src/streamlit_app.py

import json
import altair as alt
import numpy as np
import pandas as pd
import pydeck as pdk
import streamlit as st
from collections import Counter
from pathlib import Path
# ──────────────────────────────────────────────────────────────────────────────
# Page configuration
# ──────────────────────────────────────────────────────────────────────────────
st.set_page_config(
    page_title="PLACES Dataset Explorer",
    page_icon="🌍",
    layout="wide",
)
# ──────────────────────────────────────────────────────────────────────────────
# Geocoded coordinates for each participant locale
# ──────────────────────────────────────────────────────────────────────────────
LOCALE_GEO = {
    "karnataka": {
        "lat": 12.9716,
        "lon": 77.5946,
        "label": "Karnataka, India",
        "city": "Bangalore",
        "color": [255, 87, 51],       # coral-red
    },
    "punjab": {
        "lat": 31.1471,
        "lon": 75.3412,
        "label": "Punjab, India",
        "city": "Amritsar",
        "color": [255, 195, 0],       # amber
    },
    "nigeria": {
        "lat": 12.0022,
        "lon": 8.5920,
        "label": "Kano, Nigeria",
        "city": "Kano",
        "color": [0, 168, 107],       # green
    },
    "ghana": {
        "lat": 5.1315,
        "lon": -1.2795,
        "label": "Cape Coast, Ghana",
        "city": "Cape Coast",
        "color": [30, 144, 255],      # dodger blue
    },
    "zindi": {
        "lat": -33.9249,
        "lon": 18.4241,
        "label": "Zindi (Online Platform)",
        "city": "Cape Town",
        "color": [155, 89, 182],      # purple
    },
}
# ──────────────────────────────────────────────────────────────────────────────
# Load data (cached so it's only read once)
# ──────────────────────────────────────────────────────────────────────────────
# Define the filepath relative to this script file, not cwd.
# __file__ = src/streamlit_app.py  →  .parent = src/  →  .parent.parent = repo root
filepath = Path(__file__).parent.parent / "PLACES_full_dataset.json"
@st.cache_data
def load_data(filepath=filepath):
    # Optional: Add a debug check that prints to your HF Space logs if it fails
    if not filepath.exists():
        print(f"DEBUG: Current directory is {Path.cwd()}")
        print(f"DEBUG: Files here: {list(Path.cwd().iterdir())}")
        
    with open(filepath, "r") as f:
        raw = json.load(f)
    return pd.DataFrame(raw)
df = load_data()
# ──────────────────────────────────────────────────────────────────────────────
# Helper: explode a list-valued column and count occurrences
# ──────────────────────────────────────────────────────────────────────────────
def count_list_column(series):
    counter = Counter()
    for val_list in series.dropna():
        if isinstance(val_list, list):
            for v in val_list:
                counter[v] += 1
        else:
            counter[val_list] += 1
    return pd.DataFrame(
        {"Category": list(counter.keys()), "Count": list(counter.values())}
    ).sort_values("Count", ascending=False)
# ══════════════════════════════════════════════════════════════════════════════
#  SIDEBAR — Place selector & prompt viewer
# ══════════════════════════════════════════════════════════════════════════════
with st.sidebar:
    st.markdown("## 📍 Select a Place")
    st.caption("Click a place below to see sample prompts from that region.")
    locale_options = {
        geo["label"]: key for key, geo in LOCALE_GEO.items()
    }
    selected_label = st.radio(
        "Choose a location",
        options=list(locale_options.keys()),
        index=0,
        label_visibility="collapsed",
    )
    selected_locale = locale_options[selected_label]
    place_df = df[df["participant_locale"] == selected_locale]
    st.markdown("---")
    st.markdown(
        f"### {selected_label}\n"
        f"**{len(place_df):,}** records · "
        f"**{place_df['prompt'].nunique():,}** unique prompts"
    )
    # Harm-type mini-breakdown for this place
    place_harm = count_list_column(place_df["harm_types"])
    st.markdown("#### Harm types at this place")
    for _, row in place_harm.iterrows():
        st.markdown(f"- **{row['Category']}**: {row['Count']:,}")
    st.markdown("---")
    n_sidebar = st.slider("Sample size", 1, 20, 5, key="sidebar_n")
    if st.button("🔄 Resample prompts", key="sidebar_resample"):
        pass  # forces re-run → new random draw
    st.markdown("#### 🎲 Sample Prompts")
    sample_place = place_df.sample(n=min(n_sidebar, len(place_df)))
    for i, (_, row) in enumerate(sample_place.iterrows()):
        with st.container():
            st.markdown(
                f"**Prompt:** {row['prompt']}\n\n"
                f"**Model revised to:** {row['revised_prompt']}\n\n"
                f"🛑 {', '.join(row['harm_types']) if isinstance(row['harm_types'], list) else row['harm_types']}  \n"
                f"⚔️ {', '.join(row['attack_modes']) if isinstance(row['attack_modes'], list) else row['attack_modes']}  \n"
                f"🎯 {', '.join(row['targeted_identity_attribute']) if isinstance(row['targeted_identity_attribute'], list) else row['targeted_identity_attribute']}"
            )
            if i < n_sidebar - 1:
                st.markdown("---")
# ══════════════════════════════════════════════════════════════════════════════
#  TITLE
# ══════════════════════════════════════════════════════════════════════════════
st.markdown(
    """
    <h1 style='text-align:center;'>🌍 PLACES Dataset Explorer</h1>
    <p style='text-align:center; font-size:1.1rem; color:gray;'>
        Explore the <b>PLACES</b> adversarial-nibbler dataset — covering prompts,
        harm annotations, attack modes, and targeted-identity attributes from
        participants across Sub-Saharan Africa &amp; India.
