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import streamlit as st |
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from transformers import pipeline |
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from io import StringIO |
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unmasker = pipeline('fill-mask', model='dsfsi/zabantu-ven-120m') |
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st.set_page_config(layout="wide") |
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def fill_mask(sentences): |
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results = {} |
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warnings = [] |
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for sentence in sentences: |
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if "<mask>" in sentence: |
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unmasked = unmasker(sentence) |
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results[sentence] = unmasked |
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else: |
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warnings.append(f"Warning: No <mask> token found in sentence: {sentence}") |
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return results, warnings |
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def replace_mask(sentence, predicted_word): |
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return sentence.replace("<mask>", f"**{predicted_word}**") |
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st.write(f"") |
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img1, img2, img3 = st.columns(3) |
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with img2: |
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with st.container(border=False): |
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st.image("logo_transparent_small.png") |
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st.markdown(""" |
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<div style='text-align: center;'> |
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<a href='https://github.com/dsfsi' target='_blank'>Github</a> | |
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<a href='https://docs.google.com/forms/d/e/1FAIpQLSf7S36dyAUPx2egmXbFpnTBuzoRulhL5Elu-N1eoMhaO7v10w/viewform' target='_blank'>Feedback Form</a> | |
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<a href='https://huggingface.co./papers/1911.02116' target='_blank'>arxiv</a> |
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</div> |
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""", unsafe_allow_html=True) |
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st.markdown(""" |
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<div style='text-align: center;'> |
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<h2>Fill Mask | Zabantu-ven-120m</h2> |
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</div> |
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""", unsafe_allow_html=True) |
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st.write(f"") |
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st.markdown("This is a variant of Zabantu pre-trained on a monolingual dataset of Tshivenda(ven) sentences on a transformer network with 120 million traininable parameters.") |
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with st.expander("More information about the space"): |
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st.write(''' |
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Authors: Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, Veselin Stoyanov |
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''') |
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cit1,cit2 = st.columns(2) |
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col1, col2 = st.columns(2) |
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if 'text_input' not in st.session_state: |
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st.session_state['text_input'] = "" |
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if 'warnings' not in st.session_state: |
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st.session_state['warnings'] = [] |
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with col1: |
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with st.container(border=True): |
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st.markdown("Input :clipboard:") |
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select_options = ['Choose option', 'Enter text input', 'Upload a file(csv/txt)'] |
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sample_sentence = "Vhana vhane vha kha ḓi bva u bebwa vha kha khombo ya u <mask> nga Listeriosis." |
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option_selected = st.selectbox(f"Select an input option:", select_options, index=0) |
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if option_selected == 'Enter text input': |
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text_input = st.text_area( |
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"Enter sentences with <mask> token(one sentence per line):", |
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value=st.session_state['text_input'] |
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) |
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input_sentences = text_input.split("\n") |
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if st.button("Submit",use_container_width=True): |
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result, warnings = fill_mask(input_sentences) |
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st.session_state['warnings'] = warnings |
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if option_selected == 'Upload a file(csv/txt)': |
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uploaded_file = st.file_uploader("Choose a file-(one sentence per line)") |
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if uploaded_file is not None: |
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stringio = StringIO(uploaded_file.getvalue().decode("utf-8")) |
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string_data = stringio.read() |
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input_sentences = string_data.split("\n") |
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if st.button("Submit",use_container_width=True): |
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result, warnings = fill_mask(input_sentences) |
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st.session_state['warnings'] = warnings |
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if st.session_state['warnings']: |
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for warning in st.session_state['warnings']: |
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st.warning(warning) |
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st.markdown("Example") |
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st.code(sample_sentence, wrap_lines=True) |
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if st.button("Test Example",use_container_width=True): |
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result, warnings = fill_mask(sample_sentence.split("\n")) |
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with col2: |
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with st.container(border=True): |
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st.markdown("Output :bar_chart:") |
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if 'result' in locals() and result: |
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if len(result) == 1: |
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for sentence, predictions in result.items(): |
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for prediction in predictions: |
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predicted_word = prediction['token_str'] |
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score = prediction['score'] * 100 |
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st.markdown(f""" |
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<div class="bar"> |
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<div class="bar-fill" style="width: {score}%;"></div> |
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</div> |
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<div class="container"> |
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<div style="align-items: left;">{predicted_word}</div> |
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<div style="align-items: center;">{score:.2f}%</div> |
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</div> |
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""", unsafe_allow_html=True) |
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else: |
