app.py

# app.py

import os
import json
import traceback
import torch
import gradio as gr
import numpy as np
from PIL import Image
import cv2
import math
import logging

# ==============================================================================
#           1. إعداد الاستيرادات والإعدادات الأساسية
# ==============================================================================

# إعداد بسيط لعرض الرسائل الإعلامية والأخطاء
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

# --- استيراد من وحدات المشروع المنظمة ---
try:
    from model_definition import create_model_config, load_and_prepare_model
except ImportError:
    raise ImportError("فشل استيراد من 'model.architecture'. تأكد من وجود الملف وأن مسار بايثون صحيح.")

try:
    from simulation_modules import (
        InterfuserController, ControllerConfig, DisplayInterface,
        render, render_waypoints, render_self_car, ensure_rgb,
        WAYPOINT_SCALE_FACTOR, T1_FUTURE_TIME, T2_FUTURE_TIME,
        transform, lidar_transform, Tracker # تأكد من وجود transform هنا
    )
except ImportError:
     raise ImportError("فشل استيراد من 'simulation_modules'. تأكد من وجود الملف وأن مسار بايثون صحيح.")

# --- إعدادات ومسارات النماذج ---
WEIGHTS_DIR = "model"
EXAMPLES_DIR = "examples"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

def find_available_models():
    if not os.path.isdir(WEIGHTS_DIR): return []
    return [f.replace(".pth", "") for f in os.listdir(WEIGHTS_DIR) if f.endswith(".pth")]

# ==============================================================================
#           2. الدوال الأساسية (load_model, run_single_frame)
# ==============================================================================

def load_model(model_name: str):
    """
    (نسخة مبسطة)
    تستخدم الآن الدوال المساعدة الجديدة لإنشاء وتحميل النموذج.
    """
    if not model_name or "لم يتم" in model_name:
        return None, "الرجاء اختيار نموذج صالح."

    weights_path = os.path.join(WEIGHTS_DIR, f"{model_name}.pth")
    
    # 1. إنشاء إعدادات النموذج المتوافقة مع الأوزان
    config = create_model_config(model_path=weights_path)
    
    # 2. إنشاء وتحميل النموذج بخطوة واحدة
    try:
        model = load_and_prepare_model(config, device)
        status_message = f"تم تحميل نموذج: {model_name}"
        if model is None: raise RuntimeError("فشلت دالة load_and_prepare_model")
    except Exception as e:
        model = None
        status_message = f"فشل تحميل النموذج: {e}"
        logging.error(traceback.format_exc())
        
    return model, status_message


def run_single_frame(
    model_from_state, rgb_image_path, rgb_left_image_path, rgb_right_image_path,
    rgb_center_image_path, lidar_image_path, measurements_path, target_point_list
):
    """
    تعتمد الآن على الوحدات المستوردة بشكل كامل.
    """
    if model_from_state is None:
        print("API session detected or model not loaded. Loading default model...")
        available_models = find_available_models()
        if not available_models: raise gr.Error("لا توجد نماذج متاحة للتحميل.")
        model_to_use, _ = load_model(available_models[0])
    else:
        model_to_use = model_from_state
    
    if model_to_use is None:
        raise gr.Error("فشل تحميل النموذج. تحقق من السجلات (Logs).")

    try:
        if not (rgb_image_path and measurements_path):
            raise gr.Error("الرجاء توفير الصورة الأمامية وملف القياسات على الأقل.")
        
        # --- 1. قراءة ومعالجة المدخلات ---
        rgb_image_pil = Image.open(rgb_image_path).convert("RGB")
        rgb_left_pil = Image.open(rgb_left_image_path).