app.py

import os
import math
import random
import logging
from typing import List, Dict, Tuple
from flask import Flask, render_template, send_from_directory, jsonify, request

# Logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("audience-segmentation-lab")


def ensure_dirs():
    try:
        os.makedirs('static', exist_ok=True)
        os.makedirs('templates', exist_ok=True)
    except OSError as e:
        logger.warning(f"Create dir failed: {e}")


class CustomFlask(Flask):
    jinja_options = Flask.jinja_options.copy()
    jinja_options.update(dict(
        block_start_string='<%',
        block_end_string='%>',
        variable_start_string='[[',
        variable_end_string=']]',
        comment_start_string='<#',
        comment_end_string='#>',
    ))


app = CustomFlask(__name__, static_folder='static', template_folder='templates')
ensure_dirs()
app.config['MAX_CONTENT_LENGTH'] = 5 * 1024 * 1024


# ---------- Data Utilities ----------
FEATURES = ["age", "income", "clicks", "sessions", "purchase_amount"]


def generate_sample_data(n: int = 240) -> List[Dict]:
    random.seed(42)
    data = []
    # Create 3 archetypes to form visible clusters
    for i in range(n):
        g = i % 3
        if g == 0:
            age = random.randint(22, 35)
            income = random.randint(6000, 15000)
            clicks = random.randint(10, 40)
            sessions = random.randint(5, 18)
            purchase = random.randint(200, 1200)
        elif g == 1:
            age = random.randint(30, 50)
            income = random.randint(12000, 30000)
            clicks = random.randint(2, 12)
            sessions = random.randint(2, 8)
            purchase = random.randint(1000, 5000)
        else:
            age = random.randint(18, 28)
            income = random.randint(3000, 9000)
            clicks = random.randint(20, 60)
            sessions = random.randint(8, 24)
            purchase = random.randint(50, 600)
        data.append({
            "id": i + 1,
            "age": age,
            "income": income,
            "clicks": clicks,
            "sessions": sessions,
            "purchase_amount": purchase
        })
    return data


def to_matrix(items: List[Dict]) -> List[List[float]]:
    return [[float(item[f]) for f in FEATURES] for item in items]


def min_max_scale(X: List[List[float]]) -> Tuple[List[List[float]], List[float], List[float]]:
    if not X:
        return [], [], []
    d = len(X[0])
    mins = [min(row[j] for row in X) for j in range(d)]
    maxs = [max(row[j] for row in X) for j in range(d)]
    scaled = []
    for row in X:
        scaled.append([(row[j] - mins[j]) / (maxs[j] - mins[j] + 1e-9) for j in range(d)])
    return scaled, mins, maxs


def euclidean(a: List[float], b: List[float]) -> float:
    return math.sqrt(sum((x - y) ** 2 for x, y in zip(a, b)))


def kmeans(X: List[List[float]], k: int, max_iters: int = 50) -> Tuple[List[int], List[List[float]]]:
    if not X or k <= 0:
        return [], []
    random.seed(123)
    n = len(X)
    # Initialize centroids as random points
    centroids = [X[idx][:] for idx in random.sample(range(n), min(k, n))]
    assignments = [0] * n
    for _ in range(max_iters):
        changed = False
        # Assign
        for i in range(n):
            distances = [euclidean(X[i], c) for c in centroids]
            new_c = int(min(range(len(centroids)), key=lambda j: distances[j]))
            if assignments[i] != new_c:
                assignments[i] = new_c
                changed = True
        # Update
        new_centroids = []
        for c_idx in range(len(centroids)):
            members = [X[i] for i in range(n) if assignments[i] == c_idx]
            if members:
                d = len(X[0])
                new_centroids.append([sum(m[j] for m in members) / len(members) for j in range(d)])
            else:
                # Reinitialize empty cluster centroid
                new_centroids.append(X[random.randint(0, n - 1)][:])
        centroids = new_centroids
        if not changed:
            break
    return assignments, centroids


def cluster_profiles(items: List[Dict], assignments: List[int], k: int) -> List[Dict]:
    profiles = []
    for c in range(k):
        members = [items[i] for i in range(len(items)) if assignments[i] == c]
        if not members:
            profiles.append({"cluster": c, "size": 0, "means": {f: 0 for f in FEATURES}, "strategy": "数据不足"})
            continue
        means = {f: sum(m[f] for m in members) / len(members) for f in FEATURES}
        # Simple heuristic strategy suggestion
        engagement = (means["clicks"] + means["sessions"]) / 2.0
        value = means["purchase_amount"]
        income = means["income"]
        if value > 1500 and engagement >= 8:
            strategy = "主推高客单价产品 + 专属折扣"
        elif value > 800 and engagement >= 5:
            strategy = "提升复购率：会员积分、订阅优惠"
        elif engagement < 6 and income >= 10000:
            strategy = "教育型内容 + 品牌信任建设（提高点击与会话）"
        else:
            strategy = "引导新手：新手礼包、首次下单优惠"
        budget_pct = max(5, min(50, int((value / 2000.0 + engagement / 20.0) * 50)))
        profiles.append({
            "cluster": c,
            "size": len(members),
            "means": means,
            "strategy": strategy,
            "recommended_budget_pct": budget_pct
        })
    return profiles


# ---------- Routes ----------
@app.route('/')
def index():
    return render_template('index.html')


@app.route('/static/<path:path>')
def serve_static(path):
    return send_from_directory('static', path)


@app.route('/health')
def health():
    return jsonify(status='ok'), 200


@app.errorhandler(404)
def page_not_found(e):
    return render_template('index.html'), 200


@app.errorhandler(500)
def internal_error(e):
    return jsonify(error=str(e)), 500


@app.route('/api/sample', methods=['GET'])
def api_sample():
    n = int(request.args.get('n', 240))
    return jsonify(data=generate_sample_data(n))


@app.route('/api/segment', methods=['POST'])
def api_segment():
    payload = request.get_json(silent=True) or {}
    items = payload.get('data') or generate_sample_data(240)
    k = int(payload.get('k') or 3)
    X = to_matrix(items)
    X_scaled, mins, maxs = min_max_scale(X)
    assignments, centroids = kmeans(X_scaled, k)
    profiles = cluster_profiles(items, assignments, k)
    return jsonify(
        ok=True,
        k=k,
        assignments=assignments,
        centroids=centroids,
        profiles=profiles,
        mins=mins,
        maxs=maxs
    )


if __name__ == '__main__':
    port = int(os.environ.get('PORT', 7860))
    app.run(host='0.0.0.0', port=port)