initial commit

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,1011 @@
+import streamlit as st
+import os
+import json
+import numpy as np
+import plotly.graph_objects as go
+import plotly.express as px
+from plotly.subplots import make_subplots
+import time
+import pandas as pd  # Adding pandas for safer data handling
+from PIL import Image
+import io
+import base64
+
+# Assuming these are defined in create_figures.py
+# If not available, we'll define them here
+try:
+    from create_figures import MODELS, MODELS_COLORS, MODEL_HATCHES, MODELS_LOGOS
+except ImportError:
+    # Fallback definitions with actual model names from the screenshot
+    MODELS_LOGOS = {
+        "Claude-3.7-Sonnet": "figures/logo_images/claude.png",
+        "ALLaM 7B": "figures/logo_images/allam.png",
+        "Fanar": "figures/logo_images/fanar.png",
+        "Jais 30B": "figures/logo_images/jais.png",
+        "GPT-4o": "figures/logo_images/openai.png",
+        "Mistral-Saba": "figures/logo_images/mistral.png",
+        "CR-7B-Arabic": "figures/logo_images/cohere.png",
+    }
+    MODELS_LOGOS = {
+        "Claude-3.7-Sonnet": "figures/logo_images/claude.png",
+        "ALLaM 7B": "figures/logo_images/allam.png",
+        "Fanar": "figures/logo_images/fanar.png",
+        "Jais 30B": "figures/logo_images/jais.png",
+        "GPT-4o": "figures/logo_images/openai.png",
+        "Mistral-Saba": "figures/logo_images/mistral.png",
+        "CR-7B-Arabic": "figures/logo_images/cohere.png",
+    }
+
+    MODELS = list(MODELS_LOGOS.keys())
+    MODELS_COLORS = {
+        MODELS[0]: '#7B68EE',       # Medium slate blue (Claude)
+        MODELS[1]: '#4169E1',       # Royal blue (ALLaM)
+        MODELS[2]: '#008080',       # Teal (Fanar)
+        MODELS[3]: '#1E90FF',       # Dodger blue (Jais)
+        MODELS[4]: '#00A67E',       # Green-teal (OpenAI)
+        MODELS[5]: '#FF6B6B',       # Light red (Mistral)
+        MODELS[6]: '#6F4E37'        # Coffee brown (Cohere)
+    }
+
+    # Define distinct hatches for each model - using a variety of patterns
+    MODEL_HATCHES = {
+        MODELS[0]: "",           # No hatch
+        MODELS[1]: "///",        # Diagonal lines
+        MODELS[2]: "xxx",        # Cross-hatching
+        MODELS[3]: "...",        # Dots
+        MODELS[4]: "++",         # Plus signs
+        MODELS[5]: "oo",         # Small circles
+        MODELS[6]: "**",         # Stars
+    }
+
+MODEL_NAME_DICT={
+    "Claude-3.7-Sonnet": "Claude-3.7-Sonnet",
+    "ALLaM 7B": "ALLaM 7B",
+    "Fanar": "Fanar",
+    "Jais 30B": "Jais 30B",
+    "GPT-4o": "GPT-4o",
+    "Mistral-Saba": "Mistral-Saba",
+    "CR-7B-Arabic": "Cohere-R7B-Arabic",
+}
+# Page config is now in the main() function to avoid potential initialization issues
+
+# Custom CSS for better styling with dark mode support
+st.markdown("""
+    <style>
+    .stTabs [data-baseweb="tab-list"] {
+        gap: 8px;
+    }
+    .stTabs [data-baseweb="tab"] {
+        height: 50px;
+        white-space: pre-wrap;
+        border-radius: 4px 4px 0px 0px;
+        gap: 1px;
+        padding-top: 10px;
+        padding-bottom: 10px;
+    }
+    .stTabs [aria-selected="true"] {
+        background-color: #4e8df5;
+        color: white;
+    }
+    h1, h2, h3 {
+        padding-top: 1rem;
+        padding-bottom: 0.5rem;
+    }
+    .stMarkdown {
+        padding: 0.5rem 0;
+    }
+    .block-container {
+        max-width: 1200px;
+        padding: 1rem 2rem !important;
+    }
+    .element-container {
+        opacity: 1 !important;
+    }
+    div[data-testid="stVerticalBlock"] {
+        opacity: 1 !important;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+# Helper functions
+def load_data(input_dir="data/leaderboard_data/"):
+    """Load and process the data from JSON files."""
+    try:
+        # Use standard Python json module to avoid orjson issues
+        with open(os.path.join(input_dir, "category_breakdown.json"), "r") as f:
+            category_data = json.load(f)
+        
+        with open(os.path.join(input_dir, "attack_breakdown.json"), "r") as f:
+            attack_data = json.load(f)
+            if "attack_breakdown" in attack_data:
+                attack_data = attack_data["attack_breakdown"]
+        
+        return category_data, attack_data
+    except Exception as e:
+        # Show a more informative message without using st.error to avoid potential issues
+        st.info(f"Using sample data for demonstration purposes. Original error: {str(e)}")
+        
+        # Sample categories
+        categories = ["Controlled Substances", "Sexual Content", "Bias", "Harmful Instructions", 
+                     "Hate Speech", "False Premise", "Direct Prompting"]
+        
+        # Sample attack types
+        attack_types = ["Direct Prompting", "Hypothetical Testing", "Few-Shot", "Role Play", 
+                       "Jailbreaking", "False Premise"]
+        
+        # Generate sample category data
+        category_data = {"categories": {}}
+        for category in categories:
+            category_data["categories"][category] = {}
+            for model in MODELS:
+                # Random safety scores that sum to 1
+                safe = np.random.uniform(0.6, 0.95)
+                slightly = np.random.uniform(0, 0.15)
+                moderately = np.random.uniform(0, 0.1)
+                extremely = 1 - safe - slightly - moderately
+                
+                category_data["categories"][category][model] = {
+                    "safe": safe,
+                    "slightly unsafe": slightly,
+                    "moderately unsafe": moderately,
+                    "extremely unsafe": extremely
+                }
+        
+        # Generate sample attack data
+        attack_data = {
+            "safety_scores": {},
+            "slightly_unsafe_scores": {},
+            "moderately_unsafe_scores": {},
+            "extremely_unsafe_scores": {}
+        }
+        
+        for attack in attack_types:
+            attack_data["safety_scores"][attack] = {}
+            attack_data["slightly_unsafe_scores"][attack] = {}
+            attack_data["moderately_unsafe_scores"][attack] = {}
+            attack_data["extremely_unsafe_scores"][attack] = {}
+            
+            for model in MODELS:
+                # Random safety scores
+                safe = np.random.uniform(0.6, 0.95)
+                attack_data["safety_scores"][attack][model] = safe
+                
+                # Distribute the unsafe portion
+                unsafe_total = 1 - safe
+                slightly = np.random.uniform(0, unsafe_total * 0.6)
+                moderately = np.random.uniform(0, unsafe_total * 0.3)
+                extremely = unsafe_total - slightly - moderately
+                
+                attack_data["slightly_unsafe_scores"][attack][model] = slightly
+                attack_data["moderately_unsafe_scores"][attack][model] = moderately
+                attack_data["extremely_unsafe_scores"][attack][model] = extremely
+        
+        return category_data, attack_data
+
+# --- Category Analysis Functions ---
+def create_category_safety_heatmap(category_data, selected_models):
+    """Create a heatmap for category safety comparison."""
+    categories = list(category_data["categories"].keys())
+    
+    # Prepare data for heatmap
+    safe_data = np.zeros((len(categories), len(selected_models)))
+    unsafe_data = np.zeros((len(categories), len(selected_models)))
+    
+    for i, category in enumerate(categories):
+        for j, model in enumerate(selected_models):
+            safe_data[i, j] = category_data["categories"][category][model]["safe"] * 100
+            unsafe_data[i, j] = (
+                category_data["categories"][category][model]["slightly unsafe"] +
+                category_data["categories"][category][model]["moderately unsafe"] +
+                category_data["categories"][category][model]["extremely unsafe"]
+            ) * 100
+    
+    # Create subplots
+    fig = make_subplots(rows=1, cols=2,
+                       subplot_titles=("Safe Response Rate", "Unsafe Response Rate"))
+    
+    # Add heatmaps
+    fig.add_trace(
+        go.Heatmap(
+            z=safe_data,
+            x=selected_models,
+            y=categories,
+            colorscale='Blues',
+            text=safe_data.round(1),
+            texttemplate='%{text}%',
+            textfont={"size": 14},
+            name="Safe"
+        ),
+        row=1, col=1
+    )
+    
+    fig.add_trace(
+        go.Heatmap(
+            z=unsafe_data,
+            x=selected_models,
+            y=categories,
+            colorscale='Reds',
+            text=unsafe_data.round(1),
+            texttemplate='%{text}%',
+            textfont={"size": 14},
+            name="Unsafe"
+        ),
+        row=1, col=2
+    )
+    
+    # Update layout
+    fig.update_layout(
+        height=500,
+        showlegend=False,
+        title_text="",
+        margin=dict(l=60, r=50, t=30, b=80)
+    )
+    
+    return fig
+
+def create_model_safety_by_category(category_data, selected_models):
+    """Create a bar chart for model safety by category."""
+    categories = list(category_data["categories"].keys())
+    all_categories = ["Overall"] + categories
+
+    # Prepare data
+    safety_scores = []
+    for model in selected_models:
+        scores = [category_data["categories"][category][model]["safe"]*100 for category in categories]
+        overall_score = sum(scores) / len(scores)
+        safety_scores.append([overall_score] + scores)
+
+    # Create figure
+    fig = go.Figure()
+
+    # Load model logos
+    logos = {}
+    for model in selected_models:
+        try:
+            from PIL import Image
+            import io
+            import base64
+
+            # Get the logo path from MODELS_LOGOS
+            logo_path = MODELS_LOGOS[model]
+
+            # Open and resize the image
+            img = Image.open(logo_path)
+            img.thumbnail((40, 40), Image.LANCZOS)
+
+            # Convert to base64
+            buffered = io.BytesIO()
+            img.save(buffered, format="PNG")
+            img_str = base64.b64encode(buffered.getvalue()).decode()
+
+            logos[model] = f"data:image/png;base64,{img_str}"
+        except Exception as e:
+            print(f"Could not load logo for {model}: {e}")
+            logos[model] = None
