update app.py

app.py

@@ -7,9 +7,8 @@ from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
 st.markdown("# Hello, friend!")
 st.markdown(" This magic application going to help you with understanding of science paper topic! Cool? Yeah! ")
-# st.markdown("<img width=200px src='https://rozetked.me/images/uploads/dwoilp3BVjlE.jpg'>", unsafe_allow_html=True)
 
-st.write("Loading tokenizer and dict")
+# st.write("Loading tokenizer and dict")
 model_name_global = "allenai/scibert_scivocab_uncased"
 tokenizer_ = AutoTokenizer.from_pretrained(model_name_global)
 with open('./models/scibert/decode_dict.pkl', 'rb') as f:
@@ -18,32 +17,7 @@ with open('./models/scibert/decode_dict.pkl', 'rb') as f:
 with st.form(key="my_form"):
     st.markdown("### 🎈 Do you want a little magic?  ")
     st.markdown(" Write your article title and abstract to textboxes bellow and I'll gues topic of your paper!  ")
-    # ce, c1, ce, c2, c3 = st.columns([0.07, 1, 0.07, 5, 0.07])
-    ce, c2, c3 = st.columns([0.07, 5, 0.07])
-    # with c1:
-    #     ModelType = st.radio(
-    #         "Choose your model",
-    #         ["DistilBERT (Default)", "Flair"],
-    #         help="At present, you can choose between 2 models (Flair or DistilBERT) to embed your text. More to come!",
-    #     )
-    #
-    #     if ModelType == "Default (DistilBERT)":
-    #         # kw_model = KeyBERT(model=roberta)
-    #
-    #         @st.cache(allow_output_mutation=True)
-    #         def load_model():
-    #             return KeyBERT(model=roberta)
-    #
-    #
-    #         kw_model = load_model()
-    #
-    #     else:
-    #         @st.cache(allow_output_mutation=True)
-    #         def load_model():
-    #             return KeyBERT("distilbert-base-nli-mean-tokens")
-    #
-    #
-    #         kw_model = load_model()
+    ce, c2, c3 = st.columns([0.07, 6, 0.07])
 
     with c2:
         doc_title = st.text_area(
@@ -115,7 +89,7 @@ title = doc_title
 abstract = doc_abstract
 tokens = tokenizer_(title + abstract, return_tensors="pt")
 
-predicts = make_predict(model_name_global, model_local, tokens, decode_dict, title, abstract)
+predicts = make_predict(tokens, decode_dict)
 
 st.markdown("## 🎈 Yor article probably about:  ")
 st.header("")