import pandas as pd
import networkx as nx
import tqdm
import plotly.graph_objects as go
from datasets import load_dataset
import pandas as pd


def load_graph_from_edge_df(
    repo_name: str,
    edge_df: pd.DataFrame,
) -> nx.DiGraph:
    """
    Create a NetworkX directed graph from the dependency edge DataFrame.
    Uses all edge types for centrality calculation.

    Args:
        repo_name: Name of the repository to filter by
        edge_df: DataFrame with columns [repo_name, target, source, edge_type]

    Returns:
        NetworkX DiGraph with edges and edge attributes
    """
    G = nx.DiGraph()
    repo_edge_df = edge_df[edge_df["repo_name"] == repo_name]

    # Add edges with attributes (all edge types for accurate centrality)
    for _, row in repo_edge_df.iterrows():
        source = row["source"]
        target = row["target"]
        edge_type = row["edge_type"]

        # Add edge with attributes
        G.add_edge(source, target, edge_type=edge_type, repo_name=repo_name)

    return G


def init_graphs():
    """Initialize graphs from dependency data on startup"""
    print("Loading dependency data from HuggingFace Hub...")
    dataset = load_dataset(
        "lambdaofgod/pwc_github_search",
        data_files="sample_repo_dependency_records.parquet",
    )
    graph_dependencies_df = dataset["train"].to_pandas()

    repos = graph_dependencies_df["repo_name"].unique()

    graphs = dict()
    print(f"Loading {len(repos)} graphs...")
    for repo_name in tqdm.tqdm(repos):
        graph = load_graph_from_edge_df(repo_name, graph_dependencies_df)
        graphs[repo_name] = graph

    print("Graphs loaded successfully!")
    return graphs


def get_node_type(node, graph):
    """Determine node type based on edge relationships"""
    node_str = str(node)

    # Check if it's a repository (has '/' and is source of repo-file edges)
    if "/" in node_str:
        for _, _, data in graph.edges(node, data=True):
            if data.get("edge_type") == "repo-file":
                return "repository"

    # Check if it's a file (target of repo-file edges or source of file-* edges)
    if ".py" in node_str:
        # Check if it's target of repo-file edge
        for source, target, data in graph.edges(data=True):
            if target == node and data.get("edge_type") == "repo-file":
                return "file"
        # Check if it's source of file-* edges
        for _, _, data in graph.edges(node, data=True):
            edge_type = data.get("edge_type", "")
            if edge_type.startswith("file-"):
                return "file"

    # Check if it's an import (target of file-import or source/target of import-import)
    for source, target, data in graph.edges(data=True):
        edge_type = data.get("edge_type", "")
        if (target == node and edge_type == "file-import") or (
            edge_type == "import-import" and (source == node or target == node)
        ):
            return "import"

    # Check if it's a class (target of file-class edges or source of class-method/inheritance)
    for source, target, data in graph.edges(data=True):
        edge_type = data.get("edge_type", "")
        if target == node and edge_type == "file-class":
            return "class"
        if source == node and edge_type in ["class-method", "inheritance"]:
            return "class"

    # Check if it's a function (target of file-function or function-function edges)
    for source, target, data in graph.edges(data=True):
        edge_type = data.get("edge_type", "")
        if target == node and edge_type == "file-function":
            return "function"
        if edge_type == "function-function" and (source == node or target == node):
            return "function"

    # Check if it's a method (target of class-method edges)
    for source, target, data in graph.edges(data=True):
        if target == node and data.get("edge_type") == "class-method":
            return "method"

