# app.py
# Static bipartite network for Mutual Fund Churn Explorer
# Left = AMCs, Right = Companies. Static positions (no animation). Mobile-safe.

import gradio as gr
import pandas as pd
import networkx as nx
import plotly.graph_objects as go
import numpy as np
import json
from collections import defaultdict

# ---------------------------
# DATA
# ---------------------------
AMCS = [
    "SBI MF", "ICICI Pru MF", "HDFC MF", "Nippon India MF", "Kotak MF",
    "UTI MF", "Axis MF", "Aditya Birla SL MF", "Mirae MF", "DSP MF"
]

COMPANIES = [
    "HDFC Bank", "ICICI Bank", "Bajaj Finance", "Bajaj Finserv", "Adani Ports",
    "Tata Motors", "Shriram Finance", "HAL", "TCS", "AU Small Finance Bank",
    "Pearl Global", "Hindalco", "Tata Elxsi", "Cummins India", "Vedanta"
]

BUY_MAP = {
    "SBI MF": ["Bajaj Finance", "AU Small Finance Bank"],
    "ICICI Pru MF": ["HDFC Bank"],
    "HDFC MF": ["Tata Elxsi", "TCS"],
    "Nippon India MF": ["Hindalco"],
    "Kotak MF": ["Bajaj Finance"],
    "UTI MF": ["Adani Ports", "Shriram Finance"],
    "Axis MF": ["Tata Motors", "Shriram Finance"],
    "Aditya Birla SL MF": ["AU Small Finance Bank"],
    "Mirae MF": ["Bajaj Finance", "HAL"],
    "DSP MF": ["Tata Motors", "Bajaj Finserv"]
}

SELL_MAP = {
    "SBI MF": ["Tata Motors"],
    "ICICI Pru MF": ["Bajaj Finance", "Adani Ports"],
    "HDFC MF": ["HDFC Bank"],
    "Nippon India MF": ["Hindalco"],
    "Kotak MF": ["AU Small Finance Bank"],
    "UTI MF": ["Hindalco", "TCS"],
    "Axis MF": ["TCS"],
    "Aditya Birla SL MF": ["Adani Ports"],
    "Mirae MF": ["TCS"],
    "DSP MF": ["HAL", "Shriram Finance"]
}

COMPLETE_EXIT = {"DSP MF": ["Shriram Finance"]}
FRESH_BUY = {"HDFC MF": ["Tata Elxsi"], "UTI MF": ["Adani Ports"], "Mirae MF": ["HAL"]}

def sanitize_map(m):
    out = {}
    for k, vals in m.items():
        out[k] = [v for v in vals if v in COMPANIES]
    return out

BUY_MAP = sanitize_map(BUY_MAP)
SELL_MAP = sanitize_map(SELL_MAP)
COMPLETE_EXIT = sanitize_map(COMPLETE_EXIT)
FRESH_BUY = sanitize_map(FRESH_BUY)

# ---------------------------
# Build graph + inferred transfers
# ---------------------------
def infer_amc_transfers(buy_map, sell_map):
    transfers = defaultdict(int)
    c2s = defaultdict(list)
    c2b = defaultdict(list)
    for amc, comps in sell_map.items():
        for c in comps:
            c2s[c].append(amc)
    for amc, comps in buy_map.items():
        for c in comps:
            c2b[c].append(amc)
    for c in set(c2s.keys()) | set(c2b.keys()):
        for s in c2s[c]:
            for b in c2b[c]:
                transfers[(s,b)] += 1
    out = []
    for (s,b), w in transfers.items():
        out.append((s,b,{"action":"transfer","weight":w}))
    return out

transfer_edges = infer_amc_transfers(BUY_MAP, SELL_MAP)

