src/streamlit_app.py

import pandas as pd
import pygwalker as pyg
import streamlit as st
from pygwalker.api.streamlit import StreamlitRenderer
import random

st.set_page_config(layout="wide")

def no_op(*args, **kwargs):
    """This function replaces the original one and does not perform any action."""
    pass
st.user_info.maybe_show_deprecated_user_warning = no_op

st.header("Leaderboard")

benchmark = st.selectbox(
    "Select the type of benchmark you want",
    ("rabakbench", "toxicchat", "openaimod" ),
)

def get_statistics (df):
    tp = df[df['classification']=="True Positive"]["prompt"].nunique()
    fp = df[df['classification']=="False Positive"]["prompt"].nunique()
    fn = df[df['classification']=="False Negative"]["prompt"].nunique()

    avg_time = df['time'].mean()
    precision = tp / (tp + fp) if (tp + fp) > 0 else 0
    recall = tp / (tp + fn) if (tp + fn) > 0 else 0
    f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0

    return f1, precision, recall, avg_time

df_len = pd.read_csv(f'./src/llamaguard3_8b_{benchmark}.csv').shape[0]
if df_len > 500:
    random.seed(42)
    end = df_len - 1
    random_indices = [random.randint(0, end) for _ in range(500)]
else:
    random_indices = list(range(df_len))

## get the statistics for rabakbench for the Llamaguard 3 8b
df_ll = pd.read_csv(f'./src/llamaguard3_8b_{benchmark}.csv').iloc[random_indices]
ll_f1, ll_precision, ll_recall, ll_time = get_statistics (df_ll)

## get the statistics for rabakbench for the Mistral Moderation
df_mm = pd.read_csv(f'./src/mistral_{benchmark}.csv').iloc[random_indices]
mm_f1, mm_precision, mm_recall, mm_time = get_statistics (df_mm)

# ## get the statistics for rabakbench for the Qwen3Guard
df_qw = pd.read_csv(f'./src/qwen3guard_{benchmark}.csv').iloc[random_indices]
qw_f1, qw_precision, qw_recall, qw_time = get_statistics (df_qw)

## display statistics table 
df = pd.DataFrame(
    [
        {"model": "Mistral Moderation", "F1": round(mm_f1,2), "Precision": round(mm_precision,2), "Recall": round(mm_recall,2), "Avg Time/req (s)": round(mm_time,2)},
        {"model": "Qwen3Guard", "F1": round(qw_f1,2), "Precision": round(qw_precision,2), "Recall": round(qw_recall,2), "Avg Time/req (s)": round(qw_time,2)},
        {"model": "llamaguard 3 8b", "F1": round(ll_f1,2), "Precision": round(ll_precision,2), "Recall": round(ll_recall,2), "Avg Time/req (s)": round(ll_time,2)},
    ]
)

st.dataframe(df, hide_index= True, use_container_width=True)

st.header("Analysis")
guardrail = "Qwen3Guard"
df_dictionary = {
    "Qwen3Guard": df_qw,
    "Llama3_8b": df_ll,
    "Mistral": df_mm}

c1, c2 = st.columns([4, 3])

with c1:
    if df_len > 500:
        st.write(f"Analysis of 500 randomly selected samples from {benchmark} benchmark on")
    else:
        st.write(f"Analysis of full {benchmark} benchmark on")


with c2:
    guardrail = st.selectbox(
        "model-type",
        ["Qwen3Guard", "Llama3_8b", "Mistral"],
        label_visibility="collapsed"
    )
@st.cache_resource
def load_pygwalker(df_dictionary, guardrail):
    pygapp = StreamlitRenderer(df_dictionary[guardrail], spec='./src/pygwalker_spec_display.json', scrolling=True)

    return pygapp

pygapp = load_pygwalker(df_dictionary, guardrail)
pygapp.explorer()