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import streamlit as st
import pandas as pd
import json
import os
from PIL import Image
# --- CONFIG ---
st.set_page_config(layout="wide", page_title="Semantic-Drive Explorer", page_icon="🚗")
DATA_FILE = "hf_demo_pack/demo_data.jsonl"
# --- SIDEBAR ---
st.sidebar.title("🚗 Semantic-Drive")
st.sidebar.markdown("**Mining Long-Tail Edge Cases with Neuro-Symbolic VLMs**")
st.sidebar.info("This demo showcases scenarios retrieved from NuScenes using a local Llama-3/Qwen pipeline running on consumer hardware.")
# --- LOAD DATA ---
@st.cache_data
def load_data():
if not os.path.exists(DATA_FILE):
return pd.DataFrame()
data = []
with open(DATA_FILE, 'r') as f:
for line in f:
data.append(json.loads(line))
return pd.DataFrame(data)
df = load_data()
if df.empty:
st.error("No data found. Please upload demo_data.jsonl")
st.stop()
# --- FILTERS ---
st.sidebar.header("🔍 Search Filters")
# 1. Filter by Tags
all_tags = set()
for tags in df['wod_e2e_tags']:
all_tags.update(tags)
selected_tags = st.sidebar.multiselect("WOD-E2E Tags", sorted(list(all_tags)))
# 2. Filter by Risk
min_risk = st.sidebar.slider("Minimum Risk Score", 0, 10, 0)
# Apply Filters
filtered_df = df.copy()
if selected_tags:
filtered_df = filtered_df[filtered_df['wod_e2e_tags'].apply(lambda x: any(t in x for t in selected_tags))]
filtered_df = filtered_df[filtered_df.apply(lambda x: x['scenario_criticality']['risk_score'] >= min_risk, axis=1)]
st.sidebar.markdown(f"**Found:** {len(filtered_df)} / {len(df)} scenarios")
# --- MAIN FEED ---
st.title("Scenario Database")
# Display as a feed of cards
for idx, row in filtered_df.iterrows():
with st.container():
c1, c2 = st.columns([1, 2])
with c1:
# Load Image
raw_path = row.get("web_image_path", "")
# Fix path if it doesn't start with the folder name
if not raw_path.startswith("hf_demo_pack"):
img_path = os.path.join("hf_demo_pack", raw_path)
else:
img_path = raw_path
if os.path.exists(img_path):
img = Image.open(img_path)
st.image(img, caption=f"Token: {row['token'][:8]}...")
else:
st.warning("Image not available in demo pack")
with c2:
# Header
tags = row['wod_e2e_tags']
risk = row['scenario_criticality']['risk_score']
# Badge Logic
risk_color = "red" if risk >= 7 else "orange" if risk >= 4 else "green"
st.markdown(f"### :{risk_color}[Risk {risk}/10] {' '.join([f'`{t}`' for t in tags])}")
# Description
st.info(row['description'])
# Details Expander
with st.expander("🧬 View Scenario DNA (JSON)"):
st.json({
"ODD": row['odd_attributes'],
"Topology": row['road_topology'],
"Agents": row['key_interacting_agents'],
"Reasoning": row.get('judge_log', [])
})
st.divider() |