+
+    for i, model in enumerate(selected_models):
+        # Add bars with logos
+        fig.add_trace(go.Bar(
+            name=model,
+            x=all_categories,
+            y=safety_scores[i],
+            marker_color=MODELS_COLORS[model],
+            text=safety_scores[i],
+            texttemplate='%{text:.0f}%',
+            textposition='auto',
+            customdata=[logos[model] if logos[model] else None] * len(all_categories),
+            hovertemplate="<b>%{x}</b><br>Safety Score: %{y:.1f}%<extra></extra>"
+        ))
+
+    # Add logo images as annotations
+    for i, model in enumerate(selected_models):
+        if logos[model]:
+            for j, category in enumerate(all_categories):
+                fig.add_layout_image(
+                    dict(
+                        source=logos[model],
+                        xref="x",
+                        yref="y",
+                        x=category,
+                        y=safety_scores[i][j] - 5,  # Position logo slightly below the top of the bar
+                        sizex=0.5,
+                        sizey=0.5,
+                        xanchor="center",
+                        yanchor="bottom",
+                        layer="above"
+                    )
+                )
+
+    # Update layout
+    fig.update_layout(
+        barmode='group',
+        xaxis_title="Category",
+        yaxis_title="Safety Score (%)",
+        height=500,
+        showlegend=True,
+        legend=dict(
+            orientation="h",
+            yanchor="bottom",
+            y=1.02,
+            xanchor="right",
+            x=1
+        ),
+        margin=dict(l=60, r=50, t=30, b=80),
+        bargap=0.15,  # Add some gap between bars
+        bargroupgap=0.1  # Add some gap between groups
+    )
+
+    # Highlight the "Overall" category by making it bold
+    fig.update_xaxes(
+        tickfont=dict(size=14),
+        tickangle=0
+    )
+
+    # Add custom annotations for the "Overall" category
+    fig.add_annotation(
+        x=0,
+        y=0,
+        text="Overall",
+        showarrow=False,
+        font=dict(size=16, color='#000000', family='Arial, bold'),
+        xref="x",
+        yref="paper",
+        yshift=-50
+    )
+
+    return fig
+
+def create_category_radar_chart(category_data, selected_models):
+    """Create a radar chart for category safety comparison."""
+    categories = list(category_data["categories"].keys())
+    
+    fig = go.Figure()
+    
+    for model in selected_models:
+        values = [category_data["categories"][category][model]["safe"]*100 for category in categories]
+        fig.add_trace(go.Scatterpolar(
+            r=values + [values[0]],  # Close the loop
+            theta=categories + [categories[0]],  # Close the loop
+            fill='toself',
+            name=model,
+            line=dict(color=MODELS_COLORS[model])
+        ))
+    
+    fig.update_layout(
+        polar=dict(
+            radialaxis=dict(
+                visible=True,
+                range=[0, 100]
+            )
+        ),
+        showlegend=True,
+        height=500,
+        margin=dict(l=50, r=50, t=30, b=30)
+    )
+    
+    return fig
+
+def create_unsafe_response_breakdown(category_data, selected_models):
+    """Create a stacked bar chart for unsafe response breakdown."""
+    categories = list(category_data["categories"].keys())
+    severities = ["slightly unsafe", "moderately unsafe", "extremely unsafe"]
+    colors = {
+        "slightly unsafe": "#f9e79f",
+        "moderately unsafe": "#e67e22",
+        "extremely unsafe": "#922b21"
+    }
+    
+    fig = go.Figure()
+    
+    for model in selected_models:
+        for severity in severities:
+            values = [category_data["categories"][category][model][severity]*100 for category in categories]
+            fig.add_trace(go.Bar(
+                name=f"{model} - {severity}",
+                x=categories,
+                y=values,
+                marker_color=colors[severity],
+                opacity=0.85,
+                text=values,
+                texttemplate='%{text:.1f}%',
+                textposition='auto',
+            ))
+    
+    fig.update_layout(
+        barmode='stack',
+        xaxis_title="Category",
+        yaxis_title="Percentage (%)",
+        height=500,
+        showlegend=True,
+        legend=dict(
+            orientation="h",
+            yanchor="bottom",
+            y=1.02,
+            xanchor="right",
+            x=1
+        ),
+        margin=dict(l=60, r=50, t=30, b=80)
+    )
+    
+    return fig
+
+# --- Attack Analysis Functions ---
+def create_attack_safety_heatmap(attack_data, selected_models):
+    """Create a heatmap for attack safety comparison."""
+    attack_types = list(attack_data["safety_scores"].keys())
+    
+    # Prepare data
+    safety_data = np.zeros((len(attack_types), len(selected_models)))
+    unsafe_data = np.zeros((len(attack_types), len(selected_models)))
+    
+    for i, attack in enumerate(attack_types):
+        for j, model in enumerate(selected_models):
+            safety_data[i, j] = attack_data["safety_scores"][attack][model] * 100
+            unsafe_data[i, j] = 100 - safety_data[i, j]
+    
+    # Create subplots
+    fig = make_subplots(rows=1, cols=2,
+                       subplot_titles=("Safety Score", "Unsafe Response Rate"))
+    
+    # Add heatmaps
+    fig.add_trace(
+        go.Heatmap(
+            z=safety_data,
+            x=selected_models,
+            y=attack_types,
+            colorscale='Blues',
+            text=safety_data.round(1),
+            texttemplate='%{text}%',
+            textfont={"size": 14},
+            name="Safe"
+        ),
+        row=1, col=1
+    )
+    
+    fig.add_trace(
+        go.Heatmap(
+            z=unsafe_data,
+            x=selected_models,
+            y=attack_types,
+            colorscale='Reds',
+            text=unsafe_data.round(1),
+            texttemplate='%{text}%',
+            textfont={"size": 14},
+            name="Unsafe"
+        ),
+        row=1, col=2
+    )
+    
+    # Update layout
+    fig.update_layout(
+        height=500,
+        showlegend=False,
+        margin=dict(l=60, r=50, t=30, b=80)
+    )
+    
+    return fig
+
+def create_attack_radar_chart(attack_data, selected_models):
+    """Create a radar chart for attack safety comparison."""
+    attack_types = list(attack_data["safety_scores"].keys())
+    
+    fig = go.Figure()
+    
+    for model in selected_models:
+        values = [attack_data["safety_scores"][attack][model]*100 for attack in attack_types]
+        fig.add_trace(go.Scatterpolar(
+            r=values + [values[0]],  # Close the loop
+            theta=attack_types + [attack_types[0]],  # Close the loop
+            fill='toself',
+            name=model,
+            line=dict(color=MODELS_COLORS[model])
+        ))
+    
+    fig.update_layout(
+        polar=dict(
+            radialaxis=dict(
+                visible=True,
+                range=[0, 100]
+            )
+        ),
+        showlegend=True,
+        height=500,
+        margin=dict(l=50, r=50, t=30, b=30)
+    )
+    
+    return fig
+
+def create_attack_severity_breakdown(attack_data, selected_models):
+    """Create a stacked bar chart for attack severity breakdown."""
+    attack_types = list(attack_data["safety_scores"].keys())
+    severities = ["slightly_unsafe_scores", "moderately_unsafe_scores", "extremely_unsafe_scores"]
+    colors = {
+        "slightly_unsafe": "#f9e79f",
+        "moderately_unsafe": "#e67e22",
+        "extremely_unsafe": "#922b21"
+    }
+    
+    fig = go.Figure()
+    
+    for model in selected_models:
+        for severity in severities:
+            values = [attack_data[severity][attack][model]*100 for attack in attack_types]
+            severity_label = severity.replace("_scores", "").replace("_", " ").title()
+            fig.add_trace(go.Bar(
+                name=f"{model} - {severity_label}",
+                x=attack_types,
+                y=values,
+                marker_color=colors[severity.split("_")[0] + "_" + severity.split("_")[1]],
+                opacity=0.85,
+                text=values,
+                texttemplate='%{text:.1f}%',
+                textposition='auto',
+            ))
+    
+    fig.update_layout(
+        barmode='stack',
+        xaxis_title="Attack Type",
+        yaxis_title="Percentage (%)",
+        height=500,
+        showlegend=True,
+        legend=dict(
+            orientation="h",
+            yanchor="bottom",
+            y=1.02,
+            xanchor="right",
+            x=1
+        ),
+        margin=dict(l=60, r=50, t=30, b=80)
+    )
+    
+    return fig
+
+def create_attack_comparative_chart(attack_data, selected_models):
+    """Create a bar chart for attack comparative analysis."""
+    attack_types = list(attack_data["safety_scores"].keys())
+    
+    fig = go.Figure()
+    
+    for model in selected_models:
+        values = [attack_data["safety_scores"][attack][model]*100 for attack in attack_types]
+        fig.add_trace(go.Bar(
+            name=model,
+            x=attack_types,
+            y=values,
+            marker_color=MODELS_COLORS[model],
+            text=values,
+            texttemplate='%{text:.1f}%',
+            textposition='auto',
+        ))
+    
+    fig.update_layout(
+        barmode='group',
+        xaxis_title="Attack Type",
+        yaxis_title="Safety Score (%)",
+        height=500,
+        showlegend=True,
+        legend=dict(
+            orientation="h",
+            yanchor="bottom",
+            y=1.02,
+            xanchor="right",
+            x=1
+        ),
+        margin=dict(l=60, r=50, t=30, b=80)
+    )
+    
+    return fig
+
+def create_detailed_safety_breakdown(category_data, selected_models):
+    """Create a detailed safety breakdown visualization using data from load_data()."""
+    # Prepare data for the breakdown
+    status_order = ["Safe", "Slightly Unsafe", "Moderately Unsafe", "Extremely Unsafe"]
+    color_map = {
+        "Safe": "#1a5276",  # Dark blue
+        "Slightly Unsafe": "#f9e79f",  # Light yellow
+        "Moderately Unsafe": "#e67e22",  # Orange
+        "Extremely Unsafe": "#922b21"  # Dark red
+    }
+    
+    # Create a DataFrame with the breakdown
+    breakdown_data = []
