inference.py

import torch
import json
import os
import csv
from datetime import datetime
import pandas as pd
import numpy as np

from nets.model import Model
from Actor.actor import Actor

# --- إعداد المسارات ---
safe_data_dir = "/home/user/data"
orders_file = os.path.join(safe_data_dir, "orders.csv")
params_path = os.path.join(safe_data_dir, 'params_saved.json')
model_path = os.path.join(safe_data_dir, "model_state_dict.pt")
txt_results_file = os.path.join(safe_data_dir, "inference_results.txt")
csv_results_file = os.path.join(safe_data_dir, "inference_results.csv")

# --- التحقق من الملفات ---
if not os.path.exists(params_path):
    raise FileNotFoundError(f"Settings file not found at {params_path}")
if not os.path.exists(model_path):
    raise FileNotFoundError(f"Model not found at {model_path}")
if not os.path.exists(orders_file):
    raise FileNotFoundError(f"orders.csv not found at {orders_file}")

# --- تحميل الإعدادات ---
with open(params_path, 'r') as f:
    params = json.load(f)

device = params['device']

# --- تحميل النموذج بعد ضبط input_size = 4 ---
model = Model(
    input_size = 4,
    embedding_size=params["embedding_size"],
    decoder_input_size=params["decoder_input_size"]
)
model.load_state_dict(torch.load(model_path, map_location=device))
model.eval()

# --- قراءة بيانات الطلبات ---
df = pd.read_csv(orders_file)

# --- استخراج الإحداثيات ---
pickup_lng = df['lng'].to_numpy()
pickup_lat = df['lat'].to_numpy()
delivery_lng = df['delivery_gps_lng'].to_numpy()
delivery_lat = df['delivery_gps_lat'].to_numpy()

coords = np.stack([pickup_lng, pickup_lat, delivery_lng, delivery_lat], axis=1)
coords_tensor = torch.tensor(coords, dtype=torch.float32).unsqueeze(0).to(device)

# --- تجهيز Batch كـ Tuple لتجنب unpacking error في Actor ---
graph_data = {"coords": coords_tensor}
fleet_data = {"dummy": torch.tensor([0])}  # يمكن تعديل هذا لاحقاً عند الحاجة
batch = (graph_data, fleet_data)

# --- تهيئة الممثل والـ NN ---
actor = Actor(
    model=model,
    num_movers=params['num_movers'],
    num_neighbors_encoder=params['num_neighbors_encoder'],
    num_neighbors_action=params['num_neighbors_action'],
    device=device,
    normalize=False
)

nn_actor = Actor(model=None, num_movers=1, num_neighbors_action=1, device=device)
nn_actor.nearest_neighbors()

# --- تنفيذ الاستدلال ---
with torch.no_grad():
    actor.greedy_search()
    actor_output = actor(batch)
    total_time = actor_output['total_time'].item()

    nn_output = nn_actor(batch)
    nn_time = nn_output['total_time'].item()

    improvement = (nn_time - total_time) / nn_time * 100
    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")

    # --- عرض ملخص الإدخالات ---
    coords_preview = "\n".join([
        f"Order {i}: P=({lng1:.4f},{lat1:.4f}) → D=({lng2:.4f},{lat2:.4f})"
        for i, (lng1, lat1, lng2, lat2) in enumerate(coords[:5])
    ])
    if coords.shape[0] > 5:
        coords_preview += f"\n... (showing 5 of {coords.shape[0]} orders)"

    input_summary = f"📌 Input Orders Preview:\n{coords_preview}"

    # --- نتيجة مفصلة للطباعة ---
    result_text = (
        "\n===== INFERENCE RESULT =====\n"
        f"Time: {timestamp}\n"
        f"Actor Model Total Cost: {total_time:.4f} units\n"
        f"Nearest Neighbor Cost : {nn_time:.4f} units\n"
        f"Improvement over NN    : {improvement:.2f}%\n"
    )
    print(result_text)

    # --- ملخص للواجهة ---
    summary_text = (
        f"🕒 Time: {timestamp}\n"
        f"🚚 Actor Cost: {total_time:.4f} units\n"
        f"📍 NN Cost: {nn_time:.4f} units\n"
        f"📈 Improvement: {improvement:.2f}%\n\n"
        f"{input_summary}"
    )
    print(f"\n🔍 Summary for UI:\n{summary_text}")

    # --- حفظ النتائج CSV ---
    write_header = not os.path.exists(csv_results_file)
    with open(csv_results_file, 'a', newline='') as csvfile:
        writer = csv.writer(csvfile)
        if write_header:
            writer.writerow(["Timestamp", "Actor Cost", "NN Cost", "Improvement (%)"])
        writer.writerow([timestamp, f"{total_time:.4f}", f"{nn_time:.4f}", f"{improvement:.2f}"])

    # --- حفظ نصي ---
    with open(txt_results_file, 'a') as f:
        f.write(result_text)
        f.write("\n=============================\n")