# import streamlit as st
# import networkx as nx
# import pickle
# import os
# import json
# import random
# import datetime
# import math
# import pandas as pd
# import streamlit.components.v1 as components

# # --- CONFIGURATION ---
# CACHE_FILE = "mastodon_network.pkl"
# LABELS_FILE = "topic_labels.json"
# MAX_PRE_FETCH = 50  # Python sends max 50 neighbors per node to browser
# TIME_SCALE = 1 / 3600 # Logarithmic scale factor (Hours)

# # Define Blocklist
# NSFW_KEYWORDS = {
#     "nsfw", "porn", "nude", "naked", "sex", "18+", "xxx", 
#     "cum", "dick", "pussy", "hentai", "erotic", "onlyfans",
#     "boobs", "tits", "cock", "slut", "whore", "bitch", "incest",
#     "lewd", "horny", "sensual", "fetish", "bondage"
# }

# # --- PAGE CONFIG ---
# st.set_page_config(layout="wide", page_title="Mastodon Graph Explorer")

# # --- 1. LOAD DATA (Cached) ---
# @st.cache_resource
# def load_data():
#     data = {"G": None, "labels": {}, "min_ts": 0, "max_ts": 1}
    
#     if os.path.exists(CACHE_FILE):
#         with open(CACHE_FILE, 'rb') as f:
#             data["G"] = pickle.load(f)
            
#         # Pre-calculate Time Range
#         G = data["G"]
#         timestamps = [
#             d['last_timestamp'] 
#             for u, v, d in G.edges(data=True) 
#             if 'last_timestamp' in d and d['last_timestamp'] > 0
#         ]
#         if timestamps:
#             data["min_ts"] = min(timestamps)
#             data["max_ts"] = max(timestamps)
            
#     if os.path.exists(LABELS_FILE):
#         with open(LABELS_FILE, 'r') as f:
#             data["labels"] = json.load(f)
            
#     return data

# data = load_data()
# G = data["G"]
# labels_map = data["labels"]
# MIN_TS = data["min_ts"]
# MAX_TS = data["max_ts"]

# if G is None:
#     st.error(f"Cache file '{CACHE_FILE}' not found! Please run 'build_graph_cache.py' locally first.")
#     st.stop()

# # --- HELPER FUNCTIONS ---
# def get_time_color(ts):
#     """Returns a hex color code from Cyan (Old) to Red (New) using Logarithmic Age."""
#     if ts <= 0: return "#888888"
    
#     age = (MAX_TS - ts) * TIME_SCALE
#     max_age = (MAX_TS - MIN_TS) * TIME_SCALE
    
#     if max_age == 0: return "#FF0000"
    
#     # Logarithmic decay
#     decay = math.log(age + 1) / math.log(max_age + 1)
#     ratio = max(0.0, min(1.0, 1.0 - decay))
    
#     # Cyan (#00FFFF) to Red (#FF0000)
#     r = int(ratio * 255)
#     g = int((1 - ratio) * 255)
#     b = int((1 - ratio) * 255)
#     return f"#{r:02x}{g:02x}{b:02x}"

# def is_node_allowed(node_id):
#     if node_id == "Topic_0": return False
#     label = labels_map.get(node_id, G.nodes[node_id].get('label', node_id))
#     if label == "[NSFW]": return False
#     if isinstance(label, str):
#         label_words = set(label.lower().replace("_", " ").split())
#         if not NSFW_KEYWORDS.isdisjoint(label_words):
#             return False
#     return True

# def get_node_label(node_id):
#     """Returns the friendly label for a node if available, else the ID."""
#     if node_id.startswith("Topic_") and node_id in labels_map:
#         return labels_map[node_id]
#     return G.nodes[node_id].get('label', node_id)

# def get_related_entities(graph, node_id, top_n=10):
#     """
#     Finds nodes that share the most connections with the target node.
#     For a User, finds Users with shared Topics.
#     For a Topic, finds Topics with shared Users.
#     """
#     if node_id not in graph:
#         return []
    
#     # Get direct neighbors (e.g., Topics for a User)
#     direct_neighbors = list(graph.neighbors(node_id))
    
#     related_scores = {}
    
#     for neighbor in direct_neighbors:
#         if not is_node_allowed(neighbor): continue

#         # Get neighbors of neighbor (e.g., Users for a Topic)
#         second_degree_neighbors = list(graph.neighbors(neighbor))
        
#         for second_node in second_degree_neighbors:
#             if second_node == node_id: continue
#             if not is_node_allowed(second_node): continue
                
#             # Score = Number of shared connections (Simple Common Neighbors)
#             related_scores[second_node] = related_scores.get(second_node, 0) + 1
            