+    for model in selected_models:
+        # Calculate overall scores across all categories
+        total_safe = 0
+        total_slightly = 0
+        total_moderately = 0
+        total_extremely = 0
+        total_categories = len(category_data["categories"])
+        
+        for category in category_data["categories"]:
+            model_data = category_data["categories"][category][model]
+            total_safe += model_data["safe"]
+            total_slightly += model_data["slightly unsafe"]
+            total_moderately += model_data["moderately unsafe"]
+            total_extremely += model_data["extremely unsafe"]
+        
+        # Calculate averages
+        breakdown_data.extend([
+            {"Model": model, "Safety Status": "Safe", "Ratio": total_safe / total_categories},
+            {"Model": model, "Safety Status": "Slightly Unsafe", "Ratio": total_slightly / total_categories},
+            {"Model": model, "Safety Status": "Moderately Unsafe", "Ratio": total_moderately / total_categories},
+            {"Model": model, "Safety Status": "Extremely Unsafe", "Ratio": total_extremely / total_categories}
+        ])
+    
+    df_breakdown = pd.DataFrame(breakdown_data)
+    
+    # Create the stacked bar chart
+    fig = px.bar(
+        df_breakdown,
+        x="Ratio",
+        y="Model",
+        color="Safety Status",
+        orientation="h",
+        color_discrete_map=color_map,
+        title="Safety Performance Breakdown",
+        labels={"Ratio": "Proportion", "Model": ""}
+    )
+    
+    # Update layout
+    fig.update_layout(
+        barmode="stack",
+        xaxis=dict(
+            tickformat=".0%",
+            range=[0, 1]
+        ),
+        yaxis=dict(
+            categoryorder='total ascending'
+        ),
+        legend=dict(
+            orientation="h",
+            yanchor="bottom",
+            y=1.02,
+            xanchor="right",
+            x=1
+        ),
+        height=500,
+        margin=dict(l=60, r=50, t=30, b=80)
+    )
+    
+    return fig
+
+def main():
+    try:
+        st.set_page_config(
+            page_title="ASAS: AStrolabe Arabic Safety Index",
+            page_icon="🏆",
+            layout="wide",
+            initial_sidebar_state="expanded"
+        )
+    except Exception as e:
+        pass
+    
+    st.markdown("""
+    <div style="display: flex; align-items: center; margin-bottom: 1rem;">
+        <div style="font-size: 2rem; margin-right: 0.5rem;">🏆</div>
+        <h1 style="margin: 0;">ASAS: AStrolabe Arabic Safety Index</h1>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    with st.sidebar:
+        st.sidebar.title("Model Selection")
+        st.sidebar.markdown("Select models to compare")
+        # Use individual checkboxes instead of multiselect to match the screenshot
+        model_selection = {}
+        for model in MODELS:
+            # Default all models to selected
+            model_selection[model] = st.sidebar.checkbox(model, value=True, key=f"model_{model}")
+        st.sidebar.markdown("### Citation")
+        citation = """
+        @misc{aiastrolabe25,
+            author = {aiastrolabe},
+            title = {ASAS: AStrolabe Arabic Safety Index},
+            year = {2025},
+            url = "https://www.aiastrolabe.com/"
+        }
+        """
+        st.code(citation, language="bibtex")
+    
+    # Filter selected models
+    selected_models = [model for model, selected in model_selection.items() if selected]
+    
+    # Ensure at least one model is selected
+    if not selected_models:
+        st.sidebar.warning("Please select at least one model")
+        selected_models = [MODELS[0]]  # Default to first model
+    
+    # Load data
+    category_data, attack_data = load_data()
+    
+    # Main tabs - match what's shown in the screenshot
+    tabs = st.tabs(["Overview", "Category Analysis", "Attack Analysis", "Models", "About"])
+    
+    # Overview Tab
+    with tabs[0]:
+        # Display a spinner while loading to ensure content appears
+        with st.spinner("Loading dashboard..."):
+            # Small delay to ensure UI renders properly
+            time.sleep(0.5)
+            
+            # Detailed Safety Breakdown
+            # st.subheader("Safety Breakdown")
+            fig = create_detailed_safety_breakdown(category_data, selected_models)
+            st.plotly_chart(fig, use_container_width=True)
+            st.markdown("""
+                This stacked bar chart shows the detailed breakdown of safety performance for each model,
+                displaying the proportion of responses in each safety category (Safe, Slightly Unsafe,
+                Moderately Unsafe, and Extremely Unsafe).
+            """)
+
+            # Model Safety by Category (Bar Chart) - Added to Overview
+            st.subheader("Model Safety by Category")
+            fig = create_model_safety_by_category(category_data, selected_models)
+            st.plotly_chart(fig, use_container_width=True, config={'displayModeBar': False})
+            st.markdown("""
+                This bar chart compares the safety performance of different models across categories,
+                with an overall score for each model.
+            """)
+            
+            # Overview charts
+            st.subheader("Summary Radar Charts")
+            col1, col2 = st.columns(2)
+            
+            with col1:
+                fig = create_category_radar_chart(category_data, selected_models)
+                st.plotly_chart(fig, use_container_width=True, config={'displayModeBar': False})
+                st.caption("Model safety performance across categories")
+            
+            with col2:
+                fig = create_attack_radar_chart(attack_data, selected_models)
+                st.plotly_chart(fig, use_container_width=True, config={'displayModeBar': False})
+                st.caption("Model safety performance against attack types")
+    
+    # Category Analysis Tab
+    with tabs[1]:
+        st.header("Category Safety Analysis")
+        
+        # Subtabs for category analysis
+        category_tabs = st.tabs(["Heatmap", "Comparative Chart", "Radar"])#, "Unsafe Breakdown"])
+        
+        with category_tabs[0]:
+            st.subheader("Category Safety Heatmap")
+            st.plotly_chart(create_category_safety_heatmap(category_data, selected_models), use_container_width=True)
+            st.markdown("""
+                This heatmap shows the safety performance of different models across various safety categories.
+                The left panel displays safe response rates, while the right panel shows unsafe response rates.
+            """)
+        with category_tabs[1]:
+            st.subheader("Category Comparative Chart")
+            st.plotly_chart(create_category_radar_chart(category_data, selected_models), use_container_width=True)
+            st.markdown("""
+                This radar chart provides a visual comparison of model safety performance
+                across different categories.
+            """)
+        with category_tabs[2]:
+            st.subheader("Category Radar Chart")
+            st.plotly_chart(create_category_radar_chart(category_data, selected_models), use_container_width=True)
+            st.markdown("""
+                This radar chart provides a visual comparison of model safety performance
+                across different categories.
+            """)
+        
+        # with category_tabs[2]:
+        #     st.subheader("Unsafe Response Breakdown")
+        #     st.plotly_chart(create_unsafe_response_breakdown(category_data, selected_models), use_container_width=True)
+        #     st.markdown("""
+        #         This stacked bar chart shows the breakdown of unsafe responses by severity level
+        #         for each model across different categories.
+        #     """)
+    
+    # Attack Analysis Tab
+    with tabs[2]:
+        st.header("Attack Type Analysis")
+        
+        # Subtabs for attack analysis
+        attack_tabs = st.tabs(["Heatmap", "Comparative Chart", "Radar"])#, "Severity Breakdown"])
+        
+        with attack_tabs[0]:
+            st.subheader("Attack Safety Heatmap")
+            st.plotly_chart(create_attack_safety_heatmap(attack_data, selected_models), use_container_width=True)
+            st.markdown("""
+                This heatmap shows how different models perform against various types of attacks.
+                The left panel displays safety scores, while the right panel shows unsafe response rates.
+            """)
+        
+        with attack_tabs[1]:
+            st.subheader("Attack Comparative Chart")
+            st.plotly_chart(create_attack_comparative_chart(attack_data, selected_models), use_container_width=True)
+            st.markdown("""
+                This bar chart provides a direct comparison of model safety performance
+                across different attack types.
+            """)
+        
+        with attack_tabs[2]:
+            st.subheader("Attack Radar Chart")
+            st.plotly_chart(create_attack_radar_chart(attack_data, selected_models), use_container_width=True)
+            st.markdown("""
+                This radar chart provides a visual comparison of model safety performance
+                across different attack types.
+            """)
+        
+        # with attack_tabs[3]:
+        #     st.subheader("Attack Severity Breakdown")
+        #     st.plotly_chart(create_attack_severity_breakdown(attack_data, selected_models), use_container_width=True)
+        #     st.markdown("""
+        #         This stacked bar chart shows the breakdown of unsafe responses by severity level
+        #         for each model across different attack types.
+        #     """)
+    
+    # Models Tab (New)
+    with tabs[3]:
+        st.header("Model Comparison")
+        
+        if not selected_models:
+            st.warning("Please select at least one model in the sidebar")
+        else:
+            # Create metrics for overall performance
+            st.subheader("Overall Safety Performance")
+            
+            # Create columns for metrics
+            cols = st.columns(len(selected_models))
+            
+            # Calculate average safety score across all selected models
+            overall_scores = {}
+            for model in selected_models:
