Upload app.py

app.py

@@ -0,0 +1,126 @@
+import streamlit as st
+import numpy as np
+import pandas as pd
+import torch
+import transformers
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from scipy.special import softmax
+
+
+
+def load_model():
+    model = AutoModelForSequenceClassification.from_pretrained('model_distilbert_trained')
+    tokenizer = AutoTokenizer.from_pretrained(
+        'distilbert-base-cased', do_lower_case=True)
+    model.eval()
+    return model, tokenizer
+    
+
+def get_predictions(logits, indexes):
+    sum = 0
+    ind = []
+    probs = []
+    for i in indexes:
+        sum += logits[i]
+        ind.append(i)
+        probs.append(indexes[i])
+        if sum >= 0.95:
+            return ind, probs
+
+
+def return_pred_name(name_dict, ind):
+    out = []
+    for i in ind:
+        out.append(name_dict[i])
+    return out
+
+
+def predict(title, summary, model, tokenizer):
+    text = title + '.' + summary
+    tokens = tokenizer.encode(text)
+    with torch.no_grad():
+        logits = model(torch.as_tensor([tokens]))[0]
+        probs = torch.softmax(logits[-1, :], dim=-1).data.cpu().numpy()
+        
+    classes = np.flip(np.argsort(probs))
+    sum_probs = 0
+    ind = 0
+    prediction = []
+    prediction_probs = []
+    while sum_probs < 0.95:
+        prediction.append(name_dict[classes[ind]])
+        prediction_probs.append(str("{:.2f}".format(100 * probs[classes[ind]])) + "%")
+        sum_probs += probs[classes[ind]]
+        ind += 1
+    
+    return prediction, prediction_probs
+
+
+def get_results(prediction, prediction_probs):
+    frame =  pd.DataFrame({'Category': prediction, 'Confidence': prediction_probs})
+    frame.index = np.arange(1, len(frame) + 1)
+    return frame
+    
+name_dict = {4: 'cs',
+ 19: 'stat',
+ 1: 'astro-ph',
+ 16: 'q-bio',
+ 6: 'eess',
+ 3: 'cond-mat',
+ 12: 'math',
+ 15: 'physics',
+ 18: 'quant-ph',
+ 17: 'q-fin',
+ 7: 'gr-qc',
+ 13: 'nlin',
+ 2: 'cmp-lg',
+ 5: 'econ',
+ 8: 'hep-ex',
+ 11: 'hep-th',
+ 14: 'nucl-th',
+ 10: 'hep-ph',
+ 9: 'hep-lat',
+ 0: 'adap-org'}
+
+
+
+st.title("Find out the topic of the article without reading!")
+st.markdown("<h1 style='text-align: center;'><img width=320px src = 'https://upload.wikimedia.org/wikipedia/ru/8/81/Sheldon_cooper.jpg'>",
+    unsafe_allow_html=True)
+# ^-- можно показывать пользователю текст, картинки, ограниченное подмножество html - всё как в jupyter
+
+title = st.text_area(label='Title',
+                            value='',
+                            height=30,
+                            help='If you know a title type it here')
+                            
+
+summary = st.text_area(label='Summary',
+                            value='',
+                            height=200,
+                            help='If you have a summary enter it here')
+                            
+                       
+button = st.button(label='Get the theme!')
+
+if button:
+    if (title == '' and summary == ''):
+        st.write('There is nothing to analyze...')
+        st.write('Fill at list one of the fields')
+    else:
+        model, tokenizer = load_model()
+        prediction, prediction_probs = predict(title, summary, model, tokenizer)
+        ans = get_results(prediction, prediction_probs)
+        st.write('Result')
+        st.write(ans)
+
+
+# ^-- показать текстовое поле. В поле text лежит строка, которая находится там в данный момент
+
+#from transformers import pipeline
+#pipe = pipeline("ner", "Davlan/distilbert-base-multilingual-cased-ner-hrl")
+#raw_predictions = pipe(text)
+# тут уже знакомый вам код с huggingface.transformers -- его можно заменить на что угодно от fairseq до catboost
+
+#st.markdown(f"{raw_predictions}")
+# выводим результаты модели в текстовое поле, на потеху пользователю