#     # Sort by score descending
#     sorted_related = sorted(related_scores.items(), key=lambda x: x[1], reverse=True)
#     return sorted_related[:top_n]

# # --- SIDEBAR CONTROLS ---
# st.sidebar.title("🔍 Graph Controls")

# # Prepare Topic List for Dropdown
# topic_nodes = [n for n in G.nodes if n.startswith("Topic_")]
# topic_options = {}

# for t in topic_nodes:
#     # Get fancy label if available, otherwise ID
#     label = labels_map.get(t, G.nodes[t].get('label', t))
    
#     # Skip NSFW/Filtered topics in the dropdown
#     if not is_node_allowed(t):
#         continue
        
#     # Format: "Nature Photography (Topic_5)"
#     display_name = f"{label} ({t})"
#     topic_options[display_name] = t

# # Prepare User List for Dropdown
# user_nodes = [n for n in G.nodes if n.startswith("User_")]
# user_options = {}

# for u in user_nodes:
#     # Label is usually "User {id}"
#     label = G.nodes[u].get('label', u)
#     user_options[label] = u

# # Search Mode
# search_mode = st.sidebar.radio("Search Mode", ["Select Topic", "Select User", "Random Topic"])

# target_node = None

# if search_mode == "Select User":
#     # Sort options for easier finding
#     sorted_users = sorted(user_options.keys())
#     selected_user = st.sidebar.selectbox("Search User", sorted_users)
    
#     if selected_user:
#         target_node = user_options[selected_user]

# elif search_mode == "Select Topic":
#     # Sort options alphabetically
#     sorted_options = sorted(topic_options.keys())
#     selected_option = st.sidebar.selectbox("Choose a Topic", sorted_options)
    
#     if selected_option:
#         target_node = topic_options[selected_option]

# elif search_mode == "Random Topic":
#     if st.sidebar.button("Pick Random Topic"):
#         # Pick from the filtered list we created above
#         if topic_options:
#             random_display = random.choice(list(topic_options.keys()))
#             target_node = topic_options[random_display]
#             st.session_state['selected_node'] = target_node

# if 'selected_node' in st.session_state and not target_node:
#     target_node = st.session_state['selected_node']

# # Sliders
# st.sidebar.markdown("---")
# max_neighbors = st.sidebar.slider("Top Neighbors per Node", 1, 50, 5, help="How many strong connections to show per node.")
# depth = st.sidebar.slider("Graph Depth", 1, 2, 2, help="1 = Direct connections only. 2 = Friends of friends.")

# # --- MAIN GRAPH GENERATION ---
# def generate_html(center_node, max_neighbors, depth):
#     # 1. Python BFS (Pre-Filtering)
#     nodes_to_include = {center_node}
#     edges_to_include = []
    
#     l1_neighbors = list(G.neighbors(center_node))
#     l1_neighbors.sort(key=lambda x: G[center_node][x].get('weight', 1), reverse=True)
#     l1_neighbors = l1_neighbors[:MAX_PRE_FETCH]

#     for n1 in l1_neighbors:
#         if not is_node_allowed(n1): continue
#         nodes_to_include.add(n1)
#         edges_to_include.append((center_node, n1))
        
#         l2_neighbors = list(G.neighbors(n1))
#         l2_neighbors.sort(key=lambda x: G[n1][x].get('weight', 1), reverse=True)
#         l2_neighbors = l2_neighbors[:MAX_PRE_FETCH]
        
#         for n2 in l2_neighbors:
#             if not is_node_allowed(n2): continue
#             if n2 != center_node:
#                 nodes_to_include.add(n2)
#                 edges_to_include.append((n1, n2))

#     # 2. Prepare JSON Data
#     js_nodes = []
#     for nid in nodes_to_include:
#         attr = G.nodes[nid]
#         group = attr.get('group', 'unknown')
#         base_size = attr.get('size', 10)
        
#         # Apply Labels
#         if group == "topics" and nid in labels_map:
#             label = labels_map[nid]
#             if len(label) > 20: label = label[:20] + "..."
#             title = f"{nid}: {labels_map[nid]}"
#         else:
#             label = attr.get('label', nid)
#             title = label

#         node_data = {
#             "id": nid,
#             "label": label,
#             "group": group,
#             "value": base_size, # Default VisJS scaling
#             "title": title,
#             "baseSize": base_size # Used for custom slider scaling
#         }
        
#         if nid == center_node:
#             node_data["color"] = "#00FF00" 
#             node_data["size"] = 40
#             node_data["baseSize"] = 40
#         elif group == "users":
#             node_data["color"] = "#97C2FC"
#         else:
#             node_data["color"] = "#FB7E81"
            
