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Financial_LLM_Performance_Leaderboard
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import gradio as gr
import pandas as pd
import numpy as np

# Data for Table 1: Robustness Results (unchanged)
robustness_data = {
    "Model Name": [
        "Gemini 2.0 Flash Exp", "Gemini 1.5 Pro 002", "OpenAI GPT-4o", "OpenAI o1", "OpenAI o3-mini",
        "DeepSeek-R1-Distill-Llama-8B", "DeepSeek-R1-Distill-Qwen-14B", "DeepSeek-R1-Distill-Qwen-32B",
        "DeepSeek-R1-Distill-Llama-70B", "DeepSeek-R1", "Meta-Llama-3.1-8B-Instruct",
        "Meta-Llama-3.1-70B-Instruct", "Meta-Llama-3.3-70B-Instruct", "Qwen2.5-7B-Instruct",
        "Qwen2.5-14B-Instruct", "Qwen2.5-32B-Instruct", "Qwen2.5-72B-Instruct", "Qwen2.5-7B-Instruct-1M",
        "Qwen2.5-14B-Instruct-1M", "Nemotron-70B-Instruct-HF", "Phi-3-mini-128k-Instruct",
        "Phi-3-small-128k-Instruct", "Phi-3-medium-128k-Instruct", "Palmyra-Fin-128k-Instruct"
    ],
    "Baseline": [0.95, 0.96, 0.95, 0.97, 0.98, 0.83, 0.95, 0.95, 0.96, 0.94, 0.91, 0.94, 0.95, 0.92, 0.95, 0.95, 0.94, 0.91, 0.95, 0.94, 0.86, 0.88, 0.89, 0.96],
    "Misspelled (Ξ”)": ["0.95 (0.0)", "0.95 (0.0)", "0.94 (↓0.01)", "0.95 (↓0.02)", "0.96 (↓0.02)", "0.85 (↑0.02)", "0.90 (↓0.05)", "0.97 (↑0.02)", "0.97 (↑0.01)", "0.94 (0.0)", "0.90 (↓0.01)", "0.92 (↓0.02)", "0.92 (↓0.03)", "0.91 (↓0.01)", "0.94 (↓0.01)", "0.94 (0.0)", "0.94 (0.0)", "0.91 (0.0)", "0.92 (↓0.03)", "0.94 (0.0)", "0.85 (↓0.01)", "0.84 (↓0.04)", "0.84 (↓0.05)", "0.93 (↓0.03)"],
    "Incomplete (Ξ”)": ["0.95 (0.0)", "0.94 (↓0.02)", "0.94 (↓0.01)", "0.94 (↓0.03)", "0.96 (↓0.02)", "0.82 (↓0.01)", "0.92 (↓0.03)", "0.95 (0.0)", "0.95 (↓0.01)", "0.93 (↓0.01)", "0.86 (↓0.05)", "0.94 (0.0)", "0.93 (↓0.02)", "0.90 (↓0.02)", "0.94 (↓0.01)", "0.93 (↓0.02)", "0.93 (↓0.01)", "0.91 (0.0)", "0.91 (↓0.04)", "0.93 (↓0.01)", "0.78 (↓0.08)", "0.78 (↓0.10)", "0.84 (↓0.05)", "0.92 (↓0.04)"],
    "Out-of-Domain (Ξ”)": ["0.88 (↓0.07)", "0.92 (↓0.04)", "0.92 (↓0.03)", "0.89 (↓0.08)", "0.95 (↓0.03)", "0.87 (↑0.04)", "0.93 (↓0.02)", "0.92 (↓0.03)", "0.94 (↓0.02)", "0.91 (↓0.03)", "0.82 (↓0.09)", "0.87 (↓0.07)", "0.90 (↓0.05)", "0.85 (↓0.07)", "0.94 (↓0.01)", "0.92 (↓0.03)", "0.92 (↓0.02)", "0.86 (↓0.05)", "0.91 (↓0.04)", "0.90 (↓0.04)", "0.79 (↓0.07)", "0.83 (↓0.05)", "0.81 (↓0.08)", "0.90 (↓0.06)"],
    "OCR Context (Ξ”)": ["0.91 (↓0.04)", "0.92 (↓0.04)", "0.95 (0.0)", "0.94 (↓0.03)", "0.90 (↓0.08)", "0.72 (↓0.11)", "0.86 (↓0.09)", "0.89 (↓0.06)", "0.93 (↓0.03)", "0.88 (↓0.06)", "0.80 (↓0.11)", "0.88 (↓0.06)", "0.89 (↓0.06)", "0.80 (↓0.12)", "0.88 (↓0.07)", "0.92 (↓0.03)", "0.91 (↓0.03)", "0.77 (↓0.14)", "0.89 (↓0.06)", "0.91 (↓0.03)", "0.69 (↓0.17)", "0.78 (↓0.10)", "0.72 (↓0.17)", "0.89 (↓0.07)"],
    "Robustness (Ξ”)": ["0.83 (↓0.12)", "0.84 (↓0.12)", "0.85 (↓0.10)", "0.81 (↓0.16)", "0.90 (↓0.08)", "0.64 (↓0.19)", "0.82 (↓0.13)", "0.86 (↓0.09)", "0.89 (↓0.07)", "0.80 (↓0.14)", "0.70 (↓0.21)", "0.80 (↓0.14)", "0.82 (↓0.13)", "0.75 (↓0.17)", "0.86 (↓0.09)", "0.85 (↓0.10)", "0.84 (↓0.10)", "0.74 (↓0.17)", "0.80 (↓0.15)", "0.82 (↓0.12)", "0.58 (↓0.28)", "0.70 (↓0.18)", "0.63 (↓0.26)", "0.83 (↓0.13)"]
}