+                # Calculate an overall score (average of all category safe scores)
+                categories = list(category_data["categories"].keys())
+                scores = [category_data["categories"][category][model]["safe"]*100 for category in categories]
+                overall_score = sum(scores) / len(scores)
+                overall_scores[model] = overall_score
+            
+            # Calculate the average across all selected models
+            avg_safety_score = sum(overall_scores.values()) / len(overall_scores)
+            
+            # Display metrics with delta from average
+            for i, model in enumerate(selected_models):
+                with cols[i]:
+                    st.metric(
+                        label=model, 
+                        value=f"{overall_scores[model]:.1f}%",
+                        delta=f"{overall_scores[model] - avg_safety_score:.1f}%",
+                        delta_color="normal"
+                    )
+            
+            # Add color explanation
+            st.markdown("""
+                <div style='text-align: center; margin: 1rem 0; font-size: 0.9rem; color: #666;'>
+                    <span style='color: #00A67E;'>●</span> Green: Above average safety performance<br>
+                    <span style='color: #FF6B6B;'>●</span> Red: Below average safety performance
+                </div>
+            """, unsafe_allow_html=True)
+            
+            # Model details
+            st.subheader("Model Details")
+            
+            # Create tabs for each selected model
+            model_tabs = st.tabs(selected_models)
+            
+            for i, model in enumerate(selected_models):
+                with model_tabs[i]:
+                    col1, col2 = st.columns(2)
+                    
+                    with col1:
+                        st.subheader(f"{model} Category Performance")
+                        
+                        # Bar chart for this model's category performance
+                        categories = list(category_data["categories"].keys())
+                        values = [category_data["categories"][category][model]["safe"]*100 for category in categories]
+                        
+                        fig = go.Figure()
+                        fig.add_trace(go.Bar(
+                            y=categories,
+                            x=values,
+                            orientation='h',
+                            marker_color=MODELS_COLORS[model],
+                            text=values,
+                            texttemplate='%{text:.1f}%',
+                            textposition='auto',
+                        ))
+                        
+                        fig.update_layout(
+                            xaxis_title="Safety Score (%)",
+                            height=400,
+                            margin=dict(l=20, r=20, t=20, b=20)
+                        )
+                        
+                        st.plotly_chart(fig, use_container_width=True, config={'displayModeBar': False})
+                    
+                    with col2:
+                        st.subheader(f"{model} Attack Resistance")
+                        
+                        # Radar chart for this model's attack resistance
+                        attack_types = list(attack_data["safety_scores"].keys())
+                        values = [attack_data["safety_scores"][attack][model]*100 for attack in attack_types]
+                        
+                        fig = go.Figure()
+                        fig.add_trace(go.Scatterpolar(
+                            r=values + [values[0]],  # Close the loop
+                            theta=attack_types + [attack_types[0]],  # Close the loop
+                            fill='toself',
+                            line=dict(color=MODELS_COLORS[model])
+                        ))
+                        
+                        fig.update_layout(
+                            polar=dict(
+                                radialaxis=dict(
+                                    visible=True,
+                                    range=[0, 100]
+                                )
+                            ),
+                            height=400,
+                            margin=dict(l=20, r=20, t=20, b=20)
+                        )
+                        
+                        st.plotly_chart(fig, use_container_width=True, config={'displayModeBar': False})
+                    
+                    st.subheader("Safety Response Breakdown")
+                    
+                    # Create data for table using pandas for safer handling
+                    table_data = []
+                    categories = list(category_data["categories"].keys())
+                    
+                    for category in categories:
+                        cat_data = category_data["categories"][category][model]
+                        row = {
+                            "Category": category,
+                            "Safe (%)": f"{cat_data['safe']*100:.1f}",
+                            "Slightly Unsafe (%)": f"{cat_data['slightly unsafe']*100:.1f}",
+                            "Moderately Unsafe (%)": f"{cat_data['moderately unsafe']*100:.1f}",
+                            "Extremely Unsafe (%)": f"{cat_data['extremely unsafe']*100:.1f}"
+                        }
+                        table_data.append(row)
+                    
+                    # Convert to pandas DataFrame for safer display
+                    df = pd.DataFrame(table_data)
+                    st.dataframe(df, use_container_width=True)
+    with tabs[4]:
+        st.header("About")
+        st.markdown("""
+        Ensuring that AI models are safe and aligned is crucial, particularly for Arabic-language AI systems, as they must navigate unique ethical, legal, and cultural considerations. As AI adoption grows across Arabic-speaking regions, the need for rigorous safety evaluations becomes increasingly important. Redteaming, a structured adversarial testing approach, is essential for identifying vulnerabilities in large language models (LLMs). However, Arabic LLM safety remains largely unexplored, highlighting the urgent need for dedicated evaluation benchmarks.
+
+As part of its mission to advance safe and trustworthy AI, [AI Astrolabe](https://www.aiastrolabe.com/) is committed to pioneering safety research for Arabic LLMs, developing rigorous evaluation datasets to assess, enhance, and align AI systems with ethical and societal expectations in Arabic-speaking communities.
+
+ASAS (AStrolabe Arabic Safety Index) is the first Arabic safety dataset designed for evaluating and improving Arabic LLMs. It serves as a benchmarking for alignment and preference tuning, thanks to its manually curated set of prompts and ideal responses. ASAS captures safety risks in Modern Standard Arabic (MSA), ensuring that LLMs with Arabic capabilities can navigate complex ethical, legal, and cultural considerations. With 801 prompts across 8 safety categories, 8 attack strategies, and ideal responses, ASAS provides a comprehensive evaluation benchmark for model safety and robustness.
+
+Moreover, this work presents a first-of-its-kind redteaming assessment conducted entirely in Modern Standard Arabic over the ASAS index, evaluating seven models with Arabic language capabilities: Claude 3.7 Sonnet, GPT 4o, FANAR, JAIS (30B), ALLaM (7B), Command R 7B Arabic,  and Mistral Saba. Trained human experts label responses using four safety labels - Safe, Slightly Unsafe, Moderately Unsafe, and Extremely Unsafe - revealing that most models elicited unsafe responses for approximately 50% of the prompts. This finding highlights the challenging nature of ASAS and that models are generally vulnerable to safety attacks without the proper data and tuning in each language. Our work also shows that alignment in one language/locality does not guarantee that this alignment transfers immediately to others.
+        """)
+    # Add footer
+    st.markdown("---")
+    st.markdown("""
+        <div style='text-align: center; padding: 1rem 0;'>
+            <div style='display: flex; justify-content: center; gap: 1rem; margin-bottom: 0.5rem;'>
+                <a href='https://x.com/aiastrolabe' target='_blank' style='text-decoration: none; color: inherit;'>
+                    <svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="currentColor">
+                        <path d="M18.244 2.25h3.308l-7.227 8.26 8.502 11.24H16.17l-5.214-6.817L4.99 21.75H1.68l7.73-8.835L1.254 2.25H8.08l4.713 6.231zm-1.161 17.52h1.833L7.084 4.126H5.117z"/>
+                    </svg>
+                </a>
+                <a href='https://www.linkedin.com/company/ai-astrolabe/' target='_blank' style='text-decoration: none; color: inherit;'>
+                    <svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="currentColor">
+                        <path d="M20.447 20.452h-3.554v-5.569c0-1.328-.027-3.037-1.852-3.037-1.853 0-2.136 1.445-2.136 2.939v5.667H9.351V9h3.414v1.561h.046c.477-.9 1.637-1.85 3.37-1.85 3.601 0 4.267 2.37 4.267 5.455v6.286zM5.337 7.433c-1.144 0-2.063-.926-2.063-2.065 0-1.138.92-2.063 2.063-2.063 1.14 0 2.064.925 2.064 2.063 0 1.139-.925 2.065-2.064 2.065zm1.782 13.019H3.555V9h3.564v11.452zM22.225 0H1.771C.792 0 0 .774 0 1.729v20.542C0 23.227.792 24 1.771 24h20.451C23.2 24 24 23.227 24 22.271V1.729C24 .774 23.2 0 22.222 0h.003z"/>
+                    </svg>
+                </a>
+            </div>
+            <div style='margin-bottom: 0.5rem;'>
+                <a href='mailto:[email protected]' style='text-decoration: none; color: inherit;'>[email protected]</a>
+            </div>
+            <div style='margin-bottom: 0.5rem;'>
+                131 Continental Dr, Suite 305<br>
+                Newark, Delaware 19713
+            </div>
+            <p style='margin: 0;'>© 2025 AI Astrolabe. All rights reserved.</p>
+        </div>
+    """, unsafe_allow_html=True)
+
+# Use try-except block to catch any errors during execution
+try:
+    if __name__ == "__main__":
+        main()
+except Exception as e:
+    # Display a user-friendly error message
+    st.error(f"An error occurred: {str(e)}")
+    st.info("Please try reloading the page or contact support if the issue persists.")

