not initial

app.py

@@ -0,0 +1,175 @@
+import pandas as pd
+import numpy as np
+import gradio as gr
+from urllib.parse import quote
+
+def style_dataframe(df):
+    if len(df) == 0:
+        return df
+
+    highlight_cols = ["Average", "IFEval", "BBHI", "MATH", "GPQA", "MUJB", "MMLU-PRO"]
+    styled = df.style
+
+    def highlight_green(val):
+        try:
+            val_float = float(str(val).replace('%', '').replace(' kg', ''))
+            return f'background: linear-gradient(90deg, rgba(46, 125, 50, 0.5) {val_float}%, rgba(46, 125, 50, 0.1) {val_float}%); color: white;'
+        except:
+            return 'background-color: #121212; color: white;'
+
+    for col in highlight_cols:
+        styled = styled.applymap(highlight_green, subset=[col])
+
+    styled = styled.set_properties(
+        subset=["Model"],
+        **{'color': '#4da6ff'}
+    )
+
+    return styled
+
+def create_leaderboard_data():
+    models = [
+        {"model_name": "meta-llama/llama-3-70b-instruct", "type": "open"},
+        {"model_name": "mistralai/Mistral-7B-Instruct-v0.3", "type": "open"},
+        {"model_name": "google/gemma-7b-it", "type": "open"},
+        {"model_name": "Qwen/Qwen2-7B-Instruct", "type": "open"},
+        {"model_name": "anthropic/claude-3-opus", "type": "closed"},
+        {"model_name": "OpenAI/gpt-4o", "type": "closed"},
+        {"model_name": "01-ai/Yi-1.5-34B-Chat", "type": "open"},
+        {"model_name": "google/gemma-2b", "type": "open"},
+        {"model_name": "microsoft/phi-3-mini-4k-instruct", "type": "open"},
+        {"model_name": "microsoft/phi-3-mini-128k-instruct", "type": "open"},
+        {"model_name": "stabilityai/stable-beluga-7b", "type": "open"},
+        {"model_name": "togethercomputer/RedPajama-INCITE-7B-Instruct", "type": "open"},
+        {"model_name": "databricks/dbrx-instruct", "type": "closed"},
+        {"model_name": "mosaicml/mpt-7b-instruct", "type": "open"},
+        {"model_name": "01-ai/Yi-1.5-9B-Chat", "type": "open"}
+    ]
+
+    np.random.seed(42)
+
+    rows = []
+    for i, model in enumerate(models, 1):
+        model_name = model["model_name"]
+        model_type = model["type"]
+
+        emoji = "🟢" if model_type.lower() == "open" else "🔴"
+        type_with_emoji = f"{emoji} {model_type.upper()}"
+
+        if "/" in model_name:
+            org, name = model_name.split("/", 1)
+            model_link = f"[{model_name}](https://huggingface.co/{quote(model_name)})"
+        else:
+            model_link = f"[{model_name}](https://huggingface.co/models?search={quote(model_name)})"
+
+        average = round(np.random.uniform(40, 90), 2)
+        ifeval = round(np.random.uniform(30, 90), 2)
+        bbhi = round(np.random.uniform(40, 85), 2)
+        math = round(np.random.uniform(20, 80), 2)
+        gpqa = round(np.random.uniform(10, 70), 2)
+        mujb = round(np.random.uniform(10, 70), 2)
+        mmlu = round(np.random.uniform(40, 85), 2)
+        co2_cost = round(np.random.uniform(1, 100), 2)
+
+        rows.append([
+            i,
+            type_with_emoji,
+            model_link,
+            f"{average}",
+            f"{ifeval}",
+            f"{bbhi}",
+            f"{math}",
+            f"{gpqa}",
+            f"{mujb}",
+            f"{mmlu}",
+            f"{co2_cost} kg"
+        ])
+
+    rows.sort(key=lambda x: float(x[3]), reverse=True)
+
+    for i, row in enumerate(rows, 1):
+        row[0] = i
