import streamlit as st
import streamlit.components.v1 as components
import json
import networkx as nx
from pyvis.network import Network

st.set_page_config(page_title="Education Network Explorer", layout="wide")
st.title("📚 Education Network Explorer")

# ---------- Load graph from JSON ----------
@st.cache_data
def load_graph_from_json(path: str):
    with open(path, "r", encoding="utf-8") as f:
        data = json.load(f)

    G = nx.Graph()
    for node in data.get("nodes", []):
        G.add_node(
            node["id"],
            label=node.get("label", node["id"]),
            influencer_count=node.get("influencer_count", 0),
            top_influencers=node.get("top_influencers", []),
        )
    for edge in data.get("edges", []):
        G.add_edge(edge["source"], edge["target"], weight=edge.get("weight", 1))
    return G

nx_graph = load_graph_from_json("education_graph.json")

# ---------- Intro ----------
st.markdown("""
Welcome to the **Education Network Explorer** – an interactive dashboard for exploring relationships between education topics on Twitter.

– Nodes are topics  
– Edges connect topics that share influencers  
– Hover a node to see influencer count and top users
""")
st.markdown("---")

# ---------- Main preview (static HTML you generated in Colab) ----------
st.subheader("🔍 Interactive Network Graph")
try:
    with open("education_graph_preview.html", "r", encoding="utf-8") as f:
        graph_html = f.read()
    components.html(graph_html, height=750, scrolling=True)
except FileNotFoundError:
    st.warning("`education_graph_preview.html` not found. Upload it to the Space or regenerate it.")

st.markdown("---")

# ---------- Subgraph explorer ----------
st.subheader("🎯 Explore by Topic")

# Show labels in the dropdown, map back to node ids
all_nodes = sorted(nx_graph.nodes())
label_by_id = {n: nx_graph.nodes[n].get("label", n) for n in all_nodes}
id_by_label = {v: k for k, v in label_by_id.items()}

selected_label = st.selectbox("Select a topic", [label_by_id[n] for n in all_nodes])
selected_topic = id_by_label[selected_label]

# Build neighborhood subgraph
sub_nodes = [selected_topic] + list(nx_graph.neighbors(selected_topic))
subgraph = nx_graph.subgraph(sub_nodes)

# PyVis render of the subgraph
net = Network(height="750px", width="100%", notebook=False, bgcolor="#ffffff", font_color="black")
net.barnes_hut()
net.from_nx(subgraph)

# Enrich tooltips/labels
for node in net.nodes:
    nid = node["id"]
    tdata = nx_graph.nodes[nid]
    label = tdata.get("label", nid)
    count = tdata.get("influencer_count", 0)
    top_users = tdata.get("top_influencers", [])
    node["title"] = f"<b>{label}</b><br>Influencers: {count}<br>Top: {', '.join(top_users)}"
    node["label"] = label

net.save_graph("education_graph_filtered.html")
with open("education_graph_filtered.html", "r", encoding="utf-8") as f:
    components.html(f.read(), height=750, scrolling=True)

st.markdown("---")
st.markdown("ℹ️ *Click a node to view influencer data in the tooltip.*")