def build_graph(include_transfers=True):
    G = nx.DiGraph()
    for a in AMCS: G.add_node(a, type="amc")
    for c in COMPANIES: G.add_node(c, type="company")
    # buys
    for amc, comps in BUY_MAP.items():
        for c in comps:
            if G.has_edge(amc, c):
                G[amc][c]["weight"] += 1
                G[amc][c]["actions"].append("buy")
            else:
                G.add_edge(amc, c, weight=1, actions=["buy"])
    # sells
    for amc, comps in SELL_MAP.items():
        for c in comps:
            if G.has_edge(amc, c):
                G[amc][c]["weight"] += 1
                G[amc][c]["actions"].append("sell")
            else:
                G.add_edge(amc, c, weight=1, actions=["sell"])
    # complete exits
    for amc, comps in COMPLETE_EXIT.items():
        for c in comps:
            if G.has_edge(amc, c):
                G[amc][c]["weight"] += 3
                G[amc][c]["actions"].append("complete_exit")
            else:
                G.add_edge(amc, c, weight=3, actions=["complete_exit"])
    # fresh buy
    for amc, comps in FRESH_BUY.items():
        for c in comps:
            if G.has_edge(amc, c):
                G[amc][c]["weight"] += 3
                G[amc][c]["actions"].append("fresh_buy")
            else:
                G.add_edge(amc, c, weight=3, actions=["fresh_buy"])
    # inferred transfers
    if include_transfers:
        for s,b,attr in transfer_edges:
            if G.has_edge(s,b):
                G[s][b]["weight"] += attr.get("weight",1)
                G[s][b]["actions"].append("transfer")
            else:
                G.add_edge(s,b, weight=attr.get("weight",1), actions=["transfer"])
    return G

# ---------------------------
# Static bipartite layout generator
# ---------------------------
def bipartite_positions(G, left_nodes, right_nodes, x_left=-1.0, x_right=1.0, y_pad=0.1):
    """
    Place left_nodes at x_left and right_nodes at x_right.
    Spread nodes vertically from -1..1 with padding y_pad.
    Returns dict {node: (x,y)}
    """
    pos = {}
    # left column
    nL = len(left_nodes)
    if nL == 1:
        ysL = [0.0]
    else:
        span = 2.0 - 2*y_pad
        ysL = [ -1 + y_pad + i * (span/(nL-1)) for i in range(nL) ]
    for n, y in zip(left_nodes, ysL):
        pos[n] = (x_left, y)
    # right column
    nR = len(right_nodes)
    if nR == 1:
        ysR = [0.0]
    else:
        span = 2.0 - 2*y_pad
        ysR = [ -1 + y_pad + i * (span/(nR-1)) for i in range(nR) ]
    for n, y in zip(right_nodes, ysR):
        pos[n] = (x_right, y)
    return pos

# ---------------------------
# Build static Plotly figure
# ---------------------------
def build_plotly_static_figure(G,
                              node_color_amc="#9EC5FF",
                              node_color_company="#FFCF9E",
                              edge_color_buy="#2ca02c",
                              edge_color_sell="#d62728",
                              edge_color_transfer="#888888",
                              edge_thickness=1.6):
    # positions: left=AMCS, right=COMPANIES
    pos = bipartite_positions(G, AMCS, COMPANIES, x_left=-1.0, x_right=1.0, y_pad=0.06)

    node_names = []
    node_x = []
    node_y = []
    node_color = []
    node_size = []
    node_type = []

    for n, d in G.nodes(data=True):
        node_names.append(n)
        x,y = pos[n]
        node_x.append(x)
        node_y.append(y)
        if d["type"] == "amc":
            node_color.append(node_color_amc); node_size.append(36); node_type.append("amc")
        else:
            node_color.append(node_color_company); node_size.append(52); node_type.append("company")