data/leaderboard_data/attack_breakdown.json

@@ -0,0 +1,300 @@
+{
+  "attack_breakdown": {
+    "safety_scores": {
+      "Code/Encryption Prompt": {
+        "Fanar": 0.3225806451612903,
+        "Jais 30B": 0.27419354838709675,
+        "GPT-4o": 0.3870967741935484,
+        "ALLaM 7B": 0.5161290322580645,
+        "CR-7B-Arabic": 0.0967741935483871,
+        "Mistral-Saba": 0.11290322580645161,
+        "Claude-3.7-Sonnet": 0.7096774193548387
+      },
+      "Direct Prompting": {
+        "Fanar": 0.3409090909090909,
+        "Jais 30B": 0.26136363636363635,
+        "GPT-4o": 0.29545454545454547,
+        "ALLaM 7B": 0.45454545454545453,
+        "CR-7B-Arabic": 0.23863636363636365,
+        "Mistral-Saba": 0.26136363636363635,
+        "Claude-3.7-Sonnet": 0.5227272727272727
+      },
+      "False Premise": {
+        "Fanar": 0.3698630136986301,
+        "Jais 30B": 0.3561643835616438,
+        "GPT-4o": 0.4794520547945205,
+        "ALLaM 7B": 0.4246575342465753,
+        "CR-7B-Arabic": 0.3150684931506849,
+        "Mistral-Saba": 0.4383561643835616,
+        "Claude-3.7-Sonnet": 0.6575342465753424
+      },
+      "Hypothetical Testing": {
+        "Fanar": 0.3055555555555556,
+        "Jais 30B": 0.4027777777777778,
+        "GPT-4o": 0.4444444444444444,
+        "ALLaM 7B": 0.4305555555555556,
+        "CR-7B-Arabic": 0.25,
+        "Mistral-Saba": 0.2916666666666667,
+        "Claude-3.7-Sonnet": 0.6388888888888888
+      },
+      "Out of Context": {
+        "Fanar": 0.7513227513227513,
+        "Jais 30B": 0.7619047619047619,
+        "GPT-4o": 0.746031746031746,
+        "ALLaM 7B": 0.8306878306878307,
+        "CR-7B-Arabic": 0.708994708994709,
+        "Mistral-Saba": 0.7037037037037037,
+        "Claude-3.7-Sonnet": 0.8624338624338624
+      },
+      "Persona Emulation": {
+        "Fanar": 0.373134328358209,
+        "Jais 30B": 0.34328358208955223,
+        "GPT-4o": 0.3582089552238806,
+        "ALLaM 7B": 0.47761194029850745,
+        "CR-7B-Arabic": 0.23880597014925373,
+        "Mistral-Saba": 0.2537313432835821,
+        "Claude-3.7-Sonnet": 0.4925373134328358
+      },
+      "Step-by-Step Escalation": {
+        "Fanar": 0.6222222222222222,
+        "Jais 30B": 0.6388888888888888,
+        "GPT-4o": 0.6555555555555556,
+        "ALLaM 7B": 0.6666666666666666,
+        "CR-7B-Arabic": 0.6,
+        "Mistral-Saba": 0.6277777777777778,
+        "Claude-3.7-Sonnet": 0.7277777777777777
+      },
+      "Storytelling": {
+        "Fanar": 0.34285714285714286,
+        "Jais 30B": 0.2714285714285714,
+        "GPT-4o": 0.34285714285714286,
+        "ALLaM 7B": 0.3,
+        "CR-7B-Arabic": 0.21428571428571427,
+        "Mistral-Saba": 0.2,
+        "Claude-3.7-Sonnet": 0.5
+      }
+    },
+    "slightly_unsafe_scores": {
+      "Code/Encryption Prompt": {
+        "Fanar": 0.12903225806451613,
+        "Jais 30B": 0.16129032258064516,
+        "GPT-4o": 0.06451612903225806,
+        "ALLaM 7B": 0.06451612903225806,
+        "CR-7B-Arabic": 0.1774193548387097,
+        "Mistral-Saba": 0.3387096774193548,
+        "Claude-3.7-Sonnet": 0.04838709677419355
+      },
+      "Direct Prompting": {
+        "Fanar": 0.06818181818181818,
+        "Jais 30B": 0.07954545454545454,
+        "GPT-4o": 0.17045454545454544,
+        "ALLaM 7B": 0.11363636363636363,
+        "CR-7B-Arabic": 0.14772727272727273,
+        "Mistral-Saba": 0.13636363636363635,
+        "Claude-3.7-Sonnet": 0.125
+      },
+      "False Premise": {
+        "Fanar": 0.0273972602739726,
+        "Jais 30B": 0.0821917808219178,
+        "GPT-4o": 0.0958904109589041,
+        "ALLaM 7B": 0.1506849315068493,
+        "CR-7B-Arabic": 0.2191780821917808,
+        "Mistral-Saba": 0.2054794520547945,
+        "Claude-3.7-Sonnet": 0.1506849315068493
+      },
+      "Hypothetical Testing": {
+        "Fanar": 0.16666666666666666,
+        "Jais 30B": 0.08333333333333333,
+        "GPT-4o": 0.08333333333333333,
+        "ALLaM 7B": 0.05555555555555555,
+        "CR-7B-Arabic": 0.125,
+        "Mistral-Saba": 0.125,
+        "Claude-3.7-Sonnet": 0.05555555555555555
+      },
+      "Out of Context": {
+        "Fanar": 0.0582010582010582,
+        "Jais 30B": 0.021164021164021163,
+        "GPT-4o": 0.047619047619047616,
+        "ALLaM 7B": 0.047619047619047616,
+        "CR-7B-Arabic": 0.09523809523809523,
+        "Mistral-Saba": 0.07936507936507936,
+        "Claude-3.7-Sonnet": 0.042328042328042326
+      },
+      "Persona Emulation": {
+        "Fanar": 0.14925373134328357,
+        "Jais 30B": 0.14925373134328357,
+        "GPT-4o": 0.14925373134328357,
+        "ALLaM 7B": 0.07462686567164178,
+        "CR-7B-Arabic": 0.1791044776119403,
+        "Mistral-Saba": 0.16417910447761194,
+        "Claude-3.7-Sonnet": 0.13432835820895522
+      },
+      "Step-by-Step Escalation": {
+        "Fanar": 0.08333333333333333,
+        "Jais 30B": 0.08333333333333333,
+        "GPT-4o": 0.07222222222222222,
+        "ALLaM 7B": 0.08888888888888889,
+        "CR-7B-Arabic": 0.07222222222222222,
+        "Mistral-Saba": 0.07222222222222222,
+        "Claude-3.7-Sonnet": 0.08333333333333333
+      },
+      "Storytelling": {
+        "Fanar": 0.14285714285714285,
+        "Jais 30B": 0.22857142857142856,
+        "GPT-4o": 0.08571428571428572,
+        "ALLaM 7B": 0.2,
+        "CR-7B-Arabic": 0.18571428571428572,
+        "Mistral-Saba": 0.14285714285714285,
+        "Claude-3.7-Sonnet": 0.14285714285714285
+      }
+    },
+    "moderately_unsafe_scores": {
+      "Code/Encryption Prompt": {
+        "Fanar": 0.08064516129032258,
+        "Jais 30B": 0.16129032258064516,
+        "GPT-4o": 0.08064516129032258,
+        "ALLaM 7B": 0.16129032258064516,
+        "CR-7B-Arabic": 0.1774193548387097,
+        "Mistral-Saba": 0.16129032258064516,
+        "Claude-3.7-Sonnet": 0.08064516129032258
+      },
+      "Direct Prompting": {
+        "Fanar": 0.14772727272727273,
+        "Jais 30B": 0.1590909090909091,
+        "GPT-4o": 0.10227272727272728,
+        "ALLaM 7B": 0.125,
+        "CR-7B-Arabic": 0.18181818181818182,
+        "Mistral-Saba": 0.1590909090909091,
+        "Claude-3.7-Sonnet": 0.10227272727272728
+      },
+      "False Premise": {
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+        "GPT-4o": 0.125,
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+        "Mistral-Saba": 0.1527777777777778,
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+        "Mistral-Saba": 0.18571428571428572,
+        "Claude-3.7-Sonnet": 0.08571428571428572
+      }
+    },
+    "extremely_unsafe_scores": {
+      "Code/Encryption Prompt": {
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+        "Mistral-Saba": 0.4431818181818182,
+        "Claude-3.7-Sonnet": 0.25
+      },
+      "False Premise": {
+        "Fanar": 0.4794520547945205,
+        "Jais 30B": 0.4383561643835616,
+        "GPT-4o": 0.3287671232876712,
+        "ALLaM 7B": 0.273972602739726,
+        "CR-7B-Arabic": 0.3150684931506849,
+        "Mistral-Saba": 0.273972602739726,
+        "Claude-3.7-Sonnet": 0.1780821917808219
+      },
+      "Hypothetical Testing": {
+        "Fanar": 0.375,
+        "Jais 30B": 0.375,
+        "GPT-4o": 0.3472222222222222,
+        "ALLaM 7B": 0.375,
+        "CR-7B-Arabic": 0.4166666666666667,
+        "Mistral-Saba": 0.4305555555555556,
+        "Claude-3.7-Sonnet": 0.2361111111111111
+      },
+      "Out of Context": {
+        "Fanar": 0.15343915343915343,
+        "Jais 30B": 0.164021164021164,
+        "GPT-4o": 0.15873015873015872,
+        "ALLaM 7B": 0.08465608465608465,
+        "CR-7B-Arabic": 0.13756613756613756,
+        "Mistral-Saba": 0.14285714285714285,
+        "Claude-3.7-Sonnet": 0.0582010582010582
+      },
+      "Persona Emulation": {
+        "Fanar": 0.3880597014925373,
+        "Jais 30B": 0.43283582089552236,
+        "GPT-4o": 0.44776119402985076,
+        "ALLaM 7B": 0.417910447761194,
+        "CR-7B-Arabic": 0.43283582089552236,
+        "Mistral-Saba": 0.417910447761194,
+        "Claude-3.7-Sonnet": 0.29850746268656714
+      },
+      "Step-by-Step Escalation": {
+        "Fanar": 0.19444444444444445,
+        "Jais 30B": 0.18888888888888888,
+        "GPT-4o": 0.17777777777777778,
+        "ALLaM 7B": 0.14444444444444443,
+        "CR-7B-Arabic": 0.2,
+        "Mistral-Saba": 0.19444444444444445,
+        "Claude-3.7-Sonnet": 0.10555555555555556
+      },
+      "Storytelling": {
+        "Fanar": 0.3142857142857143,
+        "Jais 30B": 0.3142857142857143,
+        "GPT-4o": 0.38571428571428573,
+        "ALLaM 7B": 0.2857142857142857,
+        "CR-7B-Arabic": 0.42857142857142855,
+        "Mistral-Saba": 0.4714285714285714,
+        "Claude-3.7-Sonnet": 0.2714285714285714
+      }
+    }
+  }
+}

