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import streamlit as st |
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from transformers import pipeline |
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st.set_page_config(page_title="Privacy Policy Relation Extraction", page_icon="🐢") |
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@st.cache_data |
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def get_results(text): |
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pipe = pipeline( |
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"text-classification", |
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model="PaDaS-Lab/privacy-policy-relation-extraction", |
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return_all_scores=True, |
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framework="pt", |
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) |
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return pipe(text) |
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st.title("Privacy Policy Relation Extraction") |
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example = st.text_area( |
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"Enter text:", |
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value="We store your basic account information, including your name, username, and email address until you ask us to delete them.", |
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height=150, |
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) |
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if st.button("Analyze"): |
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with st.spinner("Processing..."): |
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results = get_results(example) |
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st.session_state.results = results |
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if "results" in st.session_state: |
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threshold = st.slider( |
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"Confidence threshold:", min_value=0.0, max_value=1.0, value=0.5 |
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) |
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filtered_results = [ |
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result for result in st.session_state.results[0] if result["score"] >= threshold |
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] |
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sorted_results = sorted(filtered_results, key=lambda x: x["score"], reverse=True) |
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if sorted_results: |
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for result in sorted_results: |
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cols = st.columns([3, 5, 0.5]) |
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with cols[0]: |
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st.write(f"**{result['label']}**") |
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with cols[1]: |
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st.progress(int(result["score"] * 100)) |
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with cols[2]: |
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st.write(f"**{result['score']:.2f}**") |
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else: |
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st.warning("No relations found with the specified threshold.") |
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st.markdown( |
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""" |
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<style> |
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.reportview-container { |
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background: #f0f2f6; |
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padding: 20px; |
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} |
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.stButton button { |
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border: 0.5; |
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transition: background-color 0.3s, transform 0.2s; |
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border-radius: 10px; |
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box-shadow: 0 1px 3px rgba(0, 0, 0, 0.08); |
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} |
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.stButton button:hover { |
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transform: translateY(-1px); |
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} |
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</style> |
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""", |
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unsafe_allow_html=True, |
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) |
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