update

app.py

@@ -1,6 +1,44 @@
 import streamlit as st
+import torch
+import numpy as np
+from transformers import TrainingArguments, \
+                         Trainer, AutoTokenizer, DataCollatorWithPadding, \
+                         AutoModelForSequenceClassification 
+def print_probs(logits):
+  probs = torch.nn.functional.softmax(logits, dim=0).numpy()*100
+  ans = list(zip(probs,labels))
+  ans.sort(reverse=True)
+  sum = 0
+  i = 0
+  while sum <= 95:
+    prob, idx = ans[i]
+    text = categories[idx] + ": "+ str(np.round(prob,1) + "%"
+    st.markdown(text)
+    sum+=prob
+    i+=1
+
+def make_prediction(text):
+  tokenized_text = tokenizer(text, return_tensors='pt')
+  with torch.no_grad():
+    pred_logits = model(**tokenized_text).logits
+  st.markdown("Predictions:")
+  print_probs(pred_logits[0])
+
+
+
+tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
+
+model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=8)
+model_name = train_model2
+model_path = model_name + '.zip'
+model.load_state_dict(
+    torch.load(
+        model_path
+    )
+)
 
 st.markdown("##Hello, people!")
 st.markdown("<img src='https://centroderecursosmarista.org/wp-content/uploads/2013/05/arvix.jpg'>", unsafe_allow_html=True)
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+text = st.text_area("Введите описание статьи")
+make_prediction(text)
+