import os
import logging
import numpy as np
import pandas as pd
from scipy.optimize import minimize_scalar
from flask import Flask, render_template, send_from_directory, request, jsonify
from werkzeug.utils import secure_filename

# 配置日志
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

app = Flask(__name__)
app.config['JSON_AS_ASCII'] = False  # 支持中文 JSON
app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024  # 限制上传文件大小为 16MB
app.config['UPLOAD_FOLDER'] = 'uploads'

# 确保上传目录存在
os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)

@app.route('/')
def index():
    # 使用 render_template 渲染模板，前端需注意 Vue 分隔符冲突问题
    return render_template('index.html')

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

def calculate_demand_linear(price, base_price, base_demand, elasticity):
    """
    线性需求模型: D = a - bP
    在基准点 (base_price, base_demand) 处的弹性为 elasticity
    """
    # 弹性定义: E = (dD/dP) * (P/D)
    # 线性: dD/dP = -b
    # E = -b * (base_price / base_demand)
    # -> b = -E * (base_demand / base_price)
    # -> a = base_demand + b * base_price
    
    # 确保弹性为负值 (输入如果是正数则取反)
    e = -abs(elasticity)
    
    b = -e * (base_demand / base_price)
    a = base_demand + b * base_price
    
    demand = a - b * price
    return np.maximum(demand, 0) # 需求不能为负

def calculate_demand_constant_elasticity(price, base_price, base_demand, elasticity):
    """
    恒定弹性模型 (指数模型): D = A * P^e
    """
    # base_demand = A * base_price^e
    # -> A = base_demand / (base_price^e)
    
    e = -abs(elasticity)
    A = base_demand / (base_price ** e)
    
    demand = A * (price ** e)
    return demand

def run_optimization(cost, base_price, base_demand, elasticity, model_type='linear'):
    """
    运行价格优化算法
    """
    # 模拟范围 (0.5x 成本 到 3x 成本，或者基准价格的 2 倍)
    min_p = max(cost * 0.8, 0.01)
    max_p = max(base_price * 2.0, cost * 3.0)
    
    prices = np.linspace(min_p, max_p, 100)
    
    results = []
    max_profit = -float('inf')
    optimal_price = base_price
    
    # 第一次粗略扫描
    for p in prices:
        if model_type == 'constant':
            d = calculate_demand_constant_elasticity(p, base_price, base_demand, elasticity)
        else:
            d = calculate_demand_linear(p, base_price, base_demand, elasticity)
        
        revenue = p * d
        profit = (p - cost) * d
        
        if profit > max_profit:
            max_profit = profit
            optimal_price = p
            
        results.append({
            'price': round(p, 2),
            'demand': round(d, 2),
            'revenue': round(revenue, 2),
            'profit': round(profit, 2)
        })

    # 第二次精细优化 (SciPy minimize_scalar)
    # 确定搜索上界
    zero_demand_price = max_p
    if model_type == 'linear':
            # a - bP = 0 -> P = a/b
            e_val = -abs(elasticity)
            b_val = -e_val * (base_demand / base_price)
            if b_val != 0:
                a_val = base_demand + b_val * base_price
                zero_demand_price = a_val / b_val
    
    upper_bound = min(max_p * 1.5, zero_demand_price * 1.1 if model_type == 'linear' else max_p * 1.5)

    def profit_func(p):
        if p <= 0: return 1e9 # 惩罚项
        if model_type == 'constant':
            d = calculate_demand_constant_elasticity(p, base_price, base_demand, elasticity)
        else:
            d = calculate_demand_linear(p, base_price, base_demand, elasticity)
        
        # 目标是最大化利润，即最小化 -利润
        prof = (p - cost) * d
        return -prof 

    res = minimize_scalar(profit_func, bounds=(cost, upper_bound), method='bounded')
    
    refined_optimal_price = optimal_price 
    refined_max_profit = max_profit

    if res.success:
        opt_p = res.x
        opt_prof = -res.fun
        # 只有当结果更好时才采纳 (避免局部极小值)
        if opt_prof >= max_profit:
            refined_optimal_price = opt_p
            refined_max_profit = opt_prof
    
