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import streamlit as st |
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import altair as alt |
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import torch |
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from transformers import AlbertTokenizer, AlbertForSequenceClassification |
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import sentencepiece as spm |
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import pandas as pd |
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model_name = "albert-base-v2" |
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tokenizer = AlbertTokenizer.from_pretrained(model_name) |
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model = AlbertForSequenceClassification.from_pretrained(model_name) |
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def classify_text(text): |
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inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt") |
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outputs = model(**inputs) |
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logits = outputs.logits.detach().numpy()[0] |
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probabilities = torch.softmax(torch.tensor(logits), dim=0).tolist() |
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return probabilities |
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st.title("ALBERT Text Classification App") |
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default_text = "Streamlit-Altair: A component that allows the creation of Altair visualizations within Streamlit.\nStreamlit-Bokeh: A component that allows the creation of Bokeh visualizations within Streamlit.\nStreamlit-Plotly: A component that allows the creation of Plotly visualizations within Streamlit.\nStreamlit-Mapbox: A component that allows the creation of Mapbox maps within Streamlit.\nStreamlit-DeckGL: A component that allows the creation of Deck.GL visualizations within Streamlit.\nStreamlit-Wordcloud: A component that allows the creation of word clouds within Streamlit.\nStreamlit-Audio: A component that allows the playing of audio files within Streamlit.\nStreamlit-Video: A component that allows the playing of video files within Streamlit.\nStreamlit-EmbedCode: A component that allows the embedding of code snippets within Streamlit.\nStreamlit-Components: A component that provides a library of custom Streamlit components created by the Streamlit community." |
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text_input = st.text_area("Enter text to classify", default_text, height=200) |
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if st.button("Classify"): |
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if text_input: |
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probabilities = classify_text(text_input) |
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df = pd.DataFrame({ |
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'Label': ['Negative', 'Positive'], |
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'Probability': probabilities |
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}) |
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chart = alt.Chart(df).mark_bar().encode( |
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x='Probability', |
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y=alt.Y('Label', sort=['Negative', 'Positive']) |
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) |
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st.write(chart) |
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else: |
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st.write("Please enter some text to classify.") |
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