Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,92 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 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 = "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
+            img_path = row.get("web_image_path", "")
+            if os.path.exists(img_path):
+                img = Image.open(img_path)
+                st.image(img, use_container_width=True, caption=f"Token: {row['token'][:8]}...")
+            else:
+                st.warning("Image not available in demo pack")
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+        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()