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index = 0 |
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for sentence, predictions in result.items(): |
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index += 1 |
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if predictions: |
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top_prediction = predictions[0] |
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predicted_word = top_prediction['token_str'] |
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score = top_prediction['score'] * 100 |
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st.markdown(f""" |
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<div class="bar"> |
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<div class="bar-fill" style="width: {score}%;"></div> |
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</div> |
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<div class="container"> |
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<div style="align-items: left;">{predicted_word} (line {index})</div> |
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<div style="align-items: right;">{score:.2f}%</div> |
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</div> |
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""", unsafe_allow_html=True) |
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if 'result' in locals(): |
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if result: |
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line = 0 |
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for sentence, predictions in result.items(): |
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line += 1 |
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predicted_word = predictions[0]['token_str'] |
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full_sentence = replace_mask(sentence, predicted_word) |
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st.write(f"**Sentence {line}:** {full_sentence }") |
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css = """ |
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<style> |
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footer {display:none !important;} |
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.gr-button-primary { |
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z-index: 14; |
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height: 43px; |
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width: 130px; |
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left: 0px; |
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top: 0px; |
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padding: 0px; |
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cursor: pointer !important; |
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background: none rgb(17, 20, 45) !important; |
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border: none !important; |
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text-align: center !important; |
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font-family: Poppins !important; |
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font-size: 14px !important; |
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font-weight: 500 !important; |
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color: rgb(255, 255, 255) !important; |
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line-height: 1 !important; |
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border-radius: 12px !important; |
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transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important; |
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box-shadow: none !important; |
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} |
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.gr-button-primary:hover{ |
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z-index: 14; |
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height: 43px; |
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width: 130px; |
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left: 0px; |
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top: 0px; |
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padding: 0px; |
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cursor: pointer !important; |
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background: none rgb(66, 133, 244) !important; |
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border: none !important; |
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text-align: center !important; |
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font-family: Poppins !important; |
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font-size: 14px !important; |
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font-weight: 500 !important; |
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color: rgb(255, 255, 255) !important; |
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line-height: 1 !important; |
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border-radius: 12px !important; |
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transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important; |
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box-shadow: rgb(0 0 0 / 23%) 0px 1px 7px 0px !important; |
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} |
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.hover\:bg-orange-50:hover { |
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--tw-bg-opacity: 1 !important; |
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background-color: rgb(229,225,255) !important; |
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} |
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.to-orange-200 { |
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--tw-gradient-to: rgb(37 56 133 / 37%) !important; |
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} |
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.from-orange-400 { |
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--tw-gradient-from: rgb(17, 20, 45) !important; |
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--tw-gradient-to: rgb(255 150 51 / 0); |
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--tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to) !important; |
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} |
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.group-hover\:from-orange-500{ |
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--tw-gradient-from:rgb(17, 20, 45) !important; |
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--tw-gradient-to: rgb(37 56 133 / 37%); |
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--tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to) !important; |
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} |
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.group:hover .group-hover\:text-orange-500{ |
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--tw-text-opacity: 1 !important; |
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color:rgb(37 56 133 / var(--tw-text-opacity)) !important; |
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} |
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.container { |
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display: flex; |
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justify-content: space-between; |
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align-items: center; |
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margin-bottom: 5px; |
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width: 100%; |
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} |
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.bar { |
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# width: 70%; |
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background-color: #e6e6e6; |
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border-radius: 12px; |
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overflow: hidden; |
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margin-right: 10px; |
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height: 5px; |
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} |
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.bar-fill { |
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background-color: #17152e; |
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height: 100%; |
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border-radius: 12px; |
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} |
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</style> |
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""" |
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st.markdown(css, unsafe_allow_html=True) |