    # create edge traces (one per edge for easy restyle)
    edge_traces = []
    edge_src_idx = []
    edge_tgt_idx = []
    edge_colors = []
    edge_widths = []

    for u,v,attrs in G.edges(data=True):
        x0,y0 = pos[u]; x1,y1 = pos[v]
        acts = attrs.get("actions", [])
        w = attrs.get("weight", 1)
        if "complete_exit" in acts:
            color = edge_color_sell; width = edge_thickness * 3; dash = "solid"
        elif "fresh_buy" in acts:
            color = edge_color_buy; width = edge_thickness * 3; dash = "solid"
        elif "transfer" in acts:
            color = edge_color_transfer; width = edge_thickness * (1 + np.log1p(w)); dash = "dash"
        elif "sell" in acts:
            color = edge_color_sell; width = edge_thickness * (1 + np.log1p(w)); dash = "dot"
        else:
            color = edge_color_buy; width = edge_thickness * (1 + np.log1p(w)); dash = "solid"

        edge_traces.append(go.Scatter(
            x=[x0, x1], y=[y0, y1],
            mode="lines",
            line=dict(color=color, width=width, dash=dash),
            hoverinfo="text",
            text=f"{u} → {v} ({', '.join(acts)})"
        ))
        edge_src_idx.append(node_names.index(u))
        edge_tgt_idx.append(node_names.index(v))
        edge_colors.append(color)
        edge_widths.append(width)

    node_trace = go.Scatter(
        x=node_x, y=node_y,
        mode="markers+text",
        marker=dict(color=node_color, size=node_size, line=dict(width=2, color="#222")),
        text=node_names,
        textposition="middle right",
        hoverinfo="text"
    )

    fig = go.Figure(data=edge_traces + [node_trace])
    fig.update_layout(
        title="Mutual Fund Churn — Static Bipartite Layout",
        showlegend=False,
        autosize=True,
        margin=dict(l=10, r=10, t=40, b=10),
        xaxis=dict(visible=False),
        yaxis=dict(visible=False)
    )

    meta = {
        "node_names": node_names,
        "edge_source_index": edge_src_idx,
        "edge_target_index": edge_tgt_idx,
        "edge_colors": edge_colors,
        "edge_widths": edge_widths,
        "node_x": node_x,
        "node_y": node_y,
    }

    return fig, meta

# ---------------------------
# Make HTML (static) with JS click handlers
# ---------------------------
def make_static_html(fig, meta, div_id="network-plot-div"):
    fig_json = json.dumps(fig.to_plotly_json())
    meta_json = json.dumps(meta)
    # NOTE: inside this f-string we must double braces for JS object blocks
    html = f"""
<div id="{div_id}" style="width:100%; height:580px;"></div>
<div style="margin-top:6px;">
  <button id="{div_id}-reset" style="padding:8px 12px; border-radius:6px;">Reset</button>
</div>

<script src="https://cdn.plot.ly/plotly-latest.min.js"></script>

<script>
const fig = {fig_json};
const meta = {meta_json};
const container = document.getElementById("{div_id}");

// Render plotly figure (static positions embedded)
Plotly.newPlot(container, fig.data, fig.layout, {{responsive:true}});

const nodeTraceIndex = fig.data.length - 1;
const edgeCount = fig.data.length - 1;

// Map name -> index
const nameToIndex = {{}};
meta.node_names.forEach((n,i) => nameToIndex[n]=i);

// Focus node: show only node + neighbors, hide others (including labels)
function focusNode(name) {{
    const idx = nameToIndex[name];
    const keep = new Set([idx]);

    for (let e=0; e < meta.edge_source_index.length; e++) {{
        const s = meta.edge_source_index[e], t = meta.edge_target_index[e];
        if (s === idx) keep.add(t);
        if (t === idx) keep.add(s);
    }}

    const N = meta.node_names.length;
    const nodeOp = Array(N).fill(0.0);
    const textColors = Array(N).fill("rgba(0,0,0,0)");
    for (let i=0;i<N;i++) {{
        if (keep.has(i)) {{ nodeOp[i]=1.0; textColors[i]="black"; }}
    }}

    Plotly.restyle(container, {{
        "marker.opacity": [nodeOp],
        "textfont.color": [textColors]
    }}, [nodeTraceIndex]);