#         js_nodes.append(node_data)

#     js_edges = []
#     for u, v in edges_to_include:
#         data = G[u][v]
#         weight = data.get('weight', 1)
#         ts = data.get('last_timestamp', 0)
        
#         edge_color = get_time_color(ts)
#         date_str = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d') if ts > 0 else "Unknown"
#         tooltip = f"Posts: {weight}\nLast Active: {date_str}"

#         js_edges.append({
#             "from": u,
#             "to": v,
#             "value": weight,
#             "title": tooltip,
#             "color": edge_color
#         })

#     # 3. Generate HTML with Embedded Logic
#     html_string = f"""
#     <!DOCTYPE html>
#     <html>
#     <head>
#         <script type="text/javascript" src="https://unpkg.com/vis-network/standalone/umd/vis-network.min.js"></script>
#         <style type="text/css">
#             body {{ font-family: sans-serif; background-color: #222; color: white; margin: 0; }}
#             #mynetwork {{ width: 100%; height: 700px; border: 1px solid #444; }}
#             #controls {{ padding: 15px; background-color: #333; display: flex; align-items: center; gap: 20px; flex-wrap: wrap; }}
#             .control-group {{ display: flex; align-items: center; gap: 10px; }}
#             input[type=range] {{ width: 150px; cursor: pointer; }}
#             .label {{ font-weight: bold; font-size: 14px; color: #ddd; }}
#             .val {{ font-family: monospace; color: #00FF00; font-weight: bold; width: 30px; }}
#             .legend {{ margin-left: auto; font-size: 12px; color: #aaa; display: flex; gap: 10px; align-items: center; }}
#             .dot {{ height: 10px; width: 10px; border-radius: 50%; display: inline-block; margin-right: 5px; }}
#         </style>
#     </head>
#     <body>
#     <div id="controls">
#         <div class="control-group">
#             <span class="label">🔍 Top N:</span>
#             <input type="range" id="limitSlider" min="1" max="{MAX_PRE_FETCH}" value="{max_neighbors}">
#             <span id="limitLabel" class="val">{max_neighbors}</span>
#         </div>
#         <div class="control-group">
#             <span class="label">🕸️ Depth:</span>
#             <input type="range" id="depthSlider" min="1" max="10" value="{depth}" step="1">
#             <span id="depthLabel" class="val">{depth}</span>
#         </div>
#         <div class="control-group">
#             <span class="label">🔵 Size:</span>
#             <input type="range" id="sizeSlider" min="10" max="200" value="100">
#             <span id="sizeLabel" class="val">100%</span>
#         </div>
#         <div class="legend">
#             <span><span class="dot" style="background-color: #00FFFF;"></span>Old</span>
#             <span><span class="dot" style="background-color: #FF0000;"></span>New</span>
#         </div>
#     </div>
#     <div id="mynetwork"></div>
#     <script type="text/javascript">
#         const allNodes = {json.dumps(js_nodes)};
#         const allEdges = {json.dumps(js_edges)};
#         const rootNodeId = "{center_node}";
        
#         const adjacency = {{}};
#         allEdges.forEach(edge => {{
#             if (!adjacency[edge.from]) adjacency[edge.from] = [];
#             if (!adjacency[edge.to]) adjacency[edge.to] = [];
#             adjacency[edge.from].push(edge);
#             adjacency[edge.to].push(edge);
#         }});

#         function getFilteredData(limit, maxDepth, sizeScale) {{
#             const visitedNodes = new Set();
#             const edgesToKeep = new Set();
#             const queue = [{{ id: rootNodeId, depth: 0 }}];
#             visitedNodes.add(rootNodeId);

#             while (queue.length > 0) {{
#                 const {{ id: currentNode, depth: currentDepth }} = queue.shift();
#                 if (currentDepth >= maxDepth) continue;

#                 const myEdges = adjacency[currentNode] || [];
#                 const sortedEdges = myEdges.sort((a, b) => b.value - a.value);
#                 const topEdges = sortedEdges.slice(0, limit);

#                 topEdges.forEach(edge => {{
#                     const neighbor = (edge.from === currentNode) ? edge.to : edge.from;
#                     edgesToKeep.add(edge);
#                     if (!visitedNodes.has(neighbor)) {{
#                         visitedNodes.add(neighbor);
#                         queue.push({{ id: neighbor, depth: currentDepth + 1 }});
#                     }}
#                 }});
#             }}

#             const filteredEdges = Array.from(edgesToKeep);
#             const filteredNodes = allNodes
#                 .filter(n => visitedNodes.has(n.id))
#                 .map(n => {{
#                     return {{ ...n, size: n.baseSize * (sizeScale / 100), value: undefined }};
#                 }});
            
