import os
import random
import simpy
import numpy as np
from flask import Flask, render_template, jsonify, request, send_from_directory

app = Flask(__name__, template_folder='templates')

# Configuration
MAX_SIM_TIME = 480  # 8 hours in minutes for analysis
SEED = 42

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

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

@app.errorhandler(500)
def internal_server_error(e):
    return jsonify(error="Internal Server Error", message=str(e)), 500

@app.route('/api/upload_config', methods=['POST'])
def upload_config():
    try:
        if 'file' not in request.files:
            return jsonify({'error': 'No file part'}), 400
        file = request.files['file']
        if file.filename == '':
            return jsonify({'error': 'No selected file'}), 400
        if file:
            import json
            content = json.load(file)
            return jsonify({'message': 'Config uploaded successfully', 'config': content})
    except Exception as e:
        return jsonify({'error': str(e)}), 500


class CallCenter:
    def __init__(self, env, num_employees, service_time_avg, service_time_std):
        self.env = env
        self.staff = simpy.Resource(env, num_employees)
        self.service_time_avg = service_time_avg
        self.service_time_std = service_time_std
        self.wait_times = []
        self.service_times = []
        self.utilization_log = []
        self.events_trace = [] # For visualization: {'time': t, 'type': '...', 'id': ...}

    def support(self, customer):
        arrival_time = self.env.now
        self.events_trace.append({'time': arrival_time, 'type': 'arrival', 'id': customer})
        
        with self.staff.request() as request:
            yield request
            
            wait = self.env.now - arrival_time
            self.wait_times.append(wait)
            self.events_trace.append({'time': self.env.now, 'type': 'start', 'id': customer, 'wait': wait})
            
            # Service time (Normal distribution, clipped at 0.5 min)
            service_duration = max(0.5, random.gauss(self.service_time_avg, self.service_time_std))
            yield self.env.timeout(service_duration)
            
            self.service_times.append(service_duration)
            self.events_trace.append({'time': self.env.now, 'type': 'finish', 'id': customer})

def customer_generator(env, center, arrival_rate):
    """
    arrival_rate: Customers per hour
    """
    i = 0
    while True:
        # Inter-arrival time (Exponential distribution)
        yield env.timeout(random.expovariate(arrival_rate / 60.0))
        i += 1
        env.process(center.support(f'C{i}'))

@app.route('/api/simulate', methods=['POST'])
def simulate():
    data = request.json
    
    # Parameters
    arrival_rate = float(data.get('arrival_rate', 60)) # Cust/hr
    service_time = float(data.get('service_time', 5))  # Avg min
    service_std = float(data.get('service_std', 1))    # Std Dev min
    num_servers = int(data.get('num_servers', 3))
    duration = float(data.get('duration', 60))         # Minutes to simulate
    
    random.seed(SEED)
    env = simpy.Environment()
    center = CallCenter(env, num_servers, service_time, service_std)
    env.process(customer_generator(env, center, arrival_rate))
    env.run(until=duration)
    
    # Calculate stats
    avg_wait = np.mean(center.wait_times) if center.wait_times else 0
    max_wait = np.max(center.wait_times) if center.wait_times else 0
    served_count = len(center.service_times)
    
    # Utilization estimate (Total Service Time / (Num Servers * Duration))
    total_service = sum(center.service_times)
    utilization = (total_service / (num_servers * duration)) * 100 if num_servers > 0 else 0
    
    return jsonify({
        'metrics': {
            'avg_wait': round(avg_wait, 2),
            'max_wait': round(max_wait, 2),
            'served': served_count,
            'utilization': round(utilization, 2)
        },
        'trace': center.events_trace
    })

@app.route('/api/optimize', methods=['POST'])
def optimize():
    data = request.json
    
    arrival_rate = float(data.get('arrival_rate', 60))
    service_time = float(data.get('service_time', 5))
    service_std = float(data.get('service_std', 1))
    
    cost_per_server_hr = float(data.get('cost_server', 20)) # $20/hr
    cost_per_wait_hr = float(data.get('cost_wait', 50))     # $50/hr (Value of customer time/frustration)
    
    results = []
    
    # Test 1 to 15 servers
    for n in range(1, 16):
        random.seed(SEED) # Reset seed for fair comparison
        env = simpy.Environment()
        center = CallCenter(env, n, service_time, service_std)
        env.process(customer_generator(env, center, arrival_rate))
        env.run(until=480) # 8 hours
        
        avg_wait_min = np.mean(center.wait_times) if center.wait_times else 0
        total_wait_hours = sum(center.wait_times) / 60.0
        
        # Total Cost = (Server Cost * 8h) + (Total Wait Hours * Wait Cost)
        server_cost = n * cost_per_server_hr * 8
        wait_cost = total_wait_hours * cost_per_wait_hr
        total_cost = server_cost + wait_cost
        
        utilization = (sum(center.service_times) / (n * 480)) * 100
        
        results.append({
            'servers': n,
            'total_cost': round(total_cost, 2),
            'server_cost': round(server_cost, 2),
            'wait_cost': round(wait_cost, 2),
            'avg_wait': round(avg_wait_min, 2),
            'utilization': round(utilization, 1)
        })
        
        # Optimization: if cost starts increasing significantly and wait is low, we can stop, 
        # but let's run all 15 for the chart.
        
    return jsonify({'results': results})

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=7860, debug=True)