# app.py
# D3 physics (client-side) + Plotly visualization for MF churn explorer
# Liquid "gel" motion (viscous, slow, ooze-like) - Option L2
# Requirements: gradio, networkx, plotly, pandas, numpy

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
# ---------------------------
company_edges = []
for amc, comps in BUY_MAP.items():
    for c in comps:
        company_edges.append((amc, c, {"action": "buy", "weight": 1}))
for amc, comps in SELL_MAP.items():
    for c in comps:
        company_edges.append((amc, c, {"action": "sell", "weight": 1}))
for amc, comps in COMPLETE_EXIT.items():
    for c in comps:
        company_edges.append((amc, c, {"action": "complete_exit", "weight": 3}))
for amc, comps in FRESH_BUY.items():
    for c in comps:
        company_edges.append((amc, c, {"action": "fresh_buy", "weight": 3}))

def infer_amc_transfers(buy_map, sell_map):
    transfers = defaultdict(int)
    company_to_sellers = defaultdict(list)
    company_to_buyers = defaultdict(list)
    for amc, comps in sell_map.items():
        for c in comps:
            company_to_sellers[c].append(amc)
    for amc, comps in buy_map.items():
        for c in comps:
            company_to_buyers[c].append(amc)
    for c in set(company_to_sellers.keys()) | set(company_to_buyers.keys()):
        sellers = company_to_sellers[c]
        buyers = company_to_buyers[c]
        for s in sellers:
            for b in buyers:
                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")
    for u,v,attr in company_edges:
        if G.has_edge(u,v):
            G[u][v]["weight"] += attr["weight"]
            G[u][v]["actions"].append(attr["action"])
        else:
            G.add_edge(u,v,weight=attr["weight"], actions=[attr["action"]])
    if include_transfers:
        for s,b,attr in transfer_edges:
            if G.has_edge(s,b):
                G[s][b]["weight"] += attr["weight"]
                G[s][b]["actions"].append("transfer")
            else:
                G.add_edge(s,b,weight=attr["weight"], actions=["transfer"])
    return G

# ---------------------------
# BUILD PLOTLY FIGURE (placeholders for positions)
# ---------------------------
def build_plotly_figure(G,
                        node_color_amc="#9EC5FF",
                        node_color_company="#FFCF9E",
                        edge_color_buy="#2ca02c",
                        edge_color_sell="#d62728",
                        edge_color_transfer="#888888",
                        edge_thickness_base=1.4):
    node_names = []
    node_x = []
    node_y = []
    node_color = []
    node_size = []
    for n,d in G.nodes(data=True):
        node_names.append(n)
        node_x.append(0.0); node_y.append(0.0)
        if d["type"]=="amc":
            node_color.append(node_color_amc); node_size.append(36)
        else:
            node_color.append(node_color_company); node_size.append(56)
    edge_traces = []
    edge_src = []
    edge_tgt = []
    edge_colors = []
    edge_widths = []
    for u,v,attrs in G.edges(data=True):
        edge_traces.append(go.Scatter(x=[0,0], y=[0,0], mode="lines",
                                      line=dict(color="#888", width=1), hoverinfo="none"))
        edge_src.append(node_names.index(u))
        edge_tgt.append(node_names.index(v))
        acts = attrs.get("actions",[])
        weight = attrs.get("weight",1)
        if "complete_exit" in acts:
            edge_colors.append(edge_color_sell); edge_widths.append(edge_thickness_base*3)
        elif "fresh_buy" in acts:
            edge_colors.append(edge_color_buy); edge_widths.append(edge_thickness_base*3)
        elif "transfer" in acts:
            edge_colors.append(edge_color_transfer); edge_widths.append(edge_thickness_base*(1+np.log1p(weight)))
        elif "sell" in acts:
            edge_colors.append(edge_color_sell); edge_widths.append(edge_thickness_base*(1+np.log1p(weight)))
        else:
            edge_colors.append(edge_color_buy); edge_widths.append(edge_thickness_base*(1+np.log1p(weight)))
    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="top center", hoverinfo="text")
    fig = go.Figure(data=edge_traces + [node_trace])
    fig.update_layout(showlegend=False, autosize=True,
                      margin=dict(l=8,r=8,t=36,b=8), xaxis=dict(visible=False), yaxis=dict(visible=False))
    meta = {
        "node_names": node_names,
        "edge_source_index": edge_src,
        "edge_target_index": edge_tgt,
        "edge_colors": edge_colors,
        "edge_widths": edge_widths,
        "node_sizes": node_size
    }
    return fig, meta

