app.py

# app.py
# Mutual Fund Churn Explorer — Smooth organic motion, short-lived (L1)
# D3 + Plotly hybrid layout optimized for phones (simulation stops after ~0.8s)
# Works in Hugging Face Spaces (Gradio)

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)

# ---------------------------
# Graph building & transfer inference
# ---------------------------
def infer_amc_transfers(buy_map, sell_map):
    transfers = defaultdict(int)
    comp_sellers = defaultdict(list)
    comp_buyers = defaultdict(list)
    for amc, comps in sell_map.items():
        for c in comps:
            comp_sellers[c].append(amc)
    for amc, comps in buy_map.items():
        for c in comps:
            comp_buyers[c].append(amc)
    for c in set(comp_sellers.keys()) | set(comp_buyers.keys()):
        for s in comp_sellers[c]:
            for b in comp_buyers[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 and sells
    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"])

    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 buys
    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["weight"]
                G[s][b]["actions"].append("transfer")
            else:
                G.add_edge(s,b, weight=attr["weight"], actions=["transfer"])
    return G

# ---------------------------
# Build plotly figure (positions are placeholders)
# ---------------------------
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=1.4):

    node_names = []
    node_x = []
    node_y = []
    node_colors = []
    node_sizes = []

    for n, d in G.nodes(data=True):
        node_names.append(n)
        node_x.append(0)
        node_y.append(0)
        if d["type"] == "amc":
            node_colors.append(node_color_amc)
            node_sizes.append(36)
        else:
            node_colors.append(node_color_company)
            node_sizes.append(56)

    edge_traces = []
    src_idx = []
    tgt_idx = []
    e_colors = []
    e_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="#aaa", width=1), hoverinfo="none"))
        src_idx.append(node_names.index(u))
        tgt_idx.append(node_names.index(v))

        acts = attrs.get("actions", [])
        w = attrs.get("weight", 1)
        if "complete_exit" in acts:
            e_colors.append(edge_color_sell); e_widths.append(edge_thickness*3)
        elif "fresh_buy" in acts:
            e_colors.append(edge_color_buy); e_widths.append(edge_thickness*3)
        elif "transfer" in acts:
            e_colors.append(edge_color_transfer); e_widths.append(edge_thickness*(1+np.log1p(w)))
        elif "sell" in acts:
            e_colors.append(edge_color_sell); e_widths.append(edge_thickness*(1+np.log1p(w)))
        else:
            e_colors.append(edge_color_buy); e_widths.append(edge_thickness*(1+np.log1p(w)))

    node_trace = go.Scatter(x=node_x, y=node_y, mode="markers+text",
                            marker=dict(color=node_colors, size=node_sizes, line=dict(width=2,color="#333")),
                            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=5,r=5,t=30,b=5),
                      xaxis=dict(visible=False), yaxis=dict(visible=False))

    meta = {
        "node_names": node_names,
        "edge_source_index": src_idx,
        "edge_target_index": tgt_idx,
        "edge_colors": e_colors,
        "edge_widths": e_widths,
        "node_sizes": node_sizes
    }

    return fig, meta

# ---------------------------
# HTML maker: D3 + short-lived smooth motion
# ---------------------------
def make_network_html(fig, meta, div_id="network-plot-div"):
    fig_json = json.dumps(fig.to_plotly_json())
    meta_json = json.dumps(meta)

    # Short-lived simulation parameters:
    # - run for about 0.8s (or until alpha cools)
    # - throttle Plotly updates for performance
    html = f"""
<div id="{div_id}" style="width:100%; height:560px;"></div>
<div style="margin-top:6px;">
  <button id="{div_id}-reset" style="padding:8px 12px; border-radius:6px;">Reset</button>
  <button id="{div_id}-stop" style="padding:8px 12px; margin-left:8px; border-radius:6px;">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}});

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

// build lightweight nodes and links
const nodes = meta.node_names.map((name,i) => ({{
    id: i, name: name, r: meta.node_sizes[i] || 20,
    displayX: 0, displayY: 0, vx_smooth: 0, vy_smooth: 0
}}));
const links = meta.edge_source_index.map((s,i) => ({{
    source: s, target: meta.edge_target_index[i]
}}));

// Gentle simulation tuned to settle quickly
const simulation = d3.forceSimulation(nodes)
    .force("link", d3.forceLink(links).id(d => d.id).distance(120).strength(0.32))
    .force("charge", d3.forceManyBody().strength(-40))
    .force("collision", d3.forceCollide().radius(d => d.r * 0.9))
    .force("center", d3.forceCenter(0,0))
    .velocityDecay(0.48);

