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import streamlit as st |
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from transformers import pipeline |
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unmasker = pipeline('fill-mask', model='dsfsi/zabantu-nso-ven-170m') |
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def fill_mask(sentences): |
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results = {} |
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for sentence in sentences: |
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unmasked = unmasker(sentence) |
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results[sentence] = unmasked |
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return results |
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def replace_mask(sentence, predicted_word): |
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return sentence.replace("<mask>", predicted_word) |
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st.title("Fill Mask | Zabantu-nso-ven-170m) |
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st.write(f"") |
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col1, col2 = st.columns(2) |
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with col1: |
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sample_sentences = [ |
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"Vhana vhane vha kha ḓi bva u bebwa vha kha khombo ya u <mask> nga Listeriosis" |
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] |
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text_input = st.text_area( |
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"Enter sentences with <mask> token (one per line):", |
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"\n".join(sample_sentences) |
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) |
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input_sentences = text_input.split(",") |
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if st.button("Submit"): |
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result = fill_mask(input_sentences) |
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with col2: |
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if 'result' in locals(): |
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if result: |
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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: 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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for sentence, predictions in result.items(): |
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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:** {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: |
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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: |
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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) |