#             return {{ nodes: filteredNodes, edges: filteredEdges }};
#         }}

#         const container = document.getElementById('mynetwork');
#         const options = {{
#             nodes: {{ shape: 'dot', font: {{ color: 'white' }} }},
#             edges: {{ smooth: {{ type: 'continuous' }} }},
#             physics: {{
#                 stabilization: false,
#                 barnesHut: {{ gravitationalConstant: -8000, springConstant: 0.04, springLength: 250, damping: 0.2 }},
#                 minVelocity: 0.75
#             }}
#         }};

#         let currentLimit = {max_neighbors};
#         let currentDepth = {depth};
#         let currentSize = 100;

#         const initialData = getFilteredData(currentLimit, currentDepth, currentSize);
#         const network = new vis.Network(container, initialData, options);

#         const limitSlider = document.getElementById('limitSlider');
#         const depthSlider = document.getElementById('depthSlider');
#         const sizeSlider = document.getElementById('sizeSlider');

#         limitSlider.oninput = function() {{
#             currentLimit = parseInt(this.value);
#             document.getElementById('limitLabel').innerText = currentLimit;
#             network.setData(getFilteredData(currentLimit, currentDepth, currentSize));
#         }}
#         depthSlider.oninput = function() {{
#             currentDepth = parseInt(this.value);
#             document.getElementById('depthLabel').innerText = currentDepth;
#             network.setData(getFilteredData(currentLimit, currentDepth, currentSize));
#         }}
#         sizeSlider.oninput = function() {{
#             currentSize = parseInt(this.value);
#             document.getElementById('sizeLabel').innerText = currentSize + "%";
#             network.setData(getFilteredData(currentLimit, currentDepth, currentSize));
#         }}
#     </script>
#     </body>
#     </html>
#     """
#     return html_string

# # --- RENDER PAGE ---
# st.title("Topic Buzz Explorer 🐝")

# if target_node:
#     display_name = get_node_label(target_node)
#     st.subheader(f"Visualizing: {display_name}")
    
#     # --- GRAPH ---
#     html_data = generate_html(target_node, max_neighbors, depth)
#     components.html(html_data, height=720, scrolling=False)
#     st.info(f"Exploring **{display_name}**. Use the controls inside the graph to filter connections.")

#     # --- METRICS & ANALYSIS ---
#     st.markdown("---")
#     st.subheader(f"📊 Analysis for {display_name}")
    
#     col1, col2 = st.columns(2)
    
#     # Column 1: Direct Neighbors (Who/What are they connecting to?)
#     with col1:
#         st.markdown("#### 🔗 Top Connections")
#         neighbors = list(G.neighbors(target_node))
        
#         # Sort by edge weight (posts)
#         neighbors.sort(key=lambda x: G[target_node][x].get('weight', 1), reverse=True)
        
#         # Convert to DataFrame for nice table
#         conn_data = []
#         for n in neighbors[:15]: # Top 15
#             if not is_node_allowed(n): continue
#             weight = G[target_node][n].get('weight', 1)
#             ts = G[target_node][n].get('last_timestamp', 0)
#             date_str = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d') if ts > 0 else "N/A"
            
#             conn_data.append({
#                 "Name": get_node_label(n),
#                 "Posts": weight,
#                 "Last Active": date_str
#             })
            
#         if conn_data:
#             st.dataframe(pd.DataFrame(conn_data), hide_index=True)
#         else:
#             st.write("No connections found.")

#     # Column 2: Related Entities (Who else is like this?)
#     with col2:
#         if target_node.startswith("User_"):
#             st.markdown("#### 🤝 Similar Users (Shared Topics)")
#         else:
#             st.markdown("#### 🔗 Related Topics (Shared Users)")
            
#         related = get_related_entities(G, target_node, top_n=15)
        
#         related_data = []
#         for node_id, score in related:
#             related_data.append({
#                 "Name": get_node_label(node_id),
#                 "Shared Connections": score
#             })
            
#         if related_data:
#             st.dataframe(pd.DataFrame(related_data), hide_index=True)
#         else:
#             st.write("No related entities found.")