    </p>
    """,
    unsafe_allow_html=True,
)
st.divider()
# ══════════════════════════════════════════════════════════════════════════════
#  TOP-LINE METRICS  (big bold numbers)
# ══════════════════════════════════════════════════════════════════════════════
total_records = len(df)
unique_prompts = df["prompt"].nunique()
unique_places = df["participant_locale"].nunique()
unique_participants = df["participant_id"].nunique()
col1, col2, col3, col4 = st.columns(4)
col1.metric("📝 Total Records", f"{total_records:,}")
col2.metric("💬 Unique Prompts", f"{unique_prompts:,}")
col3.metric("📍 Unique Places", f"{unique_places:,}")
col4.metric("👥 Unique Participants", f"{unique_participants:,}")
st.divider()
# ══════════════════════════════════════════════════════════════════════════════
#  INTERACTIVE MAP
# ══════════════════════════════════════════════════════════════════════════════
st.markdown("## 🗺️ Where the Data Comes From")
st.markdown(
    "Each pin represents a participant locale. **Select a place in the sidebar** "
    "to see sample prompts from that region."
)
# Build map dataframe
locale_counts = df["participant_locale"].value_counts().to_dict()
# Pre-sample 2-3 prompts per locale for hover tooltip
locale_sample_cache = {}
for key in LOCALE_GEO:
    locale_rows = df[df["participant_locale"] == key]["prompt"].dropna()
    if len(locale_rows) > 0:
        samples = locale_rows.sample(n=min(3, len(locale_rows))).tolist()
        locale_sample_cache[key] = "<br/>".join(
            f"&#8226; {p[:100]}{'\u2026' if len(p) > 100 else ''}" for p in samples
        )
    else:
        locale_sample_cache[key] = "(no prompts)"
map_data = []
for key, geo in LOCALE_GEO.items():
    count = locale_counts.get(key, 0)
    map_data.append(
        {
            "locale": key,
            "label": geo["label"],
            "city": geo["city"],
            "lat": geo["lat"],
            "lon": geo["lon"],
            "records": count,
            "color": geo["color"],
            # scale radius by record count (min 40000, max 120000 for visibility)
            "radius": max(40000, min(120000, count * 8)),
            "sample_prompts": locale_sample_cache.get(key, ""),
        }
    )
map_df = pd.DataFrame(map_data)
# Highlight the selected place
map_df["is_selected"] = map_df["locale"] == selected_locale
map_df["elevation"] = map_df["is_selected"].apply(lambda s: 150000 if s else 30000)
map_df["opacity"] = map_df["is_selected"].apply(lambda s: 220 if s else 140)
# pydeck layers
scatter_layer = pdk.Layer(
    "ScatterplotLayer",
    data=map_df,
    get_position=["lon", "lat"],
    get_radius="radius",
    get_fill_color="color",
    get_line_color=[255, 255, 255],
    line_width_min_pixels=2,
    pickable=True,
    opacity=0.8,
    stroked=True,
)
column_layer = pdk.Layer(
    "ColumnLayer",
    data=map_df,
    get_position=["lon", "lat"],
    get_elevation="elevation",
    elevation_scale=1,
    radius=35000,
    get_fill_color="color",
    pickable=True,
    auto_highlight=True,
    opacity=0.6,
)
text_layer = pdk.Layer(
    "TextLayer",
    data=map_df,
    get_position=["lon", "lat"],
    get_text="label",
    get_size=14,
    get_color=[30, 30, 30],        # dark text for lighter basemap
    get_angle=0,
    get_text_anchor='"middle"',
    get_alignment_baseline='"bottom"',
    font_family='"Arial"',
    font_weight=700,
    billboard=True,
)
# View state centred roughly between Africa and India
view_state = pdk.ViewState(
    latitude=10,
    longitude=30,
    zoom=2.0,
    pitch=15,
    bearing=0,
)
deck = pdk.Deck(
    layers=[scatter_layer, column_layer, text_layer],
    initial_view_state=view_state,
    tooltip={
        "html": (
            "<b style='font-size:15px'>{label}</b><br/>"
            "<span style='color:#888'>📍 {city} &nbsp;·&nbsp; 📝 {records} records</span>"
            "<hr style='margin:6px 0; border-color:#ddd'/>"