    # 计算最优价格下的最终需求
    if model_type == 'constant':
        opt_d = calculate_demand_constant_elasticity(refined_optimal_price, base_price, base_demand, elasticity)
    else:
        opt_d = calculate_demand_linear(refined_optimal_price, base_price, base_demand, elasticity)

    return {
        'results': results,
        'optimal': {
            'price': round(refined_optimal_price, 2),
            'profit': round(refined_max_profit, 2),
            'demand': round(opt_d, 2),
            'margin': round(((refined_optimal_price - cost) / refined_optimal_price) * 100, 2) if refined_optimal_price > 0 else 0
        }
    }

@app.route('/api/analyze', methods=['POST'])
def analyze():
    try:
        data = request.json
        if not data:
            return jsonify({'success': False, 'error': '未提供数据'}), 400

        # 获取参数，提供默认值
        cost = float(data.get('cost', 10.0))
        base_price = float(data.get('base_price', 20.0))
        base_demand = float(data.get('base_demand', 100.0))
        elasticity = float(data.get('elasticity', -1.5))
        model_type = data.get('model_type', 'linear')
        
        result = run_optimization(cost, base_price, base_demand, elasticity, model_type)
        
        return jsonify({
            'success': True,
            'data': result['results'],
            'optimal': result['optimal']
        })

    except Exception as e:
        logger.error(f"Analysis Error: {str(e)}")
        return jsonify({'success': False, 'error': str(e)}), 500

@app.route('/api/upload', methods=['POST'])
def upload_file():
    """
    处理 CSV/Excel 文件上传并批量分析
    """
    try:
        if 'file' not in request.files:
            return jsonify({'success': False, 'error': '未找到上传文件'}), 400
        
        file = request.files['file']
        if file.filename == '':
            return jsonify({'success': False, 'error': '未选择文件'}), 400
            
        if file:
            filename = secure_filename(file.filename)
            filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
            file.save(filepath)
            
            # 读取文件
            try:
                if filename.endswith('.csv'):
                    df = pd.read_csv(filepath)
                elif filename.endswith(('.xls', '.xlsx')):
                    df = pd.read_excel(filepath)
                else:
                    return jsonify({'success': False, 'error': '不支持的文件格式 (仅支持 .csv, .xls, .xlsx)'}), 400
            except Exception as e:
                return jsonify({'success': False, 'error': f'文件读取错误: {str(e)}'}), 400

            # 检查必需列
            required_cols = ['cost', 'base_price', 'base_demand']
            missing_cols = [col for col in required_cols if col not in df.columns]
            if missing_cols:
                 return jsonify({'success': False, 'error': f'Missing columns: {", ".join(missing_cols)}'}), 400
            
            # 批量处理
            results = []
            for _, row in df.iterrows():
                try:
                    cost = float(row['cost'])
                    base_price = float(row['base_price'])
                    base_demand = float(row['base_demand'])
                    elasticity = float(row.get('elasticity', -1.5))
                    model_type = str(row.get('model_type', 'linear'))
                    
                    opt_res = run_optimization(cost, base_price, base_demand, elasticity, model_type)
                    
                    item_res = {
                        'input': {
                            'cost': cost,
                            'base_price': base_price,
                            'base_demand': base_demand,
                            'elasticity': elasticity
                        },
                        'optimal': opt_res['optimal']
                    }
                    results.append(item_res)
                except Exception as row_err:
                    logger.warning(f"Row processing error: {row_err}")
                    continue
            
            # 清理文件
            try:
                os.remove(filepath)
            except:
                pass

            return jsonify({
                'success': True, 
                'batch_results': results[:50] # 限制返回数量防止过大
            })

    except Exception as e:
        logger.error(f"Upload Error: {str(e)}")
        return jsonify({'success': False, 'error': f"服务器内部错误: {str(e)}"}), 500

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