# ---------------------------
# Build HTML with D3 + viscous "gel" motion
# ---------------------------
def make_network_html_d3_gel(fig, meta, div_id="network-plot-div"):
    fig_json = json.dumps(fig.to_plotly_json())
    meta_json = json.dumps(meta)
    html = f"""
<div id="{div_id}" style="width:100%;height:560px;"></div>
<div style="margin-top:6px;margin-bottom:8px;">
  <button id="{div_id}-reset" style="padding:8px 12px;border-radius:6px;">Reset view</button>
  <button id="{div_id}-stop" style="padding:8px 12px;border-radius:6px;margin-left:8px;">Stop layout</button>
</div>

<script src="https://d3js.org/d3.v7.min.js"></script>
<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}");
Plotly.newPlot(container, fig.data, fig.layout, {{responsive:true}});

// index bookkeeping
const nodeTraceIndex = fig.data.length - 1;
const edgeCount = fig.data.length - 1;

// build nodes and links for D3
const nodes = meta.node_names.map((n,i)=>({{id:i, name:n, r: meta.node_sizes[i] || 20}}));
const links = meta.edge_source_index.map((s,i)=>({{source:s, target: meta.edge_target_index[i], color: meta.edge_colors[i], width: meta.edge_widths[i] || 1}}));

// Viscous gel simulation parameters (softer, slower motion)
const simulation = d3.forceSimulation(nodes)
    .force("link", d3.forceLink(links).id(d => d.id).distance(140).strength(0.35))
    .force("charge", d3.forceManyBody().strength(-40))
    .force("collision", d3.forceCollide().radius(d => d.r * 0.85))
    .force("center", d3.forceCenter(0,0))
    .velocityDecay(0.55);

// We add per-node velocity smoothing variables to create "gel" feel
nodes.forEach(n => {{
    n.vx_smooth = 0;
    n.vy_smooth = 0;
    n.displayX = n.x || 0;
    n.displayY = n.y || 0;
}});

let tickCount = 0;
const maxTicks = 400; // safety cap

simulation.on("tick", () => {{
    tickCount++;
    // On each tick, update the target positions from d3, then apply viscous smoothing
    nodes.forEach(n => {{
        // D3 provides n.x/n.y; we do gel smoothing on displayX/displayY using velocity
        const targetX = n.x || 0;
        const targetY = n.y || 0;

        // viscous velocity update (gel-like): vx_smooth integrates difference slowly
        n.vx_smooth = (n.vx_smooth * 0.82) + (targetX - n.displayX) * 0.06;
        n.vy_smooth = (n.vy_smooth * 0.82) + (targetY - n.displayY) * 0.06;

        // apply a small damping to give heavy 'gel' inertia
        n.vx_smooth *= 0.92;
        n.vy_smooth *= 0.92;

        // update display positions
        n.displayX += n.vx_smooth;
        n.displayY += n.vy_smooth;
    }});

    // prepare arrays for Plotly update using displayX/displayY
    const xs = nodes.map(n => n.displayX);
    const ys = nodes.map(n => n.displayY);

    // update node trace
    Plotly.restyle(container, {{ 'x': [xs], 'y': [ys] }}, [nodeTraceIndex]);

    // update each edge trace using display positions
    for (let e = 0; e < edgeCount; e++) {{
        const sIdx = meta.edge_source_index[e];
        const tIdx = meta.edge_target_index[e];
        const sx = nodes[sIdx].displayX || 0;
        const sy = nodes[sIdx].displayY || 0;
        const tx = nodes[tIdx].displayX || 0;
        const ty = nodes[tIdx].displayY || 0;
        Plotly.restyle(container, {{ 'x': [[sx, tx]], 'y': [[sy, ty]] }}, [e]);
        // set line style color/width (ensure visual matches original meta)
        Plotly.restyle(container, {{ 'line.color': [meta.edge_colors[e]], 'line.width': [meta.edge_widths[e]] }}, [e]);
    }}

    // Safety stop conditions
    if (simulation.alpha() < 0.02 || tickCount > maxTicks) {{
        simulation.stop();
    }}
}});

// stop button
document.getElementById("{div_id}-stop").addEventListener('click', () => {{
    simulation.stop();
}});

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

// focus and reset functions (hide others on focus - Option A)
function focusNode(nodeName) {{
    const idx = nameToIndex[nodeName];
    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
    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]);
    }}