// Smoothing interpolation factor for organic motion
const interp = 0.16;

// Throttle updates to Plotly for performance
let tickCounter = 0;
const TICKS_PER_UPDATE = 3; // update Plotly every 3 ticks
let frameCount = 0;
const MAX_TICKS = 120; // safety cap (~0.8-1.0s depending on device)
let stoppedManually = false;

simulation.on("tick", () => {{
    frameCount++;
    tickCounter++;

    // apply smooth interpolation (organic)
    nodes.forEach(n => {{
        const tx = n.x || 0;
        const ty = n.y || 0;

        n.vx_smooth = n.vx_smooth * 0.80 + (tx - n.displayX) * interp;
        n.vy_smooth = n.vy_smooth * 0.80 + (ty - n.displayY) * interp;

        // mild damping
        n.vx_smooth *= 0.92;
        n.vy_smooth *= 0.92;

        n.displayX += n.vx_smooth;
        n.displayY += n.vy_smooth;
    }});

    if (tickCounter % TICKS_PER_UPDATE === 0) {{
        const xs = nodes.map(n => n.displayX);
        const ys = nodes.map(n => n.displayY);
        Plotly.restyle(container, {{ x: [xs], y: [ys] }}, [nodeTraceIndex]);

        for (let e = 0; e < edgeCount; e++) {{
            const s = meta.edge_source_index[e];
            const t = meta.edge_target_index[e];
            const sx = nodes[s].displayX || 0;
            const sy = nodes[s].displayY || 0;
            const tx = nodes[t].displayX || 0;
            const ty = nodes[t].displayY || 0;
            Plotly.restyle(container, {{
                x: [[sx, tx]],
                y: [[sy, ty]],
                "line.color": [meta.edge_colors[e]],
                "line.width": [meta.edge_widths[e]]
            }}, [e]);
        }}
    }}

    // stop conditions: either alpha cooled or reached tick cap or stopped manually
    if (simulation.alpha() < 0.03 || frameCount > MAX_TICKS || stoppedManually) {{
        simulation.stop();
    }}
}});

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

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

// focus node: keep node + direct neighbors (Option A)
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 op = Array(N).fill(0.0);
    const txt = Array(N).fill("rgba(0,0,0,0)");
    for (let i=0;i<N;i++) {{
        if (keep.has(i)) {{ op[i] = 1.0; txt[i] = "black"; }}
    }}
    Plotly.restyle(container, {{ "marker.opacity": [op], "textfont.color": [txt] }}, [nodeTraceIndex]);

    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);
        Plotly.restyle(container, {{
            "line.color": [ show ? meta.edge_colors[e] : "rgba(0,0,0,0)" ],
            "line.width": [ show ? meta.edge_widths[e] : 0.1 ]
        }}, [e]);
    }}
}}

// reset view: restore everything and run a short settling simulation
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]);
    }}

    // restart a very short simulation to gently re-space nodes
    stoppedManually = false;
    frameCount = 0;
    simulation.alpha(0.6);
    simulation.restart();
}}

// click handler to focus
container.on("plotly_click", (evt) => {{
    const p = evt.points && evt.points[0];
    if (p && p.curveNumber === nodeTraceIndex) {{
        const idx = p.pointNumber;
        const name = meta.node_names[idx];
        focusNode(name);
    }}
}});

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

# ---------------------------
# Company / AMC summaries
# ---------------------------
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_text=f"Trade summary for {company}", autosize=True, 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="lightslategray"))
    fig.update_layout(title_text=f"Inferred transfers from {amc}", autosize=True, margin=dict(t=30,b=10))
    return fig, df

# ---------------------------
# Glue: build initial html & Gradio UI
# ---------------------------
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=edge_thickness)
    return make_network_html(fig, meta)

initial_html = build_network_html()

responsive_css = """
.js-plotly-plot { height:560px !important; }
@media(max-width:780px){ .js-plotly-plot{ height:540px !important; } }
"""

with gr.Blocks(css=responsive_css, title="MF Churn Explorer (Smooth Short Motion)") as demo:
    gr.Markdown("## Mutual Fund Churn Explorer — Smooth organic motion (short-lived)")

    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</div>
  <div><span style="display:inline-block;width:28px;border-bottom:5px solid #d62728;"></span> COMPLETE EXIT</div>
</div>
""")

    with gr.Accordion("Customize Network", 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, 1.4, step=0.1, label="Edge thickness")
        include_transfers = gr.Checkbox(True, label="Show inferred AMC→AMC transfers")
        update_btn = gr.Button("Update Graph")

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

    gr.Markdown("### AMC Summary (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_btn.click(fn=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()