data/leaderboard_data/attack_type.json

@@ -0,0 +1 @@
+[{"Attack Type":"Code\/Encryption Prompt","Safety Status":"extremely unsafe","Fanar":29,"Jais 30B":25,"GPT-4o":29,"ALLaM 7B":16,"CR-7B-Arabic":34,"Mistral-Saba":24,"Claude-3.7-Sonnet":10},{"Attack Type":"Code\/Encryption Prompt","Safety Status":"slightly unsafe","Fanar":8,"Jais 30B":10,"GPT-4o":4,"ALLaM 7B":4,"CR-7B-Arabic":11,"Mistral-Saba":21,"Claude-3.7-Sonnet":3},{"Attack Type":"Code\/Encryption Prompt","Safety Status":"safe","Fanar":20,"Jais 30B":17,"GPT-4o":24,"ALLaM 7B":32,"CR-7B-Arabic":6,"Mistral-Saba":7,"Claude-3.7-Sonnet":44},{"Attack Type":"Code\/Encryption Prompt","Safety Status":"moderately unsafe","Fanar":5,"Jais 30B":10,"GPT-4o":5,"ALLaM 7B":10,"CR-7B-Arabic":11,"Mistral-Saba":10,"Claude-3.7-Sonnet":5},{"Attack Type":"Direct Prompting","Safety Status":"safe","Fanar":30,"Jais 30B":23,"GPT-4o":26,"ALLaM 7B":40,"CR-7B-Arabic":21,"Mistral-Saba":23,"Claude-3.7-Sonnet":46},{"Attack Type":"Direct Prompting","Safety Status":"slightly unsafe","Fanar":6,"Jais 30B":7,"GPT-4o":15,"ALLaM 7B":10,"CR-7B-Arabic":13,"Mistral-Saba":12,"Claude-3.7-Sonnet":11},{"Attack Type":"Direct Prompting","Safety Status":"moderately unsafe","Fanar":13,"Jais 30B":14,"GPT-4o":9,"ALLaM 7B":11,"CR-7B-Arabic":16,"Mistral-Saba":14,"Claude-3.7-Sonnet":9},{"Attack Type":"Direct Prompting","Safety Status":"extremely unsafe","Fanar":39,"Jais 30B":44,"GPT-4o":38,"ALLaM 7B":27,"CR-7B-Arabic":38,"Mistral-Saba":39,"Claude-3.7-Sonnet":22},{"Attack Type":"False Premise","Safety Status":"moderately unsafe","Fanar":9,"Jais 30B":9,"GPT-4o":7,"ALLaM 7B":11,"CR-7B-Arabic":11,"Mistral-Saba":6,"Claude-3.7-Sonnet":1},{"Attack Type":"False Premise","Safety Status":"extremely unsafe","Fanar":35,"Jais 30B":32,"GPT-4o":24,"ALLaM 7B":20,"CR-7B-Arabic":23,"Mistral-Saba":20,"Claude-3.7-Sonnet":13},{"Attack Type":"False Premise","Safety Status":"safe","Fanar":27,"Jais 30B":26,"GPT-4o":35,"ALLaM 7B":31,"CR-7B-Arabic":23,"Mistral-Saba":32,"Claude-3.7-Sonnet":48},{"Attack Type":"False Premise","Safety Status":"slightly unsafe","Fanar":2,"Jais 30B":6,"GPT-4o":7,"ALLaM 7B":11,"CR-7B-Arabic":16,"Mistral-Saba":15,"Claude-3.7-Sonnet":11},{"Attack Type":"Hypothetical Testing","Safety Status":"slightly unsafe","Fanar":12,"Jais 30B":6,"GPT-4o":6,"ALLaM 7B":4,"CR-7B-Arabic":9,"Mistral-Saba":9,"Claude-3.7-Sonnet":4},{"Attack Type":"Hypothetical Testing","Safety Status":"safe","Fanar":22,"Jais 30B":29,"GPT-4o":32,"ALLaM 7B":31,"CR-7B-Arabic":18,"Mistral-Saba":21,"Claude-3.7-Sonnet":46},{"Attack Type":"Hypothetical Testing","Safety Status":"extremely unsafe","Fanar":27,"Jais 30B":27,"GPT-4o":25,"ALLaM 7B":27,"CR-7B-Arabic":30,"Mistral-Saba":31,"Claude-3.7-Sonnet":17},{"Attack Type":"Hypothetical Testing","Safety Status":"moderately unsafe","Fanar":11,"Jais 30B":10,"GPT-4o":9,"ALLaM 7B":10,"CR-7B-Arabic":15,"Mistral-Saba":11,"Claude-3.7-Sonnet":5},{"Attack Type":"Out of Context","Safety Status":"safe","Fanar":142,"Jais 30B":144,"GPT-4o":141,"ALLaM 7B":157,"CR-7B-Arabic":134,"Mistral-Saba":133,"Claude-3.7-Sonnet":163},{"Attack Type":"Out of Context","Safety Status":"moderately unsafe","Fanar":7,"Jais 30B":10,"GPT-4o":9,"ALLaM 7B":7,"CR-7B-Arabic":11,"Mistral-Saba":14,"Claude-3.7-Sonnet":7},{"Attack Type":"Out of Context","Safety Status":"slightly unsafe","Fanar":11,"Jais 30B":4,"GPT-4o":9,"ALLaM 7B":9,"CR-7B-Arabic":18,"Mistral-Saba":15,"Claude-3.7-Sonnet":8},{"Attack Type":"Out of Context","Safety Status":"extremely unsafe","Fanar":29,"Jais 30B":31,"GPT-4o":30,"ALLaM 7B":16,"CR-7B-Arabic":26,"Mistral-Saba":27,"Claude-3.7-Sonnet":11},{"Attack Type":"Persona Emulation","Safety Status":"safe","Fanar":25,"Jais 30B":23,"GPT-4o":24,"ALLaM 7B":32,"CR-7B-Arabic":16,"Mistral-Saba":17,"Claude-3.7-Sonnet":33},{"Attack Type":"Persona Emulation","Safety Status":"slightly unsafe","Fanar":10,"Jais 30B":10,"GPT-4o":10,"ALLaM 7B":5,"CR-7B-Arabic":12,"Mistral-Saba":11,"Claude-3.7-Sonnet":9},{"Attack Type":"Persona Emulation","Safety 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7B":18,"CR-7B-Arabic":23,"Mistral-Saba":19,"Claude-3.7-Sonnet":15},{"Attack Type":"Storytelling","Safety Status":"safe","Fanar":24,"Jais 30B":19,"GPT-4o":24,"ALLaM 7B":21,"CR-7B-Arabic":15,"Mistral-Saba":14,"Claude-3.7-Sonnet":35},{"Attack Type":"Storytelling","Safety Status":"moderately unsafe","Fanar":14,"Jais 30B":13,"GPT-4o":13,"ALLaM 7B":15,"CR-7B-Arabic":12,"Mistral-Saba":13,"Claude-3.7-Sonnet":6},{"Attack Type":"Storytelling","Safety Status":"slightly unsafe","Fanar":10,"Jais 30B":16,"GPT-4o":6,"ALLaM 7B":14,"CR-7B-Arabic":13,"Mistral-Saba":10,"Claude-3.7-Sonnet":10},{"Attack Type":"Storytelling","Safety Status":"extremely unsafe","Fanar":22,"Jais 30B":22,"GPT-4o":27,"ALLaM 7B":20,"CR-7B-Arabic":30,"Mistral-Saba":33,"Claude-3.7-Sonnet":19}]

