app.py

import streamlit as st
import networkx as nx
from pyvis.network import Network
import pickle
import math
import random
import requests
import os
from huggingface_hub import hf_hub_download

# Dictionary to map brands to their respective HuggingFace model repo files
BRAND_GRAPHS = {
    'drumeo': 'drumeo_graph.pkl',
    'pianote': 'pianote_graph.pkl',
    'singeo': 'singeo_graph.pkl',
    'guitareo': 'guitareo_graph.pkl'
}

# HuggingFace Repository Info
#HF_REPO = "MusoraProductDepartment/popular-path-graphs"
AUTH_TOKEN = os.getenv('HF_TOKEN')
API_URL = "https://MusoraProductDepartment-PWGenerator.hf.space/rank_items/"


@st.cache_resource
def load_graph_from_hf(brand):
    """
    Load the graph for the selected brand from HuggingFace Hub.
    """
    try:
        # Download the file from HuggingFace Hub
        HF_REPO = f'MusoraProductDepartment/{brand}-graph'
        cache_dir = '/tmp'
        file_path = hf_hub_download(repo_id=HF_REPO, filename=BRAND_GRAPHS[brand], token=AUTH_TOKEN, cache_dir=cache_dir, repo_type='model')
        # Load the graph
        with open(file_path, 'rb') as f:
            return pickle.load(f)
    except Exception as e:
        st.error(f"Error loading graph from HuggingFace: {e}")
        return None


def filter_graph(graph, node_threshold=10, edge_threshold=5):
    """
    Filters the graph to include only popular nodes and edges.
    """
    popular_nodes = [
        node for node in graph.nodes
        if graph.degree(node) >= node_threshold
    ]

    filtered_graph = graph.subgraph(popular_nodes).copy()

    for u, v, data in list(filtered_graph.edges(data=True)):
        if data.get("weight", 0) < edge_threshold:
            filtered_graph.remove_edge(u, v)

    return filtered_graph


def get_rankings_from_api(brand, user_id, content_ids):
    """
    Call the rank_items API to fetch rankings for the given user and content IDs.
    """
    try:
        payload = {
            "brand": brand.upper(),
            "user_id": int(user_id),
            "content_ids": [int(content_id) for content_id in content_ids]
        }
        headers = {
            "Authorization": f"Bearer {AUTH_TOKEN}",
            "accept": "application/json",
            "Content-Type": "application/json"
        }
        response = requests.post(API_URL, json=payload, headers=headers)
        response.raise_for_status()
        rankings = response.json()
        return rankings
    except Exception as e:
        st.error(f"Error calling rank_items API: {e}")
        return {}


def rank_to_color(rank, max_rank):
    """
    Map a rank to a grayscale color, where dark gray indicates high relevance (low rank),
    and light gray indicates low relevance (high rank).
    """
    if rank > max_rank:  # Handle items without ranking
        return "#E8E8E8"  # Very light gray for unranked items
    intensity = int(55 + (rank / max_rank) * 200)  # Darker for lower ranks
    return f"rgb({intensity}, {intensity}, {intensity})"  # Grayscale


def dynamic_visualize_graph(graph, start_node, layers=3, top_k=5, show_titles=False, rankings=None):
    net = Network(notebook=False, width="100%", height="600px", directed=True)
    net.set_options("""
    var options = {
      "physics": {
        "barnesHut": {
          "gravitationalConstant": -15000,
          "centralGravity": 0.8
        }
      }
    }
    """)

    visited_nodes = set()
    added_edges = set()
    current_nodes = [int(start_node)]

    max_rank = len(rankings) if rankings else 0

    # Add the starting node, color it red, and include a tooltip
    start_title = graph.nodes[int(start_node)].get('title', 'No title available')
    start_in_degree = graph.in_degree(int(start_node))
    start_out_degree = graph.out_degree(int(start_node))
    start_node_size = (start_in_degree + start_out_degree) * 0.15
    start_rank = rankings.index(int(start_node)) if rankings and int(start_node) in rankings else max_rank + 1
    if rankings:
        start_border_color = rank_to_color(start_rank, max_rank)
    else:
        start_border_color = 'darkblue'
    label = str(start_node) if not show_titles else f"{str(start_node)}: {start_title[:15]}..."
    net.add_node(
        int(start_node),
        label=label,
        color={"background": "darkblue", "border": start_border_color},
        title=f"{start_title}, In-degree: {start_in_degree}, Out-degree: {start_out_degree}, Rank: {start_rank}",
        size=start_node_size,
        borderWidth=3,
        borderWidthSelected=6
    )
    visited_nodes.add(int(start_node))

