Create app.py

app.py

@@ -0,0 +1,46 @@
+import streamlit as st
+import pickle
+import torch
+import numpy as np
+from transformers import TrainingArguments, Trainer, AutoTokenizer, DataCollatorWithPadding, AutoModelForSequenceClassification 
+from PIL import Image
+
+
+# @st.cache
+def predict_topic_by_title_and_abstract(text):
+    tokenized_text = tokenizer(text, return_tensors='pt')
+    with torch.no_grad():
+        logits = model(**tokenized_text).logits
+    probs = torch.nn.functional.softmax(logits[0], dim=0).numpy() * 100
+    ans = list(zip(probs,labels.values()))
+    ans.sort(reverse=True)
+    sum = 0
+    i = 0
+    while sum <= 95:
+        prob, label = ans[i]
+        st.write("it's topic \"" label + "\" with probability "+ str(np.round(prob,1)) + "%")
+        sum += prob
+        i += 1
+
+tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
+
+model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=8)
+model.load_state_dict(
+    torch.load(
+        "./trained_model"
+    )
+)
+
+image = Image.open('logo.png')
+
+st.image(image)
+st.markdown("### To get an article topic prediction, please write down it's title, abstract, or both.")
+
+title = st.text_area("Write article title:", height=30)
+
+abstract = st.text_area("Write article abstract:", height=60)
+
+input_text = title + " " + abstract 
+
+if input_text != "":
+    predict_topic_by_title_and_abstract(input_text)