    # Default fallback
    return "unknown"


def create_interactive_plotly_graph(
    repo_name, graph, layout_type="spring", selected_edge_types=None
):
    """Create an interactive Plotly graph with node names and edge types"""
    if selected_edge_types is None:
        selected_edge_types = set()
    # Get node positions using selected layout
    if layout_type == "spring":
        pos = nx.spring_layout(graph, k=1, iterations=100)
    elif layout_type == "circular":
        pos = nx.circular_layout(graph)
    elif layout_type == "kamada_kawai":
        pos = nx.kamada_kawai_layout(graph)
    elif layout_type == "fruchterman_reingold":
        pos = nx.fruchterman_reingold_layout(graph, k=1, iterations=100)
    elif layout_type == "shell":
        pos = nx.shell_layout(graph)
    elif layout_type == "spectral":
        pos = nx.spectral_layout(graph)
    elif layout_type == "planar":
        try:
            pos = nx.planar_layout(graph)
        except nx.NetworkXException:
            # Fallback to spring layout if graph is not planar
            pos = nx.spring_layout(graph, k=1, iterations=50)
    else:
        pos = nx.spring_layout(graph, k=1, iterations=50)

    # Filter edges based on selected edge types
    filtered_edges = []
    for edge in graph.edges(data=True):
        edge_type = edge[2].get("edge_type", "unknown")
        if not selected_edge_types or edge_type in selected_edge_types:
            filtered_edges.append(edge)

    # Extract edges with their data
    edge_x = []
    edge_y = []
    edge_info = []

    for edge in filtered_edges:
        x0, y0 = pos[edge[0]]
        x1, y1 = pos[edge[1]]
        edge_x.extend([x0, x1, None])
        edge_y.extend([y0, y1, None])

        # Extract edge type from edge data
        edge_type = edge[2].get("edge_type", "unknown")
        edge_info.append(f"{edge[0]} → {edge[1]}<br>Type: {edge_type}")

    # Create edge trace
    edge_trace = go.Scatter(
        x=edge_x,
        y=edge_y,
        line=dict(width=1, color="#888"),
        hoverinfo="none",
        mode="lines",
        name="Edges",
    )

    # Define color scheme for node types
    node_type_colors = {
        "repository": "#FF6B6B",  # Red
        "file": "#4ECDC4",  # Teal
        "class": "#45B7D1",  # Blue
        "function": "#96CEB4",  # Green
        "method": "#FFEAA7",  # Yellow
        "import": "#FF9F43",  # Orange
        "unknown": "#DDA0DD",  # Plum
    }

    # Get nodes that are connected by filtered edges
    connected_nodes = set()
    for edge in filtered_edges:
        connected_nodes.add(edge[0])
        connected_nodes.add(edge[1])

    # If no edges are selected, show all nodes
    if not selected_edge_types:
        connected_nodes = set(graph.nodes())

    # Calculate degree statistics for opacity normalization
    degrees = [graph.degree(node) for node in connected_nodes]
    min_degree = min(degrees) if degrees else 0
    max_degree = max(degrees) if degrees else 1
    degree_range = max_degree - min_degree if max_degree > min_degree else 1

    # Extract node information
    node_x = []
    node_y = []
    node_text = []
    node_info = []
    node_colors = []
    node_types = []
    node_sizes = []
    node_opacities = []

    for node in connected_nodes:
        x, y = pos[node]
        node_x.append(x)
        node_y.append(y)

        # Determine node type
        node_type = get_node_type(node, graph)
        node_types.append(node_type)

        # Calculate node size based on degree
        degree = graph.degree(node)
        # Scale size between 8 and 25 based on degree
        size = max(8, min(25, 8 + degree * 1.5))
        node_sizes.append(size)

        # Calculate opacity based on normalized degree (0.3 to 1.0)
        normalized_degree = (degree - min_degree) / degree_range
        opacity = 0.3 + (normalized_degree * 0.7)  # Range from 0.3 to 1.0
        node_opacities.append(opacity)

        # Truncate long node names for display
        display_name = str(node)
        if len(display_name) > 30:
            display_name = display_name[:27] + "..."

        node_text.append(display_name)
        node_info.append(
            f"Node: {node}<br>Type: {node_type}<br>Degree: {graph.degree(node)}"
        )