    // edges: show only those connecting kept nodes
    for (let e=0; e < edgeCount; e++) {{
        const s = meta.edge_source_index[e], t = meta.edge_target_index[e];
        const show = keep.has(s) && keep.has(t);
        const color = show ? meta.edge_colors[e] : "rgba(0,0,0,0)";
        const width = show ? meta.edge_widths[e] : 0.1;
        Plotly.restyle(container, {{
            "line.color": [color],
            "line.width": [width]
        }}, [e]);
    }}
}}

// Reset view
function resetView() {{
    const N = meta.node_names.length;
    Plotly.restyle(container, {{
        "marker.opacity": [Array(N).fill(1.0)],
        "textfont.color": [Array(N).fill("black")]
    }}, [nodeTraceIndex]);

    for (let e=0; e < edgeCount; e++) {{
        Plotly.restyle(container, {{
            "line.color": [meta.edge_colors[e]],
            "line.width": [meta.edge_widths[e]]
        }}, [e]);
    }}
}}

// Hook click
container.on('plotly_click', function(evt) {{
    const p = evt.points && evt.points[0];
    if (p && p.curveNumber === nodeTraceIndex) {{
        const name = meta.node_names[p.pointNumber];
        focusNode(name);
    }}
}});

// Hook reset button
document.getElementById("{div_id}-reset").addEventListener("click", resetView);
</script>
"""
    return html

# ---------------------------
# Company & AMC summaries (unchanged)
# ---------------------------
def company_trade_summary(company):
    buyers = [a for a,cs in BUY_MAP.items() if company in cs]
    sellers = [a for a,cs in SELL_MAP.items() if company in cs]
    fresh = [a for a,cs in FRESH_BUY.items() if company in cs]
    exits = [a for a,cs in COMPLETE_EXIT.items() if company in cs]

    df = pd.DataFrame({
        "Role": ["Buyer"]*len(buyers) + ["Seller"]*len(sellers) + ["Fresh buy"]*len(fresh) + ["Complete exit"]*len(exits),
        "AMC": buyers + sellers + fresh + exits
    })
    if df.empty:
        return None, pd.DataFrame([], columns=["Role","AMC"])
    counts = df.groupby("Role").size().reset_index(name="Count")
    fig = go.Figure(go.Bar(x=counts["Role"], y=counts["Count"], marker_color=["green","red","orange","black"][:len(counts)]))
    fig.update_layout(title=f"Trade summary for {company}", margin=dict(t=30,b=10))
    return fig, df

def amc_transfer_summary(amc):
    sold = SELL_MAP.get(amc, [])
    transfers = []
    for s in sold:
        buyers = [a for a,cs in BUY_MAP.items() if s in cs]
        for b in buyers:
            transfers.append({"security": s, "buyer_amc": b})
    df = pd.DataFrame(transfers)
    if df.empty:
        return None, pd.DataFrame([], columns=["security","buyer_amc"])
    counts = df["buyer_amc"].value_counts().reset_index()
    counts.columns = ["Buyer AMC","Count"]
    fig = go.Figure(go.Bar(x=counts["Buyer AMC"], y=counts["Count"], marker_color="gray"))
    fig.update_layout(title=f"Inferred transfers from {amc}", margin=dict(t=30,b=10))
    return fig, df

# ---------------------------
# Build static figure & meta
# ---------------------------
def build_network_html(node_color_company="#FFCF9E",
                       node_color_amc="#9EC5FF",
                       edge_color_buy="#2ca02c",
                       edge_color_sell="#d62728",
                       edge_color_transfer="#888888",
                       edge_thickness=1.6,
                       include_transfers=True):
    G = build_graph(include_transfers=include_transfers)
    fig, meta = build_plotly_static_figure(
        G,
        node_color_amc=node_color_amc,
        node_color_company=node_color_company,
        edge_color_buy=edge_color_buy,
        edge_color_sell=edge_color_sell,
        edge_color_transfer=edge_color_transfer,
        edge_thickness=edge_thickness
    )
    return make_static_html(fig, meta)

initial_html = build_network_html()