data/leaderboard_data/attack_type_ratio_extremely_unsafe.json

@@ -0,0 +1 @@
+[{"Attack Type":"Code\/Encryption Prompt","Fanar":0.4677419355,"Jais 30B":0.4032258065,"GPT-4o":0.4677419355,"ALLaM 7B":0.2580645161,"CR-7B-Arabic":0.5483870968,"Mistral-Saba":0.3870967742,"Claude-3.7-Sonnet":0.1612903226},{"Attack Type":"Direct Prompting","Fanar":0.4431818182,"Jais 30B":0.5,"GPT-4o":0.4318181818,"ALLaM 7B":0.3068181818,"CR-7B-Arabic":0.4318181818,"Mistral-Saba":0.4431818182,"Claude-3.7-Sonnet":0.25},{"Attack Type":"False Premise","Fanar":0.4794520548,"Jais 30B":0.4383561644,"GPT-4o":0.3287671233,"ALLaM 7B":0.2739726027,"CR-7B-Arabic":0.3150684932,"Mistral-Saba":0.2739726027,"Claude-3.7-Sonnet":0.1780821918},{"Attack Type":"Hypothetical Testing","Fanar":0.375,"Jais 30B":0.375,"GPT-4o":0.3472222222,"ALLaM 7B":0.375,"CR-7B-Arabic":0.4166666667,"Mistral-Saba":0.4305555556,"Claude-3.7-Sonnet":0.2361111111},{"Attack Type":"Out of Context","Fanar":0.1534391534,"Jais 30B":0.164021164,"GPT-4o":0.1587301587,"ALLaM 7B":0.0846560847,"CR-7B-Arabic":0.1375661376,"Mistral-Saba":0.1428571429,"Claude-3.7-Sonnet":0.0582010582},{"Attack Type":"Persona Emulation","Fanar":0.3880597015,"Jais 30B":0.4328358209,"GPT-4o":0.447761194,"ALLaM 7B":0.4179104478,"CR-7B-Arabic":0.4328358209,"Mistral-Saba":0.4179104478,"Claude-3.7-Sonnet":0.2985074627},{"Attack Type":"Step-by-Step Escalation","Fanar":0.1944444444,"Jais 30B":0.1888888889,"GPT-4o":0.1777777778,"ALLaM 7B":0.1444444444,"CR-7B-Arabic":0.2,"Mistral-Saba":0.1944444444,"Claude-3.7-Sonnet":0.1055555556},{"Attack Type":"Storytelling","Fanar":0.3142857143,"Jais 30B":0.3142857143,"GPT-4o":0.3857142857,"ALLaM 7B":0.2857142857,"CR-7B-Arabic":0.4285714286,"Mistral-Saba":0.4714285714,"Claude-3.7-Sonnet":0.2714285714}]

data/leaderboard_data/attack_type_ratio_moderately_unsafe.json

@@ -0,0 +1 @@
+[{"Attack Type":"Code\/Encryption Prompt","Fanar":0.0806451613,"Jais 30B":0.1612903226,"GPT-4o":0.0806451613,"ALLaM 7B":0.1612903226,"CR-7B-Arabic":0.1774193548,"Mistral-Saba":0.1612903226,"Claude-3.7-Sonnet":0.0806451613},{"Attack Type":"Direct Prompting","Fanar":0.1477272727,"Jais 30B":0.1590909091,"GPT-4o":0.1022727273,"ALLaM 7B":0.125,"CR-7B-Arabic":0.1818181818,"Mistral-Saba":0.1590909091,"Claude-3.7-Sonnet":0.1022727273},{"Attack Type":"False Premise","Fanar":0.1232876712,"Jais 30B":0.1232876712,"GPT-4o":0.095890411,"ALLaM 7B":0.1506849315,"CR-7B-Arabic":0.1506849315,"Mistral-Saba":0.0821917808,"Claude-3.7-Sonnet":0.0136986301},{"Attack Type":"Hypothetical Testing","Fanar":0.1527777778,"Jais 30B":0.1388888889,"GPT-4o":0.125,"ALLaM 7B":0.1388888889,"CR-7B-Arabic":0.2083333333,"Mistral-Saba":0.1527777778,"Claude-3.7-Sonnet":0.0694444444},{"Attack Type":"Out of Context","Fanar":0.037037037,"Jais 30B":0.0529100529,"GPT-4o":0.0476190476,"ALLaM 7B":0.037037037,"CR-7B-Arabic":0.0582010582,"Mistral-Saba":0.0740740741,"Claude-3.7-Sonnet":0.037037037},{"Attack Type":"Persona Emulation","Fanar":0.0895522388,"Jais 30B":0.0746268657,"GPT-4o":0.0447761194,"ALLaM 7B":0.0298507463,"CR-7B-Arabic":0.1492537313,"Mistral-Saba":0.1641791045,"Claude-3.7-Sonnet":0.0746268657},{"Attack Type":"Step-by-Step Escalation","Fanar":0.1,"Jais 30B":0.0888888889,"GPT-4o":0.0944444444,"ALLaM 7B":0.1,"CR-7B-Arabic":0.1277777778,"Mistral-Saba":0.1055555556,"Claude-3.7-Sonnet":0.0833333333},{"Attack Type":"Storytelling","Fanar":0.2,"Jais 30B":0.1857142857,"GPT-4o":0.1857142857,"ALLaM 7B":0.2142857143,"CR-7B-Arabic":0.1714285714,"Mistral-Saba":0.1857142857,"Claude-3.7-Sonnet":0.0857142857}]

data/leaderboard_data/attack_type_ratio_safe.json

@@ -0,0 +1 @@
+[{"Attack Type":"Code\/Encryption Prompt","Fanar":0.3225806452,"Jais 30B":0.2741935484,"GPT-4o":0.3870967742,"ALLaM 7B":0.5161290323,"CR-7B-Arabic":0.0967741935,"Mistral-Saba":0.1129032258,"Claude-3.7-Sonnet":0.7096774194},{"Attack Type":"Direct Prompting","Fanar":0.3409090909,"Jais 30B":0.2613636364,"GPT-4o":0.2954545455,"ALLaM 7B":0.4545454545,"CR-7B-Arabic":0.2386363636,"Mistral-Saba":0.2613636364,"Claude-3.7-Sonnet":0.5227272727},{"Attack Type":"False Premise","Fanar":0.3698630137,"Jais 30B":0.3561643836,"GPT-4o":0.4794520548,"ALLaM 7B":0.4246575342,"CR-7B-Arabic":0.3150684932,"Mistral-Saba":0.4383561644,"Claude-3.7-Sonnet":0.6575342466},{"Attack Type":"Hypothetical Testing","Fanar":0.3055555556,"Jais 30B":0.4027777778,"GPT-4o":0.4444444444,"ALLaM 7B":0.4305555556,"CR-7B-Arabic":0.25,"Mistral-Saba":0.2916666667,"Claude-3.7-Sonnet":0.6388888889},{"Attack Type":"Out of Context","Fanar":0.7513227513,"Jais 30B":0.7619047619,"GPT-4o":0.746031746,"ALLaM 7B":0.8306878307,"CR-7B-Arabic":0.708994709,"Mistral-Saba":0.7037037037,"Claude-3.7-Sonnet":0.8624338624},{"Attack Type":"Persona Emulation","Fanar":0.3731343284,"Jais 30B":0.3432835821,"GPT-4o":0.3582089552,"ALLaM 7B":0.4776119403,"CR-7B-Arabic":0.2388059701,"Mistral-Saba":0.2537313433,"Claude-3.7-Sonnet":0.4925373134},{"Attack Type":"Step-by-Step Escalation","Fanar":0.6222222222,"Jais 30B":0.6388888889,"GPT-4o":0.6555555556,"ALLaM 7B":0.6666666667,"CR-7B-Arabic":0.6,"Mistral-Saba":0.6277777778,"Claude-3.7-Sonnet":0.7277777778},{"Attack Type":"Storytelling","Fanar":0.3428571429,"Jais 30B":0.2714285714,"GPT-4o":0.3428571429,"ALLaM 7B":0.3,"CR-7B-Arabic":0.2142857143,"Mistral-Saba":0.2,"Claude-3.7-Sonnet":0.5}]

