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from operator import setitem |
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from pathlib import Path |
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import streamlit as st |
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from transformers import AutoModelForSequenceClassification |
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from transformers import AutoTokenizer |
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from transformers import TextClassificationPipeline |
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@st.cache_data() |
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def load_model(): |
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model = AutoModelForSequenceClassification.from_pretrained( |
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"issai/rembert-sentiment-analysis-polarity-classification-kazakh") |
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tokenizer = AutoTokenizer.from_pretrained("issai/rembert-sentiment-analysis-polarity-classification-kazakh") |
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return TextClassificationPipeline(model=model, tokenizer=tokenizer) |
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st.title('KazSAnDRA') |
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static_folder = Path(__file__).parent / 'static' |
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assert static_folder.exists() |
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st.write((static_folder / 'description.txt').read_text()) |
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st.image(str(static_folder / 'kazsandra.jpg')) |
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pipe = load_model() |
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with st.form('main_form'): |
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input_text = st.text_area('Input text', placeholder='Provide your text, e.g. "Осы кітап қызық сияқты".') |
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is_submitted = st.form_submit_button(label='Submit') |
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if is_submitted: |
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if input_text: |
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out = pipe(input_text)[0] |
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st.text("Label: {label}\nScore: {score}".format(**out)) |
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else: |
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st.text("Please provide your text first.") |
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