About

app.py

@@ -1,10 +1,10 @@
 import streamlit as st
 import pandas as pd
 
-st.set_page_config(page_title="Cyber Benchmark Hub: SECQA Leaderboard", layout="wide")
+st.set_page_config(page_title="Cyber Benchmark Hub:  Leaderboard", layout="wide")
+
+st.title("Cyber Benchmark Hub:  Leaderboard")
 
-st.title("Cyber Benchmark Hub: SECQA Leaderboard")
-st.markdown("#### [View the SECQA Dataset](https://huggingface.co/datasets/zefang-liu/secqa)")
 
 with st.sidebar:
     st.image("https://cdn.prod.website-files.com/630f558f2a15ca1e88a2f774/631f1436ad7a0605fecc5e15_Logo.svg", use_container_width=True)
@@ -34,6 +34,37 @@ with st.sidebar:
     }, 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":
@@ -89,22 +120,69 @@ df_filtered = df_filtered.sort_values("Accuracy", ascending=False).reset_index(d
 df_filtered['Rank'] = df_filtered['Accuracy'].rank(method='dense', ascending=False).astype(int)
 df_filtered = df_filtered[['Rank', 'Model', 'Type', 'Accuracy']]
 
-# 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']}")
-
-# Footer
-st.markdown("---")
-st.info("More dataset benchmarks will be added to this hub in the future.")
+
+
+tab1, tab2 = st.tabs(["Leaderboard", "About"])
+
+with tab1:
+    st.markdown("#### [View the SECQA Dataset](https://huggingface.co/datasets/zefang-liu/secqa)")
+
+    # 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
+        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.*
+    """)  # Replace with actual random_accuracy values if available