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Update src/streamlit_app.py

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  1. src/streamlit_app.py +6 -6
src/streamlit_app.py CHANGED
@@ -39,7 +39,7 @@ st.markdown(
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  </p>
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  <p style="font-size:20px;">
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  πŸ“‘ <a href="https://arxiv.org/abs/2506.01241">Paper</a> | πŸ’» <a href="https://github.com/launchnlp/ExpertLongBench">GitHub</a> | πŸ€— <a href="https://huggingface.co/datasets/launch/ExpertLongBench">Public Dataset</a> |
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- βš™οΈ <strong>Version</strong>: <strong>V1</strong> | <strong># Models</strong>: 12 | Updated: <strong>June 2025</strong>
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  </p>
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  </div>
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  ''',
@@ -50,7 +50,7 @@ st.markdown(
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  @st.cache_data
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  def load_data(path):
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  df = pd.read_json(path, lines=True)
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- score_cols = [f"T{i}" for i in range(1, 12)]
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  df["Avg"] = df[score_cols].mean(axis=1).round(1)
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  # df["Avg"] = np.ceil(df[score_cols].mean(axis=1) * 10) / 10
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  # Compute rank per column (1 = best)
@@ -69,11 +69,11 @@ if selected_tier == 'F1':
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  df = load_data("src/models.json")
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  # Precompute max ranks for color scaling
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- score_cols = [f"T{i}" for i in range(1, 12)] + ["Avg"]
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  max_ranks = {col: df[f"{col}_rank"].max() for col in score_cols}
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  # Build raw HTML table
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- cols = ["Model"] + [f"T{i}" for i in range(1,12)] + ["Avg"]
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  html = "<table style='border-collapse:collapse; width:100%; font-size:14px;'>"
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  # header
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  html += "<tr>" + "".join(f"<th style='padding:6px;'>{col}</th>" for col in cols) + "</tr>"
@@ -104,11 +104,11 @@ else:
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  df2 = load_data("src/model_acc.json")
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  # Precompute max ranks for color scaling
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- score_cols = [f"T{i}" for i in range(1, 12)] + ["Avg"]
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  max_ranks = {col: df2[f"{col}_rank"].max() for col in score_cols}
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  # Build raw HTML table
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- cols = ["Model"] + [f"T{i}" for i in range(1,12)] + ["Avg"]
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  html = "<table style='border-collapse:collapse; width:100%; font-size:14px;'>"
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  # header
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  html += "<tr>" + "".join(f"<th style='padding:6px;'>{col}</th>" for col in cols) + "</tr>"
 
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  </p>
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  <p style="font-size:20px;">
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  πŸ“‘ <a href="https://arxiv.org/abs/2506.01241">Paper</a> | πŸ’» <a href="https://github.com/launchnlp/ExpertLongBench">GitHub</a> | πŸ€— <a href="https://huggingface.co/datasets/launch/ExpertLongBench">Public Dataset</a> |
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+ βš™οΈ <strong>Version</strong>: <strong>V1</strong> | <strong># Models</strong>: 13 | Updated: <strong>Sept 2025</strong>
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  </p>
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  </div>
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  ''',
 
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  @st.cache_data
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  def load_data(path):
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  df = pd.read_json(path, lines=True)
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+ score_cols = [f"T{i}" for i in range(1, 13)]
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  df["Avg"] = df[score_cols].mean(axis=1).round(1)
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  # df["Avg"] = np.ceil(df[score_cols].mean(axis=1) * 10) / 10
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  # Compute rank per column (1 = best)
 
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  df = load_data("src/models.json")
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  # Precompute max ranks for color scaling
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+ score_cols = [f"T{i}" for i in range(1, 13)] + ["Avg"]
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  max_ranks = {col: df[f"{col}_rank"].max() for col in score_cols}
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  # Build raw HTML table
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+ cols = ["Model"] + [f"T{i}" for i in range(1,13)] + ["Avg"]
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  html = "<table style='border-collapse:collapse; width:100%; font-size:14px;'>"
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  # header
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  html += "<tr>" + "".join(f"<th style='padding:6px;'>{col}</th>" for col in cols) + "</tr>"
 
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  df2 = load_data("src/model_acc.json")
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  # Precompute max ranks for color scaling
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+ score_cols = [f"T{i}" for i in range(1, 13)] + ["Avg"]
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  max_ranks = {col: df2[f"{col}_rank"].max() for col in score_cols}
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  # Build raw HTML table
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+ cols = ["Model"] + [f"T{i}" for i in range(1,13)] + ["Avg"]
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  html = "<table style='border-collapse:collapse; width:100%; font-size:14px;'>"
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  # header
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  html += "<tr>" + "".join(f"<th style='padding:6px;'>{col}</th>" for col in cols) + "</tr>"