# else:
#     st.write("👈 Select a User or Topic from the sidebar to begin.")
#     st.markdown("""
#     ### How to use:
#     1. **Select a Topic** to see who is talking about it.
#     2. **Select a User** to see what they are interested in.
#     3. **Use the Sliders** inside the graph to filter noise and explore connections.
#     """)











import streamlit as st
import networkx as nx
import pickle
import os
import json
import random
import datetime
import math
import pandas as pd
import streamlit.components.v1 as components
from supabase import create_client, Client # <--- ADDED IMPORT

# --- CONFIGURATION ---
CACHE_FILE = "mastodon_network.pkl"
LABELS_FILE = "topic_labels.json"
BUCKET = "topic_assets" # <--- ADDED BUCKET NAME
MAX_PRE_FETCH = 50 
TIME_SCALE = 1 / 3600 

# Define Blocklist
NSFW_KEYWORDS = {
    "nsfw", "porn", "nude", "naked", "sex", "18+", "xxx", 
    "cum", "dick", "pussy", "hentai", "erotic", "onlyfans",
    "boobs", "tits", "cock", "slut", "whore", "bitch", "incest",
    "lewd", "horny", "sensual", "fetish", "bondage"
}

# --- PAGE CONFIG ---
st.set_page_config(layout="wide", page_title="Mastodon Graph Explorer")

# --- SUPABASE SETUP (Added) ---
# We need this to download the graph if it's missing
SUPABASE_URL = os.environ.get("SUPABASE_URL")
SUPABASE_KEY = os.environ.get("SUPABASE_KEY")
try:
    supabase: Client = create_client(SUPABASE_URL, SUPABASE_KEY)
except Exception:
    st.warning("Supabase credentials missing. Will only look for local files.")
    supabase = None

# --- HELPER: Download from Supabase (Added) ---
def ensure_local_file(filename):
    """
    Checks if a file exists locally. If not, attempts to download it 
    from Supabase Storage and save it to disk.
    """
    if os.path.exists(filename):
        return True
    
    if not supabase:
        return False
        
    print(f"☁️ File {filename} not found locally. Downloading from Supabase...")
    try:
        # 1. Get the bytes from Supabase
        file_bytes = supabase.storage.from_(BUCKET).download(filename)
        
        # 2. Save bytes to local disk
        with open(filename, "wb") as f:
            f.write(file_bytes)
            
        print(f"✅ Downloaded {filename}")
        return True
    except Exception as e:
        print(f"❌ Failed to download {filename}: {e}")
        return False

# --- 1. LOAD DATA (Cached) ---
@st.cache_resource
def load_data():
    data = {"G": None, "labels": {}, "min_ts": 0, "max_ts": 1}
    
    # 1. Check/Download Graph
    if ensure_local_file(CACHE_FILE):
        try:
            with open(CACHE_FILE, 'rb') as f:
                data["G"] = pickle.load(f)
            
            # Pre-calculate Time Range
            G = data["G"]
            timestamps = [
                d['last_timestamp'] 
                for u, v, d in G.edges(data=True) 
                if 'last_timestamp' in d and d['last_timestamp'] > 0
            ]
            if timestamps:
                data["min_ts"] = min(timestamps)
                data["max_ts"] = max(timestamps)
        except Exception as e:
            st.error(f"Error loading graph pickle: {e}")

    # 2. Check/Download Labels
    if ensure_local_file(LABELS_FILE):
        try:
            with open(LABELS_FILE, 'r') as f:
                data["labels"] = json.load(f)
        except Exception:
            pass # Non-critical
            
    return data

data = load_data()
G = data["G"]
labels_map = data["labels"]
MIN_TS = data["min_ts"]
MAX_TS = data["max_ts"]

if G is None:
    st.error(f"Could not load '{CACHE_FILE}'. Check your Supabase bucket or run the build script.")
    st.stop()

# --- HELPER FUNCTIONS ---
def get_time_color(ts):
    """Returns a hex color code from Cyan (Old) to Red (New) using Logarithmic Age."""
    if ts <= 0: return "#888888"
    
    age = (MAX_TS - ts) * TIME_SCALE
    max_age = (MAX_TS - MIN_TS) * TIME_SCALE
    
    if max_age == 0: return "#FF0000"
    
    # Logarithmic decay
    decay = math.log(age + 1) / math.log(max_age + 1)
    ratio = max(0.0, min(1.0, 1.0 - decay))
    
    # Cyan (#00FFFF) to Red (#FF0000)
    r = int(ratio * 255)
    g = int((1 - ratio) * 255)
    b = int((1 - ratio) * 255)
    return f"#{r:02x}{g:02x}{b:02x}"

def is_node_allowed(node_id):
    if node_id == "Topic_0": return False
    label = labels_map.get(node_id, G.nodes[node_id].get('label', node_id))
    if label == "[NSFW]": return False
    if isinstance(label, str):
        label_words = set(label.lower().replace("_", " ").split())
        if not NSFW_KEYWORDS.isdisjoint(label_words):
            return False
    return True

def get_node_label(node_id):
    """Returns the friendly label for a node if available, else the ID."""
    if node_id.startswith("Topic_") and node_id in labels_map:
        return labels_map[node_id]
    return G.nodes[node_id].get('label', node_id)

def get_related_entities(graph, node_id, top_n=10):
    """
    Finds nodes that share the most connections with the target node.
    For a User, finds Users with shared Topics.
    For a Topic, finds Topics with shared Users.
    """
    if node_id not in graph:
        return []
    
