)
+def init_model():
+    model = RobertaModel.from_pretrained("roberta-large-mnli")
+
+    model.pooler = nn.Sequential(
+        nn.Linear(1024, 256),
+        nn.LayerNorm(256),
+        nn.ReLU(),
+        nn.Linear(256, 8),
+        nn.Sigmoid()
+    )
+    
+    model_path = 'model.pt'
+    model.load_state_dict(torch.load(model_path, map_location=torch.device("cpu")))
+
+cats = ['Computer Science', 'Economics', 'Electrical Engineering', 
+        'Mathematics', 'Physics', 'Biology', 'Finance', 'Statistics']
+
+def predict(outputs):
+    top = 0
+    probs = nn.functional.softmax(outputs, dim=1).tolist()[0]
+
+    for prob, cat in sorted(zip(probs, cats), reverse=True):
+        if top < 95:
+            percent = prob * 100
+            top += percent
+            st.write(f'{cat}: {round(percent, 1)}')
+
+        
+st.markdown("### Title")
+
+title = st.text_area("Enter title", height=20)
+
+st.markdown("### Abstract")
+
+abstract = st.text_area("Enter abstract", height=200)
+
+if not title:
+    st.warning("Please fill out so required fields")
+else:    
+    tokenizer = init_tokenizer()
+    model = init_model()
+
+    encoded_input = tokenizer(title + '. ' + abstract, return_tensors='pt', padding=True, 
+                          max_length = 512, truncation=True)
+    outputs = model(**encoded_input).pooler_output[:, 0, :]
+    predict(outputs)