# Data for Table 2: Context Grounding Results (unchanged)
context_grounding_data = {
    "Model Name": [
        "Gemini 2.0 Flash Exp", "Gemini 1.5 Pro 002", "OpenAI GPT-4o", "OpenAI o1", "OpenAI o3-mini",
        "DeepSeek-R1-Distill-Llama-8B", "DeepSeek-R1-Distill-Qwen-14B", "DeepSeek-R1-Distill-Qwen-32B",
        "DeepSeek-R1-Distill-Llama-70B", "DeepSeek-R1", "Meta-Llama-3.1-8B-Instruct",
        "Meta-Llama-3.1-70B-Instruct", "Meta-Llama-3.3-70B-Instruct", "Qwen2.5-7B-Instruct",
        "Qwen2.5-14B-Instruct", "Qwen2.5-32B-Instruct", "Qwen2.5-72B-Instruct", "Qwen2.5-7B-Instruct-1M",
        "Qwen2.5-14B-Instruct-1M", "Nemotron-70B-Instruct-HF", "Phi-3-mini-128k-Instruct",
        "Phi-3-small-128k-Instruct", "Phi-3-medium-128k-Instruct", "Palmyra-Fin-128k-Instruct"
    ],
    "Irrelevant Ctx": [0.81, 0.74, 0.52, 0.56, 0.67, 0.32, 0.49, 0.54, 0.50, 0.51, 0.67, 0.46, 0.50, 0.75, 0.75, 0.89, 0.69, 0.63, 0.78, 0.52, 0.54, 0.37, 0.36, 0.95],
    "No Ctx": [0.66, 0.64, 0.43, 0.55, 0.51, 0.27, 0.21, 0.24, 0.27, 0.22, 0.63, 0.37, 0.40, 0.64, 0.61, 0.68, 0.60, 0.58, 0.53, 0.48, 0.34, 0.26, 0.25, 0.66],
    "Ctx Grounding QA": [0.77, 0.72, 0.50, 0.57, 0.63, 0.30, 0.36, 0.40, 0.41, 0.39, 0.70, 0.48, 0.47, 0.75, 0.70, 0.82, 0.68, 0.65, 0.69, 0.52, 0.47, 0.34, 0.33, 0.83],
    "Ctx Grounding TG": [0.46, 0.52, 0.25, 0.45, 0.27, 0.25, 0.27, 0.35, 0.22, 0.20, 0.27, 0.37, 0.31, 0.31, 0.55, 0.55, 0.39, 0.29, 0.37, 0.39, 0.24, 0.10, 0.14, 0.65],
    "Ctx Grounding": [0.74, 0.69, 0.47, 0.55, 0.59, 0.30, 0.35, 0.39, 0.38, 0.37, 0.65, 0.47, 0.45, 0.70, 0.68, 0.79, 0.64, 0.60, 0.65, 0.50, 0.44, 0.31, 0.30, 0.80],
    "Robustness": [0.83, 0.84, 0.85, 0.81, 0.90, 0.64, 0.82, 0.86, 0.89, 0.80, 0.70, 0.80, 0.82, 0.75, 0.86, 0.85, 0.84, 0.74, 0.80, 0.82, 0.58, 0.70, 0.63, 0.83],
    "Compliance": [0.76, 0.72, 0.52, 0.59, 0.63, 0.34, 0.40, 0.44, 0.43, 0.41, 0.66, 0.51, 0.49, 0.71, 0.71, 0.80, 0.67, 0.62, 0.68, 0.54, 0.46, 0.35, 0.34, 0.81]
}

# Function to bold the highest score per column (excluding "Model Name")
def format_table(df):
    styled_df = df.copy()
    numeric_columns = [col for col in df.columns if col != "Model Name"]
    
    for col in numeric_columns:
        if col in ["Baseline", "Irrelevant Ctx", "No Ctx", "Ctx Grounding QA", "Ctx Grounding TG", "Ctx Grounding", "Robustness", "Compliance"]:
            # Convert string values (e.g., "0.95 (0.0)") to float for comparison, or use direct float values
            if any(" (" in str(x) for x in df[col]):
                # Handle string values with deltas (e.g., "0.95 (0.0)")
                values = [float(str(x).split(" (")[0]) for x in df[col]]
            else:
                # Handle direct float values
                values = df[col].astype(float)
            
            max_value = np.max(values)
            styled_df[col] = df[col].apply(lambda x: f"**{x}**" if (float(str(x).split(" (")[0]) if " (" in str(x) else float(x)) == max_value else x)
    
    return styled_df

# Function to create the Gradio interface
def create_leaderboard():
    # Convert data to DataFrames
    robustness_df = pd.DataFrame(robustness_data)
    context_grounding_df = pd.DataFrame(context_grounding_data)

    # Format tables to bold highest scores
    robustness_df = format_table(robustness_df)
    context_grounding_df = format_table(context_grounding_df)

    # Create Gradio interface with a nice theme
    with gr.Blocks(theme=gr.themes.Soft(), title="Financial Model Performance Leaderboard") as demo:
        gr.Markdown("# Financial Model Performance Leaderboard")
        
        with gr.Row():
            with gr.Column():
                with gr.Tab("Robustness Results"):
                    gr.DataFrame(
                        value=robustness_df,
                        label="Robustness Results",
                        wrap=True,
                        elem_classes=["custom-table"]  # Custom CSS class for styling
                    )
            with gr.Column():
                with gr.Tab("Context Grounding Results"):
                    gr.DataFrame(
                        value=context_grounding_df,
                        label="Context Grounding Results",
                        wrap=True,
                        elem_classes=["custom-table"]  # Custom CSS class for styling
                    )

    # Custom CSS for better table appearance (larger font, spacing, and height)
    demo.css = """
    .custom-table {
        font-size: 16px;  /* Increase font size for readability */
        line-height: 2;   /* Increase line height for longer rows */
        max-height: 600px; /* Set maximum height for scrolling if needed */
        overflow-y: auto;  /* Enable vertical scrolling if content exceeds height */
        border-collapse: collapse;
    }
    .custom-table th, .custom-table td {
        padding: 12px;    /* Increase padding for spacing */
        border: 1px solid #ddd;
    }
    .custom-table th {
        background-color: #f5f5f5;
        font-weight: bold;
    }
    """

    return demo

# Launch the Gradio app
if __name__ == "__main__":
    demo = create_leaderboard()
    demo.launch()