Update app.py

app.py

@@ -13,7 +13,7 @@ st.markdown("### Search 11,500+ Cinematic AI-Tagged Comic Panels")
 # ==========================================
 # 2. DATA BUCKETING & CLEANING
 # ==========================================
-# This groups the messy AI tags into clean, professional cinematic categories
+# Camera, Mood, and Lighting Buckets
 def categorize_camera(text):
     text = str(text).lower()
     if 'dutch' in text: return 'Dutch Angle'
@@ -44,16 +44,41 @@ def categorize_lighting(text):
     elif 'flat' in text or 'even' in text: return 'Flat Lighting'
     else: return 'Standard Lighting'
 
+# NEW: Location, Subjects, and Action Buckets
+def categorize_location(row):
+    text = str(row.get('location_setup', '')).lower() + " " + str(row.get('description', '')).lower()
+    if any(w in text for w in ['indoor', 'interior', 'room', 'house', 'building', 'office', 'corridor', 'hallway', 'wall', 'window', 'door', 'basement', 'stairs']): return 'Indoor'
+    if any(w in text for w in ['outdoor', 'exterior', 'street', 'sky', 'forest', 'mountain', 'landscape', 'city', 'outside', 'woods', 'road', 'night', 'moon', 'ocean']): return 'Outdoor'
+    return 'Unspecified / Mixed'
+
+def categorize_subject(row):
+    text = str(row.get('staging', '')).lower() + " " + str(row.get('description', '')).lower()
+    if any(w in text for w in ['group', 'crowd', 'three', 'four', 'multiple', 'several', 'guests', 'army', 'mob', 'people']): return 'Group (3+ People)'
+    if any(w in text for w in ['two', 'couple', 'duo', 'both', 'pair']): return 'Two Characters'
+    if any(w in text for w in ['man', 'woman', 'boy', 'girl', 'figure', 'character', 'person', 'creature', 'monster']): return 'Single Subject'
+    return 'Object / Environment'
+
+def categorize_action(row):
+    text = str(row.get('staging', '')).lower() + " " + str(row.get('description', '')).lower()
+    if any(w in text for w in ['action', 'fight', 'strike', 'combat', 'running', 'chasing', 'attack', 'lunging', 'falling', 'fleeing', 'struggle', 'violence', 'grab']): return 'Action Sequence'
+    if any(w in text for w in ['dialogue', 'talking', 'discussing', 'speaking', 'speech', 'conversation', 'yelling', 'screaming', 'whispering', 'saying']): return 'Dialogue / Conversation'
+    if any(w in text for w in ['reacts', 'reaction', 'looking', 'staring', 'observing', 'gazing', 'watching', 'shock', 'listening']): return 'Reaction / Observation'
+    return 'Static / Establishing'
+
 @st.cache_data
 def load_data():
     df = pd.read_csv("horror_shot_database.csv")
     
-    # Create new "Clean" columns for the UI
+    # Apply standard categories
     df['broad_camera'] = df['camera_angle'].apply(categorize_camera)
     df['broad_mood'] = df['mood'].apply(categorize_mood)
-    # We search both mood and description to figure out the lighting
     df['broad_lighting'] = (df['mood'].fillna('') + " " + df['description'].fillna('')).apply(categorize_lighting)
     
+    # Apply new Storyboard categories
+    df['location_type'] = df.apply(categorize_location, axis=1)
+    df['subject_type'] = df.apply(categorize_subject, axis=1)
+    df['action_type'] = df.apply(categorize_action, axis=1)
+    
     return df
 
 try:
@@ -67,37 +92,51 @@ except Exception as e:
 # ==========================================
 st.sidebar.header("🔍 Search Library")
 
-# The Global Text Search Bar
-search_query = st.sidebar.text_input("Keyword Search", placeholder="e.g., monster, running, eyes...")
+search_query = st.sidebar.text_input("Keyword Search", placeholder="e.g., monster, shadow, weapon...")
 st.sidebar.write("---")
 
 st.sidebar.header("📂 Filter Categories")
 
-# Expandable Category: Camera
-with st.sidebar.expander("🎥 Camera & Framing", expanded=True):
+# Expander 1: Location & Subjects
+with st.sidebar.expander("🌍 Location & Subjects", expanded=True):
+    all_locations = ["Any"] + sorted(df['location_type'].unique().tolist())
+    selected_location = st.selectbox("Setting", all_locations)
+    
+    all_subjects = ["Any"] + sorted(df['subject_type'].unique().tolist())
+    selected_subject = st.selectbox("Characters in Frame", all_subjects)
+
+# Expander 2: Scene & Action
+with st.sidebar.expander("🎬 Action & Scene Type", expanded=True):
+    all_actions = ["Any"] + sorted(df['action_type'].unique().tolist())
+    selected_action = st.selectbox("Scene Action", all_actions)
+
+# Expander 3: Camera
+with st.sidebar.expander("🎥 Camera & Framing"):
     all_angles = ["Any"] + sorted(df['broad_camera'].unique().tolist())
     selected_angle = st.selectbox("Shot Type", all_angles)
 
-# Expandable Category: Lighting
-with st.sidebar.expander("💡 Lighting Style"):
+# Expander 4: Atmosphere
+with st.sidebar.expander("🎭 Atmosphere"):
     all_lighting = ["Any"] + sorted(df['broad_lighting'].unique().tolist())
-    selected_lighting = st.selectbox("Lighting Category", all_lighting)
-
-# Expandable Category: Mood
-with st.sidebar.expander("🎭 Scene Mood"):
+    selected_lighting = st.selectbox("Lighting Style", all_lighting)
+    
     all_moods = ["Any"] + sorted(df['broad_mood'].unique().tolist())
-    selected_mood = st.selectbox("Atmosphere", all_moods)
+    selected_mood = st.selectbox("Mood", all_moods)
 
 # ==========================================
 # 4. FILTERING LOGIC
 # ==========================================
 results = df.copy()
 
-# Apply the text search
 if search_query:
     results = results[results['description'].str.contains(search_query, case=False, na=False)]
 
-# Apply the clean dropdown filters
+if selected_location != "Any":
+    results = results[results['location_type'] == selected_location]
+if selected_subject != "Any":
+    results = results[results['subject_type'] == selected_subject]
+if selected_action != "Any":
+    results = results[results['action_type'] == selected_action]
 if selected_angle != "Any":
     results = results[results['broad_camera'] == selected_angle]
 if selected_lighting != "Any":