Upload app.py

app.py

@@ -0,0 +1,334 @@
+import io
+import re
+from collections.abc import Iterable
+
+import pandas as pd
+import streamlit as st
+from pandas.api.types import is_bool_dtype, is_datetime64_any_dtype, is_numeric_dtype
+
+GITHUB_URL = "https://github.com/msamwelmollel/Swahili_LLM_Leaderboard"
+NON_BENCHMARK_COLS = ["Open?", "Publisher"]
+
+
+def extract_table_and_format_from_markdown_text(markdown_table: str) -> pd.DataFrame:
+    """Extracts a table from a markdown text and formats it as a pandas DataFrame.
+    Args:
+        text (str): Markdown text containing a table.
+    Returns:
+        pd.DataFrame: Table as pandas DataFrame.
+    """
+    df = (
+        pd.read_table(io.StringIO(markdown_table), sep="|", header=0, index_col=1)
+        .dropna(axis=1, how="all")  # drop empty columns
+        .iloc[1:]  # drop first row which is the "----" separator of the original markdown table
+        .sort_index(ascending=True)
+        .apply(lambda x: x.str.strip() if x.dtype == "object" else x)
+        .replace("", float("NaN"))
+        .astype(float, errors="ignore")
+    )
+
+    # remove whitespace from column names and index
+    df.columns = df.columns.str.strip()
+    df.index = df.index.str.strip()
+    df.index.name = df.index.name.strip()
+
+    return df
+
+
+def extract_markdown_table_from_multiline(multiline: str, table_headline: str, next_headline_start: str = "#") -> str:
+    """Extracts the markdown table from a multiline string.
+    Args:
+        multiline (str): content of README.md file.
+        table_headline (str): Headline of the table in the README.md file.
+        next_headline_start (str, optional): Start of the next headline. Defaults to "#".
+    Returns:
+        str: Markdown table.
+    Raises:
+        ValueError: If the table could not be found.
+    """
+    # extract everything between the table headline and the next headline
+    table = []
+    start = False
+    for line in multiline.split("\n"):
+        if line.startswith(table_headline):
+            start = True
+        elif line.startswith(next_headline_start):
+            start = False
+        elif start:
+            table.append(line + "\n")
+
+    if len(table) == 0:
+        raise ValueError(f"Could not find table with headline '{table_headline}'")
+
+    return "".join(table)
+
+
+def remove_markdown_links(text: str) -> str:
+    """Modifies a markdown text to remove all markdown links.
+    Example: [DISPLAY](LINK) to DISPLAY
+    First find all markdown links with regex.
+    Then replace them with: $1
+    Args:
+        text (str): Markdown text containing markdown links
+    Returns:
+        str: Markdown text without markdown links.
+    """
+
+    # find all markdown links
+    markdown_links = re.findall(r"\[([^\]]+)\]\(([^)]+)\)", text)
+
+    # remove link keep display text
+    for display, link in markdown_links:
+        text = text.replace(f"[{display}]({link})", display)
+
+    return text
+
+
+def filter_dataframe_by_row_and_columns(df: pd.DataFrame, ignore_columns: list[str] | None = None) -> pd.DataFrame:
+    """
+    Filter dataframe by the rows and columns to display.
+    This does not select based on the values in the dataframe, but rather on the index and columns.
+    Modified from https://blog.streamlit.io/auto-generate-a-dataframe-filtering-ui-in-streamlit-with-filter_dataframe/
+    Args:
+        df (pd.DataFrame): Original dataframe
+        ignore_columns (list[str], optional): Columns to ignore. Defaults to None.
+    Returns:
+        pd.DataFrame: Filtered dataframe
+    """
+    df = df.copy()
+
+    if ignore_columns is None:
+        ignore_columns = []
+
+    modification_container = st.container()
+
+    with modification_container:
+        to_filter_index = st.multiselect("Filter by model:", sorted(df.index))
+        if to_filter_index:
+            df = pd.DataFrame(df.loc[to_filter_index])
+
+        to_filter_columns = st.multiselect(
+            "Filter by benchmark:", sorted([c for c in df.columns if c not in ignore_columns])
+        )
+        if to_filter_columns:
+            df = pd.DataFrame(df[ignore_columns + to_filter_columns])
+
+    return df
+
+
+def filter_dataframe_by_column_values(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Filter dataframe by the values in the dataframe.