# ---------------------------
# Gradio UI
# ---------------------------
responsive_css = """
.js-plotly-plot { height:560px !important; }
@media(max-width:780px){ .js-plotly-plot{ height:520px !important; } }
"""

with gr.Blocks(css=responsive_css, title="MF Churn Explorer — Static Bipartite") as demo:
    gr.Markdown("## Mutual Fund Churn Explorer — Static Bipartite Layout (mobile-friendly)")

    network_html = gr.HTML(value=initial_html)

    legend_html = gr.HTML("""
<div style='font-family:sans-serif;font-size:14px;margin-top:10px;line-height:1.6;'>
  <b>Legend</b><br>
  <div><span style="display:inline-block;width:28px;border-bottom:3px solid #2ca02c;"></span> BUY (green solid)</div>
  <div><span style="display:inline-block;width:28px;border-bottom:3px dotted #d62728;"></span> SELL (red dotted)</div>
  <div><span style="display:inline-block;width:28px;border-bottom:3px dashed #888;"></span> TRANSFER (grey dashed — inferred)</div>
  <div><span style="display:inline-block;width:28px;border-bottom:5px solid #2ca02c;"></span> FRESH BUY (thick green)</div>
  <div><span style="display:inline-block;width:28px;border-bottom:5px solid #d62728;"></span> COMPLETE EXIT (thick red)</div>
</div>
""")

    with gr.Accordion("Customize Network (static)", open=False):
        node_color_company = gr.ColorPicker("#FFCF9E", label="Company node color")
        node_color_amc = gr.ColorPicker("#9EC5FF", label="AMC node color")
        edge_color_buy = gr.ColorPicker("#2ca02c", label="BUY edge color")
        edge_color_sell = gr.ColorPicker("#d62728", label="SELL edge color")
        edge_color_transfer = gr.ColorPicker("#888888", label="Transfer edge color")
        edge_thickness = gr.Slider(0.5, 6.0, value=1.6, step=0.1, label="Edge thickness")
        include_transfers = gr.Checkbox(value=True, label="Show inferred AMC→AMC transfers")
        update_button = gr.Button("Update Graph")

    gr.Markdown("### Inspect Company (buyers / sellers)")
    select_company = gr.Dropdown(choices=COMPANIES, label="Select company")
    company_plot = gr.Plot()
    company_table = gr.DataFrame()

    gr.Markdown("### Inspect AMC (inferred transfers)")
    select_amc = gr.Dropdown(choices=AMCS, label="Select AMC")
    amc_plot = gr.Plot()
    amc_table = gr.DataFrame()

    def update_network(node_color_company_val, node_color_amc_val,
                       edge_color_buy_val, edge_color_sell_val, edge_color_transfer_val,
                       edge_thickness_val, include_transfers_val):
        return build_network_html(node_color_company=node_color_company_val,
                                  node_color_amc=node_color_amc_val,
                                  edge_color_buy=edge_color_buy_val,
                                  edge_color_sell=edge_color_sell_val,
                                  edge_color_transfer=edge_color_transfer_val,
                                  edge_thickness=edge_thickness_val,
                                  include_transfers=include_transfers_val)

    update_button.click(update_network,
                        inputs=[node_color_company, node_color_amc,
                                edge_color_buy, edge_color_sell, edge_color_transfer,
                                edge_thickness, include_transfers],
                        outputs=[network_html])

    def on_company(c):
        fig, df = company_trade_summary(c)
        return fig, df

    def on_amc(a):
        fig, df = amc_transfer_summary(a)
        return fig, df

    select_company.change(on_company, inputs=[select_company], outputs=[company_plot, company_table])
    select_amc.change(on_amc, inputs=[select_amc], outputs=[amc_plot, amc_table])

if __name__ == "__main__":
    demo.launch()