Copy app,py

app.py

@@ -0,0 +1,44 @@
+import streamlit as st
+import numpy as np
+import pandas as pd
+
+import transformers
+from transformers import TextClassificationPipeline, AutoTokenizer, AutoModelForSequenceClassification   
+
+@st.cache(suppress_st_warning=True, hash_funcs={transformers.AutoTokenizer: lambda _: None})
+def load_tok_and_model(): 
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForSequenceClassification.from_pretrained(".")
+    return tokenizer, model
+
+
+CATEGORIES = ["Computer Science", "Economics", "Electrical Engineering", "Mathematics",
+ "Q. Biology", "Q. Finances", "Statistics" , "Physics"]
+    
+    
+@st.cache(suppress_st_warning=True, hash_funcs={transformers.AutoTokenizer: lambda _: None})  
+def forward_pass(title, abstract, tokenizer, model):    
+    title_tensor = torch.tensor(tokenizer(title, padding="max_length", truncation=True, max_length=32)['input_ids'])
+    abstract_tensor = torch.tensor(tokenizer(abstract, padding="max_length", truncation=True, max_length=480)['input_ids'])
+    
+    embeddings = torch.cat((title_tensor, abstract_tensor))
+    assert embeddings.shape == (512,)
+    with torch.no_grad():
+        logits = model(embeddings[None])['logits'][0]
+        assert logits.shape == (8,)
+        probs = torch.softmax(logits).data.cpu().numpy()
+    
+    return probs
+
+st.title("Classification of arXiv articles' main topic")
+st.markdown("Please provide both summary and title when possible")
+
+tokenizer, model = load_tok_and_model()
+
+title = st.text_area(label='Title', height=200)
+abstract = st.text_area(label='Abstract', height=200)
+button = st.button('Run classifier')
+
+if button:
+    probs = forward_pass(title, abstract, tokenizer, model)
+    st.write(probs)