        # Color nodes by type
        node_colors.append(node_type_colors.get(node_type, node_type_colors["unknown"]))

    # Create node trace
    node_trace = go.Scatter(
        x=node_x,
        y=node_y,
        mode="markers+text",
        hoverinfo="text",
        hovertext=node_info,
        text=node_text,
        textposition="middle center",
        textfont=dict(size=8, color="rgba(0,0,0,1)"),
        marker=dict(
            size=node_sizes,
            color=node_colors,
            line=dict(width=1, color="black"),
            opacity=node_opacities,  # Variable opacity based on degree
        ),
        name="Nodes",
    )

    # Create the figure
    fig = go.Figure(data=[edge_trace, node_trace])

    fig.update_layout(
        title=dict(
            text=f"Interactive Dependency Graph: {repo_name}", font=dict(size=16)
        ),
        showlegend=True,
        hovermode="closest",
        margin=dict(b=20, l=5, r=5, t=40),
        annotations=[
            dict(
                text="Hover over nodes for details. Zoom and pan to explore.",
                showarrow=False,
                xref="paper",
                yref="paper",
                x=0.005,
                y=-0.002,
            )
        ],
        xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
        yaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
        plot_bgcolor="white",
    )

    return fig


def get_available_edge_types(graph):
    """Get all unique edge types in the graph"""
    edge_types = set()
    for _, _, data in graph.edges(data=True):
        edge_type = data.get("edge_type", "unknown")
        edge_types.add(edge_type)

    # Define preferred order
    preferred_order = [
        "repo-file",
        "file-class",
        "file-import",
        "inheritance",
        "import-import",
        "file-function",
        "class-method",
        "function-function",
    ]

    # Sort edge types according to preferred order, then alphabetically for any others
    ordered_types = []
    for edge_type in preferred_order:
        if edge_type in edge_types:
            ordered_types.append(edge_type)
            edge_types.remove(edge_type)

    # Add any remaining edge types alphabetically
    ordered_types.extend(sorted(list(edge_types)))

    return ordered_types


def visualize_graph(
    repo_name, graphs_dict, layout_type="spring", selected_edge_types=None
):
    """Visualize the selected repository's graph"""
    if repo_name not in graphs_dict:
        return None, f"Repository '{repo_name}' not found in loaded graphs."

    if repo_name is None:
        return None, "Please select a repository."

    graph = graphs_dict[repo_name]

    # Create interactive Plotly graph
    fig = create_interactive_plotly_graph(
        repo_name, graph, layout_type, selected_edge_types
    )

    # Generate statistics for filtered graph
    edge_types = {}
    filtered_edge_count = 0
    for _, _, data in graph.edges(data=True):
        edge_type = data.get("edge_type", "unknown")
        if not selected_edge_types or edge_type in selected_edge_types:
            edge_types[edge_type] = edge_types.get(edge_type, 0) + 1
            filtered_edge_count += 1

    edge_type_summary = "\n".join(
        [f"  {edge_type}: {count}" for edge_type, count in edge_types.items()]
    )

    # Generate node type statistics for visible nodes
    if selected_edge_types:
        # Get nodes connected by filtered edges
        connected_nodes = set()
        for source, target, data in graph.edges(data=True):
            edge_type = data.get("edge_type", "unknown")
            if edge_type in selected_edge_types:
                connected_nodes.add(source)
                connected_nodes.add(target)
    else:
        connected_nodes = set(graph.nodes())

    node_types = {}
    for node in connected_nodes:
        node_type = get_node_type(node, graph)
        node_types[node_type] = node_types.get(node_type, 0) + 1

    node_type_summary = "\n".join(
        [f"  {node_type}: {count}" for node_type, count in node_types.items()]
    )

    stats = f"""Repository: {repo_name}
Visible nodes: {len(connected_nodes)} / {graph.number_of_nodes()}
Visible edges: {filtered_edge_count} / {graph.number_of_edges()}

Visible node types:
{node_type_summary}

Visible edge types:
{edge_type_summary}
"""

    return fig, stats