+    Modified from https://blog.streamlit.io/auto-generate-a-dataframe-filtering-ui-in-streamlit-with-filter_dataframe/
+    Args:
+        df (pd.DataFrame): Original dataframe
+    Returns:
+        pd.DataFrame: Filtered dataframe
+    """
+    df = df.copy()
+
+    modification_container = st.container()
+
+    with modification_container:
+        to_filter_columns = st.multiselect("Filter results on:", df.columns)
+        left, right = st.columns((1, 20))
+
+        for column in to_filter_columns:
+            if is_bool_dtype(df[column]):
+                user_bool_input = right.checkbox(f"{column}", value=True)
+                df = df[df[column] == user_bool_input]
+
+            elif is_numeric_dtype(df[column]):
+                _min = float(df[column].min())
+                _max = float(df[column].max())
+
+                if (_min != _max) and pd.notna(_min) and pd.notna(_max):
+                    step = 0.01
+                    user_num_input = right.slider(
+                        f"Values for {column}:",
+                        min_value=round(_min - step, 2),
+                        max_value=round(_max + step, 2),
+                        value=(_min, _max),
+                        step=step,
+                    )
+                    df = df[df[column].between(*user_num_input)]
+
+            elif is_datetime64_any_dtype(df[column]):
+                user_date_input = right.date_input(
+                    f"Values for {column}:",
+                    value=(
+                        df[column].min(),
+                        df[column].max(),
+                    ),
+                )
+                if isinstance(user_date_input, Iterable) and len(user_date_input) == 2:
+                    user_date_input_datetime = tuple(map(pd.to_datetime, user_date_input))
+                    start_date, end_date = user_date_input_datetime
+                    df = df.loc[df[column].between(start_date, end_date)]
+
+            else:
+                selected_values = right.multiselect(
+                    f"Values for {column}:",
+                    sorted(df[column].unique()),
+                )
+
+                if selected_values:
+                    df = df[df[column].isin(selected_values)]
+
+    return df
+
+
+def setup_basic():
+    title = "🏆 LLM-Leaderboard"
+
+    st.set_page_config(
+        page_title=title,
+        page_icon="🏆",
+        layout="wide",
+    )
+    st.title(title)
+
+    st.markdown(
+        "A joint community effort to create one central leaderboard for LLMs."
+        f" Visit [swahili-llm-leaderboard]({GITHUB_URL}) to contribute.  \n"
+        'We refer to a model being "open" if it can be locally deployed and used for commercial purposes.'
+    )
+
+
+def setup_leaderboard(readme: str):
+    leaderboard_table = extract_markdown_table_from_multiline(readme, table_headline="## Leaderboard")
+    leaderboard_table = remove_markdown_links(leaderboard_table)
+    df_leaderboard = extract_table_and_format_from_markdown_text(leaderboard_table)
+    df_leaderboard["Open?"] = df_leaderboard["Open?"].map({"yes": 1, "no": 0}).astype(bool)
+
+    st.markdown("## Leaderboard")
+    modify = st.checkbox("Add filters")
+    clear_empty_entries = st.checkbox("Clear empty entries", value=True)
+
+    if modify:
+        df_leaderboard = filter_dataframe_by_row_and_columns(df_leaderboard, ignore_columns=NON_BENCHMARK_COLS)
+        df_leaderboard = filter_dataframe_by_column_values(df_leaderboard)
+
+    if clear_empty_entries:
+        df_leaderboard = df_leaderboard.dropna(axis=1, how="all")
+        benchmark_columns = [c for c in df_leaderboard.columns if df_leaderboard[c].dtype == float]
+        rows_wo_any_benchmark = df_leaderboard[benchmark_columns].isna().all(axis=1)
+        df_leaderboard = df_leaderboard[~rows_wo_any_benchmark]
+
+    st.dataframe(df_leaderboard)
+
+    st.download_button(
+        "Download current selection as .html",
+        df_leaderboard.to_html().encode("utf-8"),