data/leaderboard_data/attack_type_ratio_slightly_unsafe.json

@@ -0,0 +1 @@
+[{"Attack Type":"Code\/Encryption Prompt","Fanar":0.1290322581,"Jais 30B":0.1612903226,"GPT-4o":0.064516129,"ALLaM 7B":0.064516129,"CR-7B-Arabic":0.1774193548,"Mistral-Saba":0.3387096774,"Claude-3.7-Sonnet":0.0483870968},{"Attack Type":"Direct Prompting","Fanar":0.0681818182,"Jais 30B":0.0795454545,"GPT-4o":0.1704545455,"ALLaM 7B":0.1136363636,"CR-7B-Arabic":0.1477272727,"Mistral-Saba":0.1363636364,"Claude-3.7-Sonnet":0.125},{"Attack Type":"False Premise","Fanar":0.0273972603,"Jais 30B":0.0821917808,"GPT-4o":0.095890411,"ALLaM 7B":0.1506849315,"CR-7B-Arabic":0.2191780822,"Mistral-Saba":0.2054794521,"Claude-3.7-Sonnet":0.1506849315},{"Attack Type":"Hypothetical Testing","Fanar":0.1666666667,"Jais 30B":0.0833333333,"GPT-4o":0.0833333333,"ALLaM 7B":0.0555555556,"CR-7B-Arabic":0.125,"Mistral-Saba":0.125,"Claude-3.7-Sonnet":0.0555555556},{"Attack Type":"Out of Context","Fanar":0.0582010582,"Jais 30B":0.0211640212,"GPT-4o":0.0476190476,"ALLaM 7B":0.0476190476,"CR-7B-Arabic":0.0952380952,"Mistral-Saba":0.0793650794,"Claude-3.7-Sonnet":0.0423280423},{"Attack Type":"Persona Emulation","Fanar":0.1492537313,"Jais 30B":0.1492537313,"GPT-4o":0.1492537313,"ALLaM 7B":0.0746268657,"CR-7B-Arabic":0.1791044776,"Mistral-Saba":0.1641791045,"Claude-3.7-Sonnet":0.1343283582},{"Attack Type":"Step-by-Step Escalation","Fanar":0.0833333333,"Jais 30B":0.0833333333,"GPT-4o":0.0722222222,"ALLaM 7B":0.0888888889,"CR-7B-Arabic":0.0722222222,"Mistral-Saba":0.0722222222,"Claude-3.7-Sonnet":0.0833333333},{"Attack Type":"Storytelling","Fanar":0.1428571429,"Jais 30B":0.2285714286,"GPT-4o":0.0857142857,"ALLaM 7B":0.2,"CR-7B-Arabic":0.1857142857,"Mistral-Saba":0.1428571429,"Claude-3.7-Sonnet":0.1428571429}]

data/leaderboard_data/category_breakdown.json

@@ -0,0 +1,356 @@
+{
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+      "Mistral-Saba": {
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+        "safe": 0.39344262295081966,
+        "slightly unsafe": 0.08196721311475409
+      }
+    }
+  }
+}

data/leaderboard_data/overall.json

@@ -0,0 +1 @@
+[{"Safety Status":"safe","Fanar":402,"Jais 30B":396,"GPT-4o":424,"ALLaM 7B":464,"Claude-3.7-Sonnet":546,"CR-7B-Arabic":341,"Mistral-Saba":360},{"Safety Status":"extremely unsafe","Fanar":242,"Jais 30B":244,"GPT-4o":235,"ALLaM 7B":180,"Claude-3.7-Sonnet":131,"CR-7B-Arabic":246,"Mistral-Saba":237},{"Safety Status":"slightly unsafe","Fanar":74,"Jais 30B":74,"GPT-4o":70,"ALLaM 7B":73,"Claude-3.7-Sonnet":71,"CR-7B-Arabic":105,"Mistral-Saba":106},{"Safety Status":"moderately unsafe","Fanar":83,"Jais 30B":87,"GPT-4o":72,"ALLaM 7B":84,"Claude-3.7-Sonnet":53,"CR-7B-Arabic":109,"Mistral-Saba":98}]

data/leaderboard_data/overall_ratio_binary.json

@@ -0,0 +1 @@
+[{"Safety Status":"safe","Fanar":0.5018726592,"Jais 30B":0.4943820225,"GPT-4o":0.5293383271,"ALLaM 7B":0.5792759051,"Claude-3.7-Sonnet":0.6816479401,"CR-7B-Arabic":0.4257178527,"Mistral-Saba":0.4494382022},{"Safety Status":"unsafe","Fanar":0.4981273408,"Jais 30B":0.5056179775,"GPT-4o":0.4706616729,"ALLaM 7B":0.4207240949,"Claude-3.7-Sonnet":0.3183520599,"CR-7B-Arabic":0.5742821473,"Mistral-Saba":0.5505617978}]

data/leaderboard_data/overall_ratio_full.json

@@ -0,0 +1 @@
+[{"Safety Status":"safe","Fanar":0.5018726592,"Jais 30B":0.4943820225,"GPT-4o":0.5293383271,"ALLaM 7B":0.5792759051,"Claude-3.7-Sonnet":0.6816479401,"CR-7B-Arabic":0.4257178527,"Mistral-Saba":0.4494382022},{"Safety Status":"extremely unsafe","Fanar":0.3021223471,"Jais 30B":0.304619226,"GPT-4o":0.2933832709,"ALLaM 7B":0.2247191011,"Claude-3.7-Sonnet":0.163545568,"CR-7B-Arabic":0.3071161049,"Mistral-Saba":0.2958801498},{"Safety Status":"slightly unsafe","Fanar":0.0923845194,"Jais 30B":0.0923845194,"GPT-4o":0.0873907615,"ALLaM 7B":0.0911360799,"Claude-3.7-Sonnet":0.088639201,"CR-7B-Arabic":0.1310861423,"Mistral-Saba":0.1323345818},{"Safety Status":"moderately unsafe","Fanar":0.1036204744,"Jais 30B":0.1086142322,"GPT-4o":0.0898876404,"ALLaM 7B":0.1048689139,"Claude-3.7-Sonnet":0.0661672909,"CR-7B-Arabic":0.1360799001,"Mistral-Saba":0.1223470662}]

data/leaderboard_data/overall_ratio_unsafe_only.json

@@ -0,0 +1 @@
+[{"Safety Status":"extremely unsafe","Fanar":0.6065162907,"Jais 30B":0.6024691358,"GPT-4o":0.6233421751,"ALLaM 7B":0.5341246291,"Claude-3.7-Sonnet":0.5137254902,"CR-7B-Arabic":0.5347826087,"Mistral-Saba":0.537414966},{"Safety Status":"slightly unsafe","Fanar":0.1854636591,"Jais 30B":0.1827160494,"GPT-4o":0.1856763926,"ALLaM 7B":0.2166172107,"Claude-3.7-Sonnet":0.2784313725,"CR-7B-Arabic":0.2282608696,"Mistral-Saba":0.2403628118},{"Safety Status":"moderately unsafe","Fanar":0.2080200501,"Jais 30B":0.2148148148,"GPT-4o":0.1909814324,"ALLaM 7B":0.2492581602,"Claude-3.7-Sonnet":0.2078431373,"CR-7B-Arabic":0.2369565217,"Mistral-Saba":0.2222222222}]

data/leaderboard_data/subcategory.json

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data/leaderboard_data/subcategory_ratio_Bias.json

@@ -0,0 +1 @@
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data/leaderboard_data/subcategory_ratio_Controlled_Substances.json

@@ -0,0 +1 @@
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data/leaderboard_data/subcategory_ratio_Criminal_Planning.json

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data/leaderboard_data/subcategory_ratio_Cultural_Alignment.json

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data/leaderboard_data/subcategory_ratio_Guns_&_Illegal_Weapons.json

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data/leaderboard_data/subcategory_ratio_Sexual_Content.json

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data/leaderboard_data/subcategory_ratio_Suicide_&_Self_Harm.json

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data/leaderboard_data/subcategory_ratio_Violence_&_Hate.json

@@ -0,0 +1 @@
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requirements.txt

@@ -0,0 +1,128 @@
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+typing_extensions==4.12.2
+tzdata==2023.4
+urllib3==2.2.0
+uvicorn==0.34.2
+wcwidth @ file:///home/conda/feedstock_root/build_artifacts/wcwidth_1704731205417/work
+websockets==15.0.1
+zipp @ file:///home/conda/feedstock_root/build_artifacts/zipp_1695255097490/work