            "<i style='font-size:12px'>Sample prompts:</i><br/>"
            "<span style='font-size:12px; line-height:1.6'>{sample_prompts}</span>"
        ),
        "style": {
            "backgroundColor": "white",
            "color": "#222",
            "fontSize": "13px",
            "padding": "10px 14px",
            "borderRadius": "8px",
            "maxWidth": "320px",
            "boxShadow": "0 2px 8px rgba(0,0,0,0.15)",
        },
    },
    # Token-free Carto Voyager — light, colourful, no API key needed
    map_style="https://basemaps.cartocdn.com/gl/voyager-gl-style/style.json",
)
st.pydeck_chart(deck, use_container_width=True, height=380)
# Small legend below the map
legend_cols = st.columns(len(LOCALE_GEO))
for col, (key, geo) in zip(legend_cols, LOCALE_GEO.items()):
    r, g, b = geo["color"]
    count = locale_counts.get(key, 0)
    col.markdown(
        f"<span style='color:rgb({r},{g},{b}); font-size:1.5rem;'>●</span> "
        f"**{geo['label']}** — {count:,}",
        unsafe_allow_html=True,
    )
st.divider()
# ══════════════════════════════════════════════════════════════════════════════
#  INTERACTIVE DATA EXPLORER
# ══════════════════════════════════════════════════════════════════════════════
st.markdown("## 🔎 Explore the Data")
with st.expander("Filters", expanded=True):
    fcol1, fcol2 = st.columns(2)
    with fcol1:
        selected_locales = st.multiselect(
            "Filter by Place",
            options=sorted(df["participant_locale"].unique()),
            default=sorted(df["participant_locale"].unique()),
        )
    with fcol2:
        # Build a flat list of all harm types present in the dataset
        all_harms = sorted(
            {h for hlist in df["harm_types"].dropna() for h in hlist}
        )
        selected_harms = st.multiselect(
            "Filter by Harm Type",
            options=all_harms,
            default=all_harms,
        )
    search_query = st.text_input(
        "🔍 Search prompts (case-insensitive substring match)", ""
    )
# Apply filters
mask = df["participant_locale"].isin(selected_locales)
# Harm-type filter (row passes if it contains ANY selected harm)
mask &= df["harm_types"].apply(
    lambda hlist: bool(set(hlist) & set(selected_harms))
    if isinstance(hlist, list)
    else False
)
if search_query:
    mask &= df["prompt"].str.contains(search_query, case=False, na=False)
filtered = df[mask]
st.markdown(f"**Showing {len(filtered):,} of {len(df):,} records**")
st.dataframe(
    filtered[
        [
            "participant_locale",
            "prompt",
            "revised_prompt",
            "harm_types",
            "attack_modes",
            "targeted_identity_attribute",
        ]
    ].reset_index(drop=True),
    use_container_width=True,
    height=500,
)
st.divider()
# ══════════════════════════════════════════════════════════════════════════════
#  SAMPLE PROMPTS
# ══════════════════════════════════════════════════════════════════════════════
# st.markdown("## 🎲 Random Sample of Prompts")
# n_samples = st.slider("Number of samples", 1, 50, 10)
# if st.button("🔄 Resample"):
#     pass  # forces re-run
# sample = filtered.sample(n=min(n_samples, len(filtered)))
# for _, row in sample.iterrows():
#     with st.container():
#         st.markdown(
#             f"**Locale:** `{row['participant_locale']}`  \n"
#             f"**Prompt:** {row['prompt']}  \n"
#             f"**Revised Prompt:** {row['revised_prompt']}  \n"
#             f"**Harm Types:** {', '.join(row['harm_types']) if isinstance(row['harm_types'], list) else row['harm_types']}  \n"
#             f"**Attack Modes:** {', '.join(row['attack_modes']) if isinstance(row['attack_modes'], list) else row['attack_modes']}  \n"
#             f"**Targeted Identity:** {', '.join(row['targeted_identity_attribute']) if isinstance(row['targeted_identity_attribute'], list) else row['targeted_identity_attribute']}"
#         )
#         st.divider()