+        "leaderboard.html",
+        "text/html",
+        key="download-html",
+    )
+
+    st.download_button(
+        "Download current selection as .csv",
+        df_leaderboard.to_csv().encode("utf-8"),
+        "leaderboard.csv",
+        "text/csv",
+        key="download-csv",
+    )
+
+
+def setup_benchmarks(readme: str):
+    benchmarks_table = extract_markdown_table_from_multiline(readme, table_headline="## Benchmarks")
+    df_benchmarks = extract_table_and_format_from_markdown_text(benchmarks_table)
+
+    st.markdown("## Covered Benchmarks")
+
+    selected_benchmark = st.selectbox("Select a benchmark to learn more:", df_benchmarks.index.unique())
+    df_selected = df_benchmarks.loc[selected_benchmark]
+    text = [
+        f"Name: {selected_benchmark}",
+    ]
+    for key in df_selected.keys():
+        text.append(f"{key}: {df_selected[key]} ")
+    st.markdown("  \n".join(text))
+
+
+def setup_sources():
+    st.markdown("## Sources")
+    st.markdown(
+        "The results of this leaderboard are collected from the individual papers and published results of the model "
+        "authors. If you are interested in the sources of each individual reported model value, please visit the "
+        f"[llm-leaderboard]({GITHUB_URL}) repository."
+    )
+    st.markdown(
+        """
+    Special thanks to the following pages:
+    - [MosaicML - Model benchmarks](https://www.mosaicml.com/blog/mpt-7b)
+    - [lmsys.org - Chatbot Arena benchmarks](https://lmsys.org/blog/2023-05-03-arena/)
+    - [Papers With Code](https://paperswithcode.com/)
+    - [Stanford HELM](https://crfm.stanford.edu/helm/latest/)
+    - [HF Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+    """
+    )
+
+def setup_Contribution():
+    st.markdown("## How to Contribute")
+
+    markdown_content = """
+    - Model name (don't forget the links):
+      - Filling in missing entries
+      - Adding a new model as a new row to the leaderboard. Please keep the descending order.
+      - Adding a new benchmark as a new column in the leaderboard and adding the benchmark to the benchmarks table. Please keep the descending order.
+    - Code work:
+      - Improving the existing code
+      - Requesting and implementing new features
+    """
+    st.markdown(markdown_content)
+    
+def setup_Sponsorship():
+    st.markdown("## Sponsorship")
+    st.markdown(
+        # "The results of this leaderboard are collected from the individual papers and published results of the model "
+        # "authors. If you are interested in the sources of each individual reported model value, please visit the "
+        # f"[llm-leaderboard]({GITHUB_URL}) repository."
+        "The benchmark is English-based, and we need support translating it into Swahili." 
+        "We welcome sponsorships to help advance this endeavor."
+        "Your sponsorship would facilitate this essential translation effort, bridging language barriers and making the benchmark "
+        "accessible to a broader audience. We're grateful for the dedication shown by our collaborators and aim to extend this impact "
+        "further with the support of sponsors committed to advancing language technologies."
+        "Any support please reach me: [email protected]"
+    )
+
+
+def setup_disclaimer():
+    st.markdown("## Disclaimer")
+    st.markdown(
+        "Above information may be wrong. If you want to use a published model for commercial use, please contact a "
+        "lawyer."
+    )
+
+
+def setup_footer():
+    st.markdown(
+        """
+        ---
+        Made with ❤️ by the awesome open-source community from all over 🌍.
+        """
+    )
+
+
+def main():
+    setup_basic()
+
+    with open("README.md", "r") as f:
+        readme = f.read()
+  
+
+    setup_leaderboard(readme)
+    # setup_benchmarks(readme)
+    # setup_sources()
+    # setup_disclaimer()
+    # setup_footer()
+    setup_Contribution()
+    setup_Sponsorship()
+
+
+if __name__ == "__main__":
+    main()