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import streamlit as st |
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import plotly.graph_objects as go |
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from mT5Model import runModel |
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text_1 = """Bilim insanları Botsvana’da Covid-19’un şu ana kadar en çok mutasyona uğramış varyantını tespit etti. \ |
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Resmi olarak B.1.1.529 koduyla bilinen bu varyantı ise “Nu varyantı” adı verildi. Uzmanlar bu varyant içerisinde \ |
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tam 32 farklı mutasyon tespit edildiğini açıklarken, bu virüsün corona virüsü aşılarına karşı daha dirençli olabileceğini duyurdu.""" |
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text_2 = """Argentina beat Australia 2-1 on Saturday and will take on the Netherlands in the World Cup quarterfinals. \ |
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It was a historic night for Lionel Messi as the Argentine superstar took to the pitch for his 1,000th match for club and country. \ |
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He also scored in the match. Messi scored the opening goal in the 35th minute as his low shot in the box beat Australian goalkeeper Mathew Ryan.""" |
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@st.cache(allow_output_mutation=True) |
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def list2text(label_list): |
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labels = "" |
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for label in label_list: |
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labels = labels + label + "," |
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labels = labels[:-1] |
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return labels |
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label_list_1 = ["dünya", "ekonomi", "kültür", "sağlık", "siyaset", "spor", "teknoloji"] |
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label_list_2 = ["positive", "negative", "neutral"] |
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hypothesis_1 = "Bu yazı {} konusundadır" |
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hypothesis_2 = "This text is in {} subject" |
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st.title("Multilingual Zero-Shot Text Classification with mT5") |
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model_name = "alan-turing-institute/mt5-large-finetuned-mnli-xtreme-xnli" |
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st.sidebar.write("For details of used model:") |
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st.sidebar.write("https://huggingface.co/alan-turing-institute/mt5-large-finetuned-mnli-xtreme-xnli") |
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st.sidebar.write("For Xtreme XNLI Dataset:") |
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st.sidebar.write("https://www.tensorflow.org/datasets/catalog/xtreme_xnli") |
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st.subheader("Select Text, Label List and Hyphothesis") |
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st.text_area("Text #1", text_1, height=128) |
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st.text_area("Text #2", text_2, height=128) |
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st.write(f"Label List #1: {list2text(label_list_1)}") |
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st.write(f"Label List #2: {list2text(label_list_2)}") |
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st.write(f"Hypothesis #1: {hypothesis_1}") |
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st.write(f"Hypothesis #2: {hypothesis_2}") |
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text = st.radio("Select Text", ("Text #1", "Text #2", "New Text")) |
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labels = st.radio("Select Label List", ("Label List #1", "Label List #2", "New Label List")) |
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hypothesis = st.radio("Select Hypothesis", ("Hypothesis #1", "Hypothesis #2", "New Hypothesis")) |
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if text == "Text #1": sequence_to_classify = text_1 |
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elif text == "Text #2": sequence_to_classify = text_2 |
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elif text == "New Text": |
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sequence_to_classify = st.text_area("New Text", value="", height=128) |
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if labels == "Label List #1": candidate_labels = label_list_1 |
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elif labels == "Label List #2": candidate_labels = label_list_2 |
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elif labels == "New Label List": |
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candidate_labels = st.text_area("New Label List (Pls Input as comma-separated)", value="", height=16).split(",") |
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if hypothesis == "Hypothesis #1": hypothesis_template = hypothesis_1 |
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elif hypothesis == "Hypothesis #2": hypothesis_template = hypothesis_2 |
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elif labels == "New Hypothesis": |
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hypothesis_template = st.text_area("Hypothesis Template for NLI (Pls use similar format of examples)", value="", height=16) |
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Run_Button = st.button("Run", key=None) |
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if Run_Button == True: |
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with st.spinner('Model is running...'): |
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output = runModel(model_name, sequence_to_classify, candidate_labels, hypothesis_template) |
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output_labels = list(output.keys()) |
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output_scores = list(output.values()) |
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st.header("Result") |
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fig = go.Figure([go.Bar(x=output_labels, y=output_scores)]) |
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st.plotly_chart(fig, use_container_width=False, sharing="streamlit") |
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st.success('Done!') |
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