+
+    df = pd.DataFrame(rows, columns=["Rank", "Type", "Model", "Average", "IFEval", "BBHI", "MATH", "GPQA", "MUJB", "MMLU-PRO", "CO_Cost"])
+    return style_dataframe(df)
+
+def get_filter_data():
+    return {
+        "For Edge Devices": 5,
+        "For Consumers": 4,
+        "Mid-range": 4,
+        "For the GPU-rich": 3,
+        "Only Official Providers": 8
+    }
+
+css = """
+.html-container {
+    text-align: center;
+    display: flex;
+    justify-content: center;
+    width: 100%;
+}
+
+.dataframe-container {
+    margin-top: 0.5rem;
+    margin-bottom: 0.5rem;
+}
+
+.leaderboard-title {
+    font-size: 1.5rem;
+    font-weight: bold;
+    margin-bottom: 0.25rem;
+    color: #f0f0f0;
+}
+
+.leaderboard-subtitle {
+    font-size: 0.9rem;
+    margin-bottom: 1rem;
+    color: #a0a0a0;
+}
+
+.filters-container {
+    margin-bottom: 0.5rem;
+}
+"""
+
+filter_data = get_filter_data()
+filter_choices = [f"{key} · {value}" for key, value in filter_data.items()]
+
+with gr.Blocks(css=css) as demo:
+    gr.HTML("""
+        <div style="display: flex; align-items: center; justify-content: center; margin-bottom: 10px;">
+            <div class="leaderboard-title">Open LLM Leaderboard</div>
+        </div>
+        <div class="leaderboard-subtitle">Comparing Large Language Models in an open and reproducible way</div>
+    """)
+
+    with gr.Row():
+        filters = gr.CheckboxGroup(
+            label="Quick Filters",
+            choices=filter_choices,
+        )
+
+    with gr.Row():
+        status_text = gr.HTML("<div style='text-align: right; color: #888; font-size: 0.8rem;'>Last updated: June 25, 2024 at 10:30 AM</div>")
+
+    leaderboard_df = create_leaderboard_data()
+    leaderboard_table = gr.Dataframe(
+        value=leaderboard_df,
+        headers=["Rank", "Type", "Model", "Average", "IFEval", "BBHI", "MATH", "GPQA", "MUJB", "MMLU-PRO", "CO_Cost"],
+        datatype=["number", "str", "markdown", "str", "str", "str", "str", "str", "str", "str", "str"],
+        elem_id="leaderboard-table",
+        interactive=False,
+        max_height=600,
+        show_search="search",
+        show_copy_button=True,
+        show_fullscreen_button=True,
+        pinned_columns=2,
+        column_widths=["5%", "7%", "35%", "7%", "7%", "7%", "7%", "7%", "7%", "7%", "6%"]
+    )
+
+    refresh_btn = gr.Button("Refresh Data")
+    refresh_btn.click(fn=lambda: create_leaderboard_data(), outputs=leaderboard_table)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,8 @@
+gradio>=3.50.0
+numpy>=1.20.0
+pandas>=1.3.0
+pillow>=8.0,<12.0
+pydub
+pyyaml>=5.0,<7.0
+python-multipart>=0.0.18
+typing_extensions~=4.0

style.css

@@ -0,0 +1,44 @@
+.html-container {
+    text-align: center;
+    display: flex;
+    justify-content: center;
+    width: 100%;
+}
+
+.dataframe-container {
+    margin-top: 0.5rem;
+    margin-bottom: 0.5rem;
+}
+
+.leaderboard-title {
+    font-size: 1.5rem;
+    font-weight: bold;
+    margin-bottom: 0.25rem;
+    color: #f0f0f0;
+}
+
+.leaderboard-subtitle {
+    font-size: 0.9rem;
+    margin-bottom: 1rem;
+    color: #a0a0a0;
+}
+
+.filters-container fieldset {
+   display: flex;
+   flex-direction: row;
+   justify-content: center;
+   align-items: center;
+   gap: 0.5rem;
+}
+
+.refresh-btn {
+    margin-top: 0.5rem;
+}
+
+.status-container {
+    display: flex;
+    justify-content: flex-end;
+    font-size: 0.75rem;
+    color: #a0a0a0;
+}
+