    // zoom to bbox
    const nodesTrace = fig.data[nodeTraceIndex];
    const xs = [], ys = [];
    for (let j=0;j<meta.node_names.length;j++) {{
        if (keep.has(j)) {{ xs.push(nodesTrace.x[j]); ys.push(nodesTrace.y[j]); }}
    }}
    if (xs.length>0) {{
        const xmin = Math.min(...xs), xmax = Math.max(...xs);
        const ymin = Math.min(...ys), ymax = Math.max(...ys);
        const padX = (xmax - xmin) * 0.4 + 10;
        const padY = (ymax - ymin) * 0.4 + 10;
        Plotly.relayout(container, {{ xaxis: {{ range: [xmin - padX, xmax + padX] }}, yaxis: {{ range: [ymin - padY, ymax + padY] }} }});
    }}
}}

// reset
function resetView() {{
    const N = meta.node_names.length;
    const nodeOp = Array(N).fill(1.0);
    const textColors = Array(N).fill("black");
    Plotly.restyle(container, {{ "marker.opacity": [nodeOp], "textfont.color": [textColors] }}, [nodeTraceIndex]);
    for (let e=0;e<edgeCount;e++) {{
        Plotly.restyle(container, {{ 'line.color': [meta.edge_colors[e]], 'line.width': [meta.edge_widths[e]] }}, [e]);
    }}
    Plotly.relayout(container, {{ xaxis: {{autorange:true}}, yaxis: {{autorange:true}} }});
    // restart a gentle simulation to re-space nodes
    tickCount = 0;
    simulation.alpha(0.5);
    simulation.restart();
}}

// click handler
container.on('plotly_click', function(eventData) {{
    const p = eventData.points[0];
    if (p.curveNumber === nodeTraceIndex) {{
        const nodeIndex = p.pointNumber;
        const nodeName = meta.node_names[nodeIndex];
        focusNode(nodeName);
    }}
}});

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

# ---------------------------
# Company / AMC summaries (unchanged)
# ---------------------------
def company_trade_summary(company_name):
    buyers = [a for a, comps in BUY_MAP.items() if company_name in comps]
    sellers = [a for a, comps in SELL_MAP.items() if company_name in comps]
    fresh = [a for a, comps in FRESH_BUY.items() if company_name in comps]
    exits = [a for a, comps in COMPLETE_EXIT.items() if company_name in comps]
    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_text=f"Trade summary for {company_name}", autosize=True, margin=dict(t=30,b=10))
    return fig, df

def amc_transfer_summary(amc_name):
    sold = SELL_MAP.get(amc_name, [])
    transfers = []
    for s in sold:
        buyers = [a for a, comps in BUY_MAP.items() if s in comps]
        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="lightslategray"))
    fig.update_layout(title_text=f"Inferred transfers from {amc_name}", autosize=True, margin=dict(t=30,b=10))
    return fig, df

# ---------------------------
# Build initial HTML
# ---------------------------
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=1.4, include_transfers=True):
    G = build_graph(include_transfers=include_transfers)
    fig, meta = build_plotly_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_base=edge_thickness)
    return make_network_html_d3_gel(fig, meta)

initial_html = build_network_html()

# ---------------------------
# Mobile CSS & UI
# ---------------------------
responsive_css = """
.gradio-container { padding:0 !important; margin:0 !important; }
.plotly-graph-div, .js-plotly-plot, .output_plot { width:100% !important; max-width:100% !important; }
.js-plotly-plot { height:560px !important; }
@media(max-width:780px){ .js-plotly-plot{ height:520px !important; } }
body, html { overflow-x:hidden !important; }
"""

with gr.Blocks(css=responsive_css, title="MF Churn Explorer (Gel Motion)") as demo:
    gr.Markdown("## Mutual Fund Churn Explorer — Gel-like liquid motion (L2)")

    network_html = gr.HTML(value=initial_html)

    legend_html = gr.HTML(value=\"\"\"
<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, not actual reported transfer)</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("Network Customization — expand to edit", 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=1.4, step=0.1, label="Edge thickness base")
        include_transfers = gr.Checkbox(value=True, label="Show AMC→AMC inferred transfers")
        update_button = gr.Button("Update Network 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_html(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)

    def on_company_select(cname):
        fig, df = company_trade_summary(cname)
        if fig is None:
            return None, pd.DataFrame([], columns=["Role", "AMC"])
        return fig, df

    def on_amc_select(aname):
        fig, df = amc_transfer_summary(aname)
        if fig is None:
            return None, pd.DataFrame([], columns=["security", "buyer_amc"])
        return fig, df

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

    select_company.change(fn=on_company_select, inputs=[select_company], outputs=[company_plot, company_table])
    select_amc.change(fn=on_amc_select, inputs=[select_amc], outputs=[amc_plot, amc_table])

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