    # Get direct neighbors (e.g., Topics for a User)
    direct_neighbors = list(graph.neighbors(node_id))
    
    related_scores = {}
    
    for neighbor in direct_neighbors:
        if not is_node_allowed(neighbor): continue

        # Get neighbors of neighbor (e.g., Users for a Topic)
        second_degree_neighbors = list(graph.neighbors(neighbor))
        
        for second_node in second_degree_neighbors:
            if second_node == node_id: continue
            if not is_node_allowed(second_node): continue
                
            # Score = Number of shared connections (Simple Common Neighbors)
            related_scores[second_node] = related_scores.get(second_node, 0) + 1
            
    # Sort by score descending
    sorted_related = sorted(related_scores.items(), key=lambda x: x[1], reverse=True)
    return sorted_related[:top_n]

# --- SIDEBAR CONTROLS ---
st.sidebar.title("🔍 Graph Controls")

# Prepare Topic List for Dropdown
topic_nodes = [n for n in G.nodes if n.startswith("Topic_")]
topic_options = {}

for t in topic_nodes:
    # Get fancy label if available, otherwise ID
    label = labels_map.get(t, G.nodes[t].get('label', t))
    
    # Skip NSFW/Filtered topics in the dropdown
    if not is_node_allowed(t):
        continue
        
    # Format: "Nature Photography (Topic_5)"
    display_name = f"{label} ({t})"
    topic_options[display_name] = t

# Prepare User List for Dropdown
user_nodes = [n for n in G.nodes if n.startswith("User_")]
user_options = {}

for u in user_nodes:
    # Label is usually "User {id}"
    label = G.nodes[u].get('label', u)
    user_options[label] = u

# Search Mode
search_mode = st.sidebar.radio("Search Mode", ["Select Topic", "Select User", "Random Topic"])

target_node = None

if search_mode == "Select User":
    # Sort options for easier finding
    sorted_users = sorted(user_options.keys())
    selected_user = st.sidebar.selectbox("Search User", sorted_users)
    
    if selected_user:
        target_node = user_options[selected_user]

elif search_mode == "Select Topic":
    # Sort options alphabetically
    sorted_options = sorted(topic_options.keys())
    selected_option = st.sidebar.selectbox("Choose a Topic", sorted_options)
    
    if selected_option:
        target_node = topic_options[selected_option]

elif search_mode == "Random Topic":
    if st.sidebar.button("Pick Random Topic"):
        # Pick from the filtered list we created above
        if topic_options:
            random_display = random.choice(list(topic_options.keys()))
            target_node = topic_options[random_display]
            st.session_state['selected_node'] = target_node

if 'selected_node' in st.session_state and not target_node:
    target_node = st.session_state['selected_node']

# Sliders
st.sidebar.markdown("---")
max_neighbors = st.sidebar.slider("Top Neighbors per Node", 1, 50, 5, help="How many strong connections to show per node.")
depth = st.sidebar.slider("Graph Depth", 1, 2, 2, help="1 = Direct connections only. 2 = Friends of friends.")

# --- MAIN GRAPH GENERATION ---
def generate_html(center_node, max_neighbors, depth):
    # 1. Python BFS (Pre-Filtering)
    nodes_to_include = {center_node}
    edges_to_include = []
    
    l1_neighbors = list(G.neighbors(center_node))
    l1_neighbors.sort(key=lambda x: G[center_node][x].get('weight', 1), reverse=True)
    l1_neighbors = l1_neighbors[:MAX_PRE_FETCH]

    for n1 in l1_neighbors:
        if not is_node_allowed(n1): continue
        nodes_to_include.add(n1)
        edges_to_include.append((center_node, n1))
        
        l2_neighbors = list(G.neighbors(n1))
        l2_neighbors.sort(key=lambda x: G[n1][x].get('weight', 1), reverse=True)
        l2_neighbors = l2_neighbors[:MAX_PRE_FETCH]
        
        for n2 in l2_neighbors:
            if not is_node_allowed(n2): continue
            if n2 != center_node:
                nodes_to_include.add(n2)
                edges_to_include.append((n1, n2))

    # 2. Prepare JSON Data
    js_nodes = []
    for nid in nodes_to_include:
        attr = G.nodes[nid]
        group = attr.get('group', 'unknown')
        base_size = attr.get('size', 10)
        
