Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import os
+
+# ==========================================
+# 1. PAGE CONFIGURATION
+# ==========================================
+st.set_page_config(page_title="Horror Reference Library", layout="wide")
+
+st.title("📽️ Horror Reference Library")
+st.markdown("### Search 11,500+ Cinematic AI-Tagged Comic Panels")
+
+# ==========================================
+# 2. DATA LOADING
+# ==========================================
+@st.cache_data
+def load_data():
+    # This loads the CSV "Map" you uploaded to the Space
+    df = pd.read_csv("horror_shot_database.csv")
+    return df
+
+try:
+    df = load_data()
+except Exception as e:
+    st.error(f"Error loading database: {e}")
+    st.stop()
+
+# ==========================================
+# 3. SIDEBAR FILTERS
+# ==========================================
+st.sidebar.header("Filter Shots")
+
+all_angles = ["Any"] + sorted(df['camera_angle'].dropna().unique().tolist())
+selected_angle = st.sidebar.selectbox("Camera Angle", all_angles)
+
+all_moods = ["Any"] + sorted(df['mood'].dropna().unique().tolist())
+selected_mood = st.sidebar.selectbox("Mood/Lighting", all_moods)
+
+all_emotions = ["Any"] + sorted(df['emotion'].dropna().unique().tolist())
+selected_emotion = st.sidebar.selectbox("Character Emotion", all_emotions)
+
+# ==========================================
+# 4. FILTERING LOGIC (Connecting to Dataset)
+# ==========================================
+results = df
+if selected_angle != "Any":
+    results = results[results['camera_angle'] == selected_angle]
+if selected_mood != "Any":
+    results = results[results['mood'] == selected_mood]
+if selected_emotion != "Any":
+    results = results[results['emotion'] == selected_emotion]
+
+# --- CHANGE 'YourUsername' TO YOUR ACTUAL HF USERNAME ---
+base_url = "https://huggingface.co/datasets/Roshanurs/Horror-Reference-Data/resolve/main/Upload_Temp/Panels_Out"
+
+valid_images = []
+for idx, row in results.iterrows():
+    # This constructs the direct link to the image in your 24 batch folders
+    img_url = f"{base_url}/{row['filename']}"
+    valid_images.append({
+        "url": img_url,
+        "filename": row['filename'],
+        "desc": row['description']
+    })
+
+st.markdown(f"**Found {len(valid_images)} matching shots**")
+st.write("---") 
+
+# ==========================================
+# 5. THE MASONRY GALLERY 
+# ==========================================
+if len(valid_images) > 0:
+    # We show the first 100 results to keep the page fast
+    display_limit = 100
+    display_list = valid_images[:display_limit]
+    
+    cols = st.columns(4)
+    
+    for index, img_data in enumerate(display_list):
+        with cols[index % 4]:
+            # Streamlit fetches the image directly from the Dataset URL
+            st.image(img_data["url"], use_container_width=True)
+            
+            # Download button that opens the raw image in a new tab
+            st.markdown(
+                f'<a href="{img_data["url"]}" target="_blank">'
+                f'<button style="width:100%; padding:8px; border-radius:4px; border:1px solid #444; background:#222; color:white; cursor:pointer;">'
+                f'View Full Size</button></a>', 
+                unsafe_allow_html=True
+            )
+            st.write("") 
+            
+    if len(valid_images) > display_limit:
+        st.info(f"Showing first {display_limit} results. Refine your filters to see more specific shots.")
+else:
+    st.warning("No shots found matching those exact parameters. Try widening your search!")