app.py

import streamlit as st
import pandas as pd

st.set_page_config(page_title="Cyber Benchmark Hub:  Leaderboard", layout="wide")

st.title("Cyber Benchmark Hub:  Leaderboard")


with st.sidebar:
    st.image("https://cdn.prod.website-files.com/630f558f2a15ca1e88a2f774/631f1436ad7a0605fecc5e15_Logo.svg", use_container_width=True)
    st.markdown("[Priam.ai](https://www.priam.ai/)")
    st.divider()
    
    dataset_categories = ["Multiple Choice"]
    selected_category = st.selectbox("Select Dataset Category", dataset_categories, index=0)
    
    datasets_by_category = {
        "Multiple Choice": ["secQA","CyberMetric80"],
    }
    dataset_choice = st.selectbox("Select Dataset", datasets_by_category[selected_category], index=0)
    
    st.divider()
    st.header("Filters & Options")
    #dataset_version = st.radio("Select Dataset Version", ["v1", "v2"])
    if dataset_choice == "secQA":
        dataset_version = st.radio("Select Dataset Version", ["v1", "v2"])
    else:
        st.markdown("**Note:** Only CyberMetric80 has been evaluated")
        dataset_version = "v1"
    # For filtering the leaderboard by model type
    # Note: The available model types will come from the CSV, once loaded.
    # We'll load the CSV later and then update this filter accordingly.
    source_filter_placeholder = st.empty()  # placeholder for source filter after data is loaded
    
    st.markdown("---")
    st.header("Test Parameters")
    test_params = pd.DataFrame({
        "Value": [0, 1, 0, 1, 0]
    }, index=["Temperature", "n", "Presence Penalty", "Top_p", "Frequency Penalty"])
    st.table(test_params)

# Function to estimate random baseline accuracy for MCQ datasets
def estimate_random_accuracy(questions):
    """
    Estimates the average accuracy when answering questions randomly.
    
    Args:
        questions: List of tuples where each tuple is (question_id, num_choices)
    
    Returns:
        The estimated average accuracy (probability of correct answers)
    """
    if not questions:
        return 0.0
    
    total_probability = 0.0
    for question_id, num_choices in questions:
        probability = 1.0 / num_choices
        total_probability += probability
    
    average_accuracy = total_probability / len(questions)
    return average_accuracy

# For the SECQA dataset we assume each question has 4 choices.
# According to the dataset card, there are 242 questions.
total_questions = 242
questionnaire = [(1, 4), (2, 1), (3, 4), (4, 2), (5, 3), (6, 3), (7, 4), (8, 2), (9, 4), (10, 2), (11, 4), (12, 4), (13, 2), (14, 2), (15, 4), (16, 4), (17, 2), (18, 2), (19, 2), (20, 1), (21, 2), (22, 4), (23, 1), (24, 4), (25, 3), (26, 3), (27, 2), (28, 3), (29, 2), (30, 1), (31, 2), (32, 3), (33, 3), (34, 2), (35, 4), (36, 3), (37, 1), (38, 2), (39, 1), (40, 2), (41, 1), (42, 3), (43, 3), (44, 1), (45, 3), (46, 1), (47, 4), (48, 2), (49, 2), (50, 4), (51, 2), (52, 4), (53, 1), (54, 4), (55, 3), (56, 3), (57, 3), (58, 1), (59, 2), (60, 4), (61, 1), (62, 3), (63, 1), (64, 3), (65, 1), (66, 3), (67, 4), (68, 1), (69, 1), (70, 1), (71, 3), (72, 2), (73, 1), (74, 2), (75, 3), (76, 3), (77, 3), (78, 4), (79, 1), (80, 4), (81, 4), (82, 4), (83, 2), (84, 3), (85, 2), (86, 1), (87, 1), (88, 2), (89, 2), (90, 2), (91, 4), (92, 4), (93, 3), (94, 2), (95, 3), (96, 3), (97, 2), (98, 4), (99, 4), (100, 3), (101, 4), (102, 2), (103, 4), (104, 2), (105, 3), (106, 2), (107, 3), (108, 4), (109, 4), (110, 2)]
questionnairev2 = [(1, 4), (2, 4), (3, 2), (4, 3), (5, 2), (6, 4), (7, 3), (8, 2), (9, 3), (10, 2), (11, 1), (12, 2), (13, 3), (14, 2), (15, 4), (16, 2), (17, 2), (18, 4), (19, 4), (20, 3), (21, 4), (22, 3), (23, 3), (24, 3), (25, 1), (26, 1), (27, 2), (28, 2), (29, 2), (30, 2), (31, 2), (32, 4), (33, 3), (34, 3), (35, 3), (36, 3), (37, 4), (38, 3), (39, 3), (40, 4), (41, 1), (42, 2), (43, 3), (44, 2), (45, 1), (46, 1), (47, 2), (48, 4), (49, 2), (50, 1), (51, 3), (52, 1), (53, 4), (54, 4), (55, 2), (56, 3), (57, 2), (58, 2), (59, 1), (60, 3), (61, 3), (62, 1), (63, 2), (64, 2), (65, 3), (66, 4), (67, 3), (68, 3), (69, 1), (70, 1), (71, 3), (72, 1), (73, 2), (74, 4), (75, 4), (76, 1), (77, 4), (78, 4), (79, 3), (80, 1), (81, 2), (82, 2), (83, 3), (84, 2), (85, 1), (86, 2), (87, 4), (88, 2), (89, 2), (90, 4), (91, 3), (92, 2), (93, 1), (94, 2), (95, 3), (96, 1), (97, 1), (98, 4), (99, 1), (100, 1)]
random_accuracy = estimate_random_accuracy(questionnaire)
random_accuracyv2 = estimate_random_accuracy(questionnairev2)