        # Apply Labels
        if group == "topics" and nid in labels_map:
            label = labels_map[nid]
            if len(label) > 20: label = label[:20] + "..."
            title = f"{nid}: {labels_map[nid]}"
        else:
            label = attr.get('label', nid)
            title = label

        node_data = {
            "id": nid,
            "label": label,
            "group": group,
            "value": base_size, # Default VisJS scaling
            "title": title,
            "baseSize": base_size # Used for custom slider scaling
        }
        
        if nid == center_node:
            node_data["color"] = "#00FF00" 
            node_data["size"] = 40
            node_data["baseSize"] = 40
        elif group == "users":
            node_data["color"] = "#97C2FC"
        else:
            node_data["color"] = "#FB7E81"
            
        js_nodes.append(node_data)

    js_edges = []
    for u, v in edges_to_include:
        data = G[u][v]
        weight = data.get('weight', 1)
        ts = data.get('last_timestamp', 0)
        
        edge_color = get_time_color(ts)
        date_str = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d') if ts > 0 else "Unknown"
        tooltip = f"Posts: {weight}\nLast Active: {date_str}"

        js_edges.append({
            "from": u,
            "to": v,
            "value": weight,
            "title": tooltip,
            "color": edge_color
        })

    # 3. Generate HTML with Embedded Logic
    html_string = f"""
    <!DOCTYPE html>
    <html>
    <head>
        <script type="text/javascript" src="https://unpkg.com/vis-network/standalone/umd/vis-network.min.js"></script>
        <style type="text/css">
            body {{ font-family: sans-serif; background-color: #222; color: white; margin: 0; }}
            #mynetwork {{ width: 100%; height: 700px; border: 1px solid #444; }}
            #controls {{ padding: 15px; background-color: #333; display: flex; align-items: center; gap: 20px; flex-wrap: wrap; }}
            .control-group {{ display: flex; align-items: center; gap: 10px; }}
            input[type=range] {{ width: 150px; cursor: pointer; }}
            .label {{ font-weight: bold; font-size: 14px; color: #ddd; }}
            .val {{ font-family: monospace; color: #00FF00; font-weight: bold; width: 30px; }}
            .legend {{ margin-left: auto; font-size: 12px; color: #aaa; display: flex; gap: 10px; align-items: center; }}
            .dot {{ height: 10px; width: 10px; border-radius: 50%; display: inline-block; margin-right: 5px; }}
        </style>
    </head>
    <body>
    <div id="controls">
        <div class="control-group">
            <span class="label">🔍 Top N:</span>
            <input type="range" id="limitSlider" min="1" max="{MAX_PRE_FETCH}" value="{max_neighbors}">
            <span id="limitLabel" class="val">{max_neighbors}</span>
        </div>
        <div class="control-group">
            <span class="label">🕸️ Depth:</span>
            <input type="range" id="depthSlider" min="1" max="10" value="{depth}" step="1">
            <span id="depthLabel" class="val">{depth}</span>
        </div>
        <div class="control-group">
            <span class="label">🔵 Size:</span>
            <input type="range" id="sizeSlider" min="10" max="200" value="100">
            <span id="sizeLabel" class="val">100%</span>
        </div>
        <div class="legend">
            <span><span class="dot" style="background-color: #00FFFF;"></span>Old</span>
            <span><span class="dot" style="background-color: #FF0000;"></span>New</span>
        </div>
    </div>
    <div id="mynetwork"></div>
    <script type="text/javascript">
        const allNodes = {json.dumps(js_nodes)};
        const allEdges = {json.dumps(js_edges)};
        const rootNodeId = "{center_node}";
        
        const adjacency = {{}};
        allEdges.forEach(edge => {{
            if (!adjacency[edge.from]) adjacency[edge.from] = [];
            if (!adjacency[edge.to]) adjacency[edge.to] = [];
            adjacency[edge.from].push(edge);
            adjacency[edge.to].push(edge);
        }});

        function getFilteredData(limit, maxDepth, sizeScale) {{
            const visitedNodes = new Set();
            const edgesToKeep = new Set();
            const queue = [{{ id: rootNodeId, depth: 0 }}];
            visitedNodes.add(rootNodeId);

            while (queue.length > 0) {{
                const {{ id: currentNode, depth: currentDepth }} = queue.shift();
                if (currentDepth >= maxDepth) continue;

                const myEdges = adjacency[currentNode] || [];
                const sortedEdges = myEdges.sort((a, b) => b.value - a.value);
                const topEdges = sortedEdges.slice(0, limit);