    for layer in range(layers):
        next_nodes = []
        for node in current_nodes:
            neighbors = sorted(
                [(int(neighbor), data['weight']) for neighbor, data in graph[node].items()],
                key=lambda x: x[1],
                reverse=True
            )[:top_k]

            for neighbor, weight in neighbors:
                if neighbor not in visited_nodes:
                    neighbor_title = graph.nodes[neighbor].get('title', 'No title available')
                    neighbor_in_degree = graph.in_degree(neighbor)
                    neighbor_out_degree = graph.out_degree(neighbor)
                    neighbor_size = (neighbor_in_degree + neighbor_out_degree) * 0.15
                    neighbor_rank = rankings.index(neighbor) if rankings and neighbor in rankings else max_rank + 1

                    node_color = 'red' if neighbor_in_degree > neighbor_out_degree * 1.5 else \
                        'green' if neighbor_out_degree > neighbor_in_degree * 1.5 else 'lightblue'
                    if rankings:
                        neighbor_border_color = rank_to_color(neighbor_rank, max_rank)
                    else:
                        neighbor_border_color = node_color

                    label = str(neighbor) if not show_titles else f"{str(neighbor)}: {neighbor_title[:15]}..."
                    net.add_node(
                        neighbor,
                        label=label,
                        title=f"{neighbor_title}, In-degree: {neighbor_in_degree}, Out-degree: {neighbor_out_degree}, Rank: {neighbor_rank}",
                        size=neighbor_size,
                        color={"background": node_color, "border": neighbor_border_color},
                        borderWidth=3,
                        borderWidthSelected=6
                    )
                edge = (node, neighbor)
                if edge not in added_edges:
                    edge_width = math.log(weight + 1) * 8
                    net.add_edge(node, neighbor, label=f"w:{weight}", width=edge_width, color='lightgray')
                    added_edges.add(edge)
                visited_nodes.add(neighbor)
                next_nodes.append(neighbor)

        current_nodes = next_nodes

    html_content = net.generate_html()
    st.components.v1.html(html_content, height=600, scrolling=False)


st.title("Popular Path Expansion + Personalization")

# Brand Selection
selected_brand = st.selectbox("Select a brand:", options=list(BRAND_GRAPHS.keys()))

if "selected_brand" not in st.session_state or st.session_state.selected_brand != selected_brand:
    st.session_state.selected_brand = selected_brand
    G = load_graph_from_hf(selected_brand)

    # Sort nodes by popularity (in-degree + out-degree) and select from top 20
    popular_nodes = sorted(G.nodes, key=lambda n: G.in_degree(n) + G.out_degree(n), reverse=True)
    top_20_nodes = popular_nodes[:20] if len(popular_nodes) > 20 else popular_nodes
    st.session_state.start_node = random.choice(top_20_nodes)
else:
    G = load_graph_from_hf(selected_brand)

# Random Selection Button
if st.button("Random Selection"):
    st.session_state.start_node = random.choice(list(G.nodes))

# Input: Starting node
start_node = st.number_input(
    "Enter the starting node ID:",
    value=st.session_state.start_node,
    step=1
)

# Input: Student ID
student_id = st.text_input("Enter a student ID (optional):", value="")

# Toggle for showing content titles
show_titles = st.checkbox("Show content titles", value=False)

# Filter the graph
node_degree_threshold = 1
edge_weight_threshold = 1
G_filtered = filter_graph(G, node_threshold=node_degree_threshold, edge_threshold=edge_weight_threshold)

# Fetch rankings if student ID is provided
rankings = {}
if student_id:
    content_ids = list(G_filtered.nodes)
    rankings = get_rankings_from_api(selected_brand, int(student_id), content_ids)
    if rankings:
        rankings = rankings['ranked_content_ids']

layers = st.slider("Depth to explore:", 1, 6, value=3)
top_k = st.slider("Branching factor (per node):", 1, 6, value=3)

if st.button("Expand Graph"):
    if start_node in G_filtered:
        dynamic_visualize_graph(G_filtered, start_node, layers=layers, top_k=top_k, show_titles=show_titles, rankings=rankings)
    else:
        st.error("The starting node is not in the graph!")