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Update app.py
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app.py
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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="Smart Text Summarizer", layout="centered")
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st.markdown(
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"""
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<style>
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.main {
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background-color: #f7f9fc;
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}
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.block-container {
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padding-top: 2rem;
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padding-bottom: 2rem;
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}
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.stButton>button {
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color: white;
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font-weight: bold;
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border-radius: 0.5rem;
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padding: 0.6rem 1.2rem;
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}
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.stTextArea textarea {
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border-radius: 0.5rem;
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padding: 1rem;
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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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# Centered title and subtitle
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st.markdown(
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"""
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<h1 style='text-align: center; font-weight: 700; margin-bottom: 0.5rem;'>
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Smart Text Summarizer 🧠
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</h1>
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<p style='text-align: center; color: #555; font-size: 18px; margin-top: 0;'>
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Generate concise and clear summaries with fine-tuned T5 models
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</p>
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""",
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unsafe_allow_html=True
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)
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# Model selection keys (simple)
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model_options = {
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"T5-Small": "NeonSamurai/summarization_t5_small_v2",
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"T5-Base": "NeonSamurai/summarization_cnndaily_t5_base"
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}
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model_choice_key = st.selectbox("Choose a model:", list(model_options.keys()))
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model_name = model_options[model_choice_key]
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@st.cache_resource
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def load_pipeline(model_name):
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return pipeline(
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"text2text-generation",
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model=model_name,
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tokenizer=model_name,
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max_length=256, # fixed max length
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min_length=60,
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truncation=True,
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do_sample=False
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)
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summarizer = load_pipeline(model_name)
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st.subheader("📄 Enter the text to summarize:")
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input_text = st.text_area("", height=300, placeholder="Paste or type your text here...")
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if st.button("✨ Generate Summary"):
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if not input_text.strip():
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st.warning("Please enter some text before summarizing.")
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else:
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with st.spinner("Summarizing..."):
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result = summarizer("summarize: " + input_text)
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summary = result[0]["generated_text"]
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st.subheader("🧾 Summary Result")
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st.success(summary.strip())
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# Background-Image
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st.markdown(
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"""
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<style>
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.stApp {
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background-image: url("https://cdn-uploads.huggingface.co/production/uploads/673f5e166c2774fcc8a82f0b/tjZJvZGkJMLKhorQFfHML.png");
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background-size: cover;
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background-position: center;
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height: 100vh;
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}
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/* Semi-transparent overlay */
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.stApp::before {
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content: "";
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position: absolute;
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top: 0;
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left: 0;
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width: 100%;
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height: 100%;
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background: rgba(0, 0, 0, 0.4); /* 40% transparency */
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z-index: -1;
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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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import streamlit as st
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from transformers import pipeline
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st.set_page_config(page_title="Smart Text Summarizer", layout="centered")
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st.markdown(
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"""
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<style>
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.main {
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background-color: #f7f9fc;
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}
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.block-container {
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padding-top: 2rem;
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padding-bottom: 2rem;
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}
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.stButton>button {
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color: white;
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font-weight: bold;
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border-radius: 0.5rem;
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padding: 0.6rem 1.2rem;
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}
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.stTextArea textarea {
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border-radius: 0.5rem;
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padding: 1rem;
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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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# Centered title and subtitle
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st.markdown(
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"""
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<h1 style='text-align: center; font-weight: 700; margin-bottom: 0.5rem;'>
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Smart Text Summarizer 🧠
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</h1>
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<p style='text-align: center; color: #555; font-size: 18px; margin-top: 0;'>
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Generate concise and clear summaries with fine-tuned T5 models
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</p>
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""",
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unsafe_allow_html=True
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)
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+
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# Model selection keys (simple)
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model_options = {
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"T5-Small": "NeonSamurai/summarization_t5_small_v2",
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"T5-Base": "NeonSamurai/summarization_cnndaily_t5_base"
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}
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model_choice_key = st.selectbox("Choose a model:", list(model_options.keys()))
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model_name = model_options[model_choice_key]
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@st.cache_resource
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def load_pipeline(model_name):
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return pipeline(
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"text2text-generation",
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model=model_name,
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tokenizer=model_name,
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max_length=256, # fixed max length
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min_length=60,
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truncation=True,
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do_sample=False
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)
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summarizer = load_pipeline(model_name)
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st.subheader("📄 Enter the text to summarize:")
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input_text = st.text_area("", height=300, placeholder="Paste or type your text here...")
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if st.button("✨ Generate Summary"):
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if not input_text.strip():
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st.warning("Please enter some text before summarizing.")
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else:
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with st.spinner("Summarizing..."):
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result = summarizer("summarize: " + input_text)
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summary = result[0]["generated_text"]
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st.subheader("🧾 Summary Result")
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st.success(summary.strip())
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# Background-Image
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st.markdown(
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"""
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<style>
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.stApp {
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background-image: url("https://cdn-uploads.huggingface.co/production/uploads/673f5e166c2774fcc8a82f0b/tjZJvZGkJMLKhorQFfHML.png");
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background-size: cover;
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background-position: center;
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height: 100vh;
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}
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/* Semi-transparent overlay */
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.stApp::before {
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content: "";
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position: absolute;
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top: 0;
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left: 0;
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width: 100%;
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height: 100%;
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background: rgba(0, 0, 0, 0.4); /* 40% transparency */
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z-index: -1;
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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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# Footer message
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st.markdown(
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"""
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<div class="footer-message">
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⚠️ This app may run slower on Hugging Face spaces. For a faster experience, please visit:
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<a href="https://summarization-cnndaily-t5.streamlit.app/" target="_blank">https://summarization-cnndaily-t5.streamlit.app/</a>
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</div>
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""",
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unsafe_allow_html=True
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)
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