                topEdges.forEach(edge => {{
                    const neighbor = (edge.from === currentNode) ? edge.to : edge.from;
                    edgesToKeep.add(edge);
                    if (!visitedNodes.has(neighbor)) {{
                        visitedNodes.add(neighbor);
                        queue.push({{ id: neighbor, depth: currentDepth + 1 }});
                    }}
                }});
            }}

            const filteredEdges = Array.from(edgesToKeep);
            const filteredNodes = allNodes
                .filter(n => visitedNodes.has(n.id))
                .map(n => {{
                    return {{ ...n, size: n.baseSize * (sizeScale / 100), value: undefined }};
                }});
            
            return {{ nodes: filteredNodes, edges: filteredEdges }};
        }}

        const container = document.getElementById('mynetwork');
        const options = {{
            nodes: {{ shape: 'dot', font: {{ color: 'white' }} }},
            edges: {{ smooth: {{ type: 'continuous' }} }},
            physics: {{
                stabilization: false,
                barnesHut: {{ gravitationalConstant: -8000, springConstant: 0.04, springLength: 250, damping: 0.2 }},
                minVelocity: 0.75
            }}
        }};

        let currentLimit = {max_neighbors};
        let currentDepth = {depth};
        let currentSize = 100;

        const initialData = getFilteredData(currentLimit, currentDepth, currentSize);
        const network = new vis.Network(container, initialData, options);

        const limitSlider = document.getElementById('limitSlider');
        const depthSlider = document.getElementById('depthSlider');
        const sizeSlider = document.getElementById('sizeSlider');

        limitSlider.oninput = function() {{
            currentLimit = parseInt(this.value);
            document.getElementById('limitLabel').innerText = currentLimit;
            network.setData(getFilteredData(currentLimit, currentDepth, currentSize));
        }}
        depthSlider.oninput = function() {{
            currentDepth = parseInt(this.value);
            document.getElementById('depthLabel').innerText = currentDepth;
            network.setData(getFilteredData(currentLimit, currentDepth, currentSize));
        }}
        sizeSlider.oninput = function() {{
            currentSize = parseInt(this.value);
            document.getElementById('sizeLabel').innerText = currentSize + "%";
            network.setData(getFilteredData(currentLimit, currentDepth, currentSize));
        }}
    </script>
    </body>
    </html>
    """
    return html_string

# --- RENDER PAGE ---
st.title("Topic Buzz Explorer 🐝")

if target_node:
    display_name = get_node_label(target_node)
    st.subheader(f"Visualizing: {display_name}")
    
    # --- GRAPH ---
    html_data = generate_html(target_node, max_neighbors, depth)
    components.html(html_data, height=720, scrolling=False)
    st.info(f"Exploring **{display_name}**. Use the controls inside the graph to filter connections.")

    # --- METRICS & ANALYSIS ---
    st.markdown("---")
    st.subheader(f"📊 Analysis for {display_name}")
    
    col1, col2 = st.columns(2)
    
    # Column 1: Direct Neighbors (Who/What are they connecting to?)
    with col1:
        st.markdown("#### 🔗 Top Connections")
        neighbors = list(G.neighbors(target_node))
        
        # Sort by edge weight (posts)
        neighbors.sort(key=lambda x: G[target_node][x].get('weight', 1), reverse=True)
        
        # Convert to DataFrame for nice table
        conn_data = []
        for n in neighbors[:15]: # Top 15
            if not is_node_allowed(n): continue
            weight = G[target_node][n].get('weight', 1)
            ts = G[target_node][n].get('last_timestamp', 0)
            date_str = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d') if ts > 0 else "N/A"
            
            conn_data.append({
                "Name": get_node_label(n),
                "Posts": weight,
                "Last Active": date_str
            })
            
        if conn_data:
            st.dataframe(pd.DataFrame(conn_data), hide_index=True)
        else:
            st.write("No connections found.")

    # Column 2: Related Entities (Who else is like this?)
    with col2:
        if target_node.startswith("User_"):
            st.markdown("#### 🤝 Similar Users (Shared Topics)")
        else:
            st.markdown("#### 🔗 Related Topics (Shared Users)")
            
        related = get_related_entities(G, target_node, top_n=15)
        
        related_data = []
        for node_id, score in related:
            related_data.append({
                "Name": get_node_label(node_id),
                "Shared Connections": score
            })
            
        if related_data:
            st.dataframe(pd.DataFrame(related_data), hide_index=True)
        else:
            st.write("No related entities found.")

else:
    st.write("👈 Select a User or Topic from the sidebar to begin.")
    st.markdown("""
    ### How to use:
    1. **Select a Topic** to see who is talking about it.
    2. **Select a User** to see what they are interested in.
    3. **Use the Sliders** inside the graph to filter noise and explore connections.
    """)