# Determine file path based on dataset choice.
# For now, if dataset_choice is "secQA", we use "Benchmark.csv"
if dataset_choice == "secQA":
    file_path = "Benchmark.csv"  # Ensure this file is uploaded in your Hugging Face Space
elif dataset_choice == "CyberMetric80":
    file_path = "metric.csv"  # Placeholder: update with actual file paths for future datasets

# Function to load and clean CSV data
@st.cache_data
def load_data(file_path):
    df = pd.read_csv(file_path)
    
    # Remove any unnamed columns (caused by trailing commas)
    df = df.loc[:, ~df.columns.str.contains('Unnamed', na=False)]
    
    # Standardize column names
    df.columns = df.columns.str.strip()
    df.rename(columns={
        "model name": "Model",
        "source": "Type",
        "v1 metric": "V1 Accuracy",
        "v2 metric": "V2 Accuracy"
    }, inplace=True)
    
    # Convert percentage strings to floats (e.g., "100%" → 1.0)
    for col in ["V1 Accuracy", "V2 Accuracy"]:
        if col in df.columns:
            df[col] = df[col].astype(str).str.replace("%", "").str.strip()
            df[col] = pd.to_numeric(df[col], errors='coerce') / 100
    
    return df

# Load dataset
df = load_data(file_path)

# Update the source filter with the actual options from the data
source_filter = source_filter_placeholder.multiselect(
    "Select Model Type",
    options=df["Type"].unique().tolist(),
    default=df["Type"].unique().tolist()
)

# Apply filtering based on the sidebar selections
df_filtered = df[df["Type"].isin(source_filter)] if source_filter else df

# Choose the correct metric version and compute Accuracy
#df_filtered["Accuracy"] = df_filtered["V1 Accuracy"] if dataset_version == "v1" else df_filtered["V2 Accuracy"]
if dataset_choice == "CyberMetric80":
    df_filtered["Accuracy"] = df_filtered["V1 Accuracy"]
else:
    df_filtered["Accuracy"] = df_filtered["V1 Accuracy"] if dataset_version == "v1" else df_filtered["V2 Accuracy"]

df_filtered = df_filtered[["Model", "Type", "Accuracy"]].dropna()  # Drop rows with errors

# Sort by Accuracy descending
df_filtered = df_filtered.sort_values("Accuracy", ascending=False).reset_index(drop=True)

# Compute dense ranking so that models with equal accuracy share the same rank
df_filtered['Rank'] = df_filtered['Accuracy'].rank(method='dense', ascending=False).astype(int)
df_filtered = df_filtered[['Rank', 'Model', 'Type', 'Accuracy']]



tab1, tab2 = st.tabs(["Leaderboard", "About"])

with tab1:
    if dataset_choice == "secQA":
        st.markdown("#### [View the SECQA Dataset](https://huggingface.co/datasets/zefang-liu/secqa)")
    elif dataset_choice == "CyberMetric80":
        st.markdown("#### [View the CyberMetric Dataset](https://github.com/cybermetric/CyberMetric)")

    # Use columns to display leaderboard and model details side-by-side
    col1, col2 = st.columns([2, 1])

    with col1:
        st.subheader(f"Leaderboard for {dataset_choice.upper()} Version {dataset_version}")
        st.dataframe(df_filtered.style.hide(axis='index'))

    with col2:
        st.subheader("Model Details")
        selected_model = st.selectbox("Select a Model", df_filtered["Model"].tolist())
        model_details = df_filtered[df_filtered["Model"] == selected_model].iloc[0]
        st.write(f"**Model:** {model_details['Model']}")
        st.write(f"**Type:** {model_details['Type']}")
        st.write(f"**Accuracy:** {model_details['Accuracy']:.2%}")
        st.write(f"**Rank:** {model_details['Rank']}")

        st.divider()
        # Display the random baseline accuracy above the leaderboard
        if dataset_choice == "secQA":
            st.markdown("### Random Baseline Accuracy")
            st.markdown("**{:.2%}** (computed with random guessing on SECQAv1)".format(random_accuracy))
            st.markdown("**{:.2%}** (computed with random guessing on SECQAv2)".format(random_accuracyv2))

    # Footer
    st.markdown("---")
    st.info("More dataset benchmarks will be added to this hub in the future.")

with tab2:
    st.title("About the Cyber Benchmark Hub")
    st.markdown("""
    Welcome to the **Cyber Benchmark Hub:  Leaderboard**!

    This application benchmarks language models on their performance across cybersecurity question-answering tasks using the [SECQA dataset](https://huggingface.co/datasets/zefang-liu/secqa). It provides an interactive interface to explore model accuracy, rank models, and understand how different model types perform on security-centric multiple-choice questions.

    
    ### Leaderboard Features

    - Compare **different models** (e.g., GPT, Claude, Mistral) based on SECQA v1 or v2.
    - Filter by **model type/source** (open-source, closed)
    - View **dense rankings** where models with equal accuracy share the same rank.
    - See detailed information for each model, including:
      - Accuracy score
      - Rank
    

    ### Random Baseline Accuracy

    The app computes the **expected accuracy** if a model guessed randomly on all questions:

    This helps contextualize the actual performance of models.



    ### Built by

    [Priam.ai](https://www.priam.ai/) 

    *This benchmark hub will continue to expand as more models and datasets are released in the cybersecurity NLP space.*
    """)