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739c6cd
update app.py
Browse files
app.py
CHANGED
@@ -7,9 +7,8 @@ from transformers import AutoTokenizer, AutoModelForSequenceClassification
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st.markdown("# Hello, friend!")
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st.markdown(" This magic application going to help you with understanding of science paper topic! Cool? Yeah! ")
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# st.markdown("<img width=200px src='https://rozetked.me/images/uploads/dwoilp3BVjlE.jpg'>", unsafe_allow_html=True)
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st.write("Loading tokenizer and dict")
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model_name_global = "allenai/scibert_scivocab_uncased"
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tokenizer_ = AutoTokenizer.from_pretrained(model_name_global)
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with open('./models/scibert/decode_dict.pkl', 'rb') as f:
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@@ -18,32 +17,7 @@ with open('./models/scibert/decode_dict.pkl', 'rb') as f:
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with st.form(key="my_form"):
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st.markdown("### π Do you want a little magic? ")
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st.markdown(" Write your article title and abstract to textboxes bellow and I'll gues topic of your paper! ")
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ce, c2, c3 = st.columns([0.07, 5, 0.07])
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# with c1:
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# ModelType = st.radio(
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# "Choose your model",
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# ["DistilBERT (Default)", "Flair"],
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# help="At present, you can choose between 2 models (Flair or DistilBERT) to embed your text. More to come!",
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# )
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#
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# if ModelType == "Default (DistilBERT)":
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# # kw_model = KeyBERT(model=roberta)
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#
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# @st.cache(allow_output_mutation=True)
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# def load_model():
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# return KeyBERT(model=roberta)
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#
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#
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# kw_model = load_model()
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#
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# else:
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# @st.cache(allow_output_mutation=True)
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# def load_model():
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# return KeyBERT("distilbert-base-nli-mean-tokens")
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#
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#
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# kw_model = load_model()
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with c2:
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doc_title = st.text_area(
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@@ -115,7 +89,7 @@ title = doc_title
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abstract = doc_abstract
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tokens = tokenizer_(title + abstract, return_tensors="pt")
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predicts = make_predict(
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st.markdown("## π Yor article probably about: ")
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st.header("")
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st.markdown("# Hello, friend!")
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st.markdown(" This magic application going to help you with understanding of science paper topic! Cool? Yeah! ")
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# st.write("Loading tokenizer and dict")
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model_name_global = "allenai/scibert_scivocab_uncased"
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tokenizer_ = AutoTokenizer.from_pretrained(model_name_global)
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with open('./models/scibert/decode_dict.pkl', 'rb') as f:
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with st.form(key="my_form"):
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st.markdown("### π Do you want a little magic? ")
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st.markdown(" Write your article title and abstract to textboxes bellow and I'll gues topic of your paper! ")
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ce, c2, c3 = st.columns([0.07, 6, 0.07])
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with c2:
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doc_title = st.text_area(
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abstract = doc_abstract
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tokens = tokenizer_(title + abstract, return_tensors="pt")
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predicts = make_predict(tokens, decode_dict)
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st.markdown("## π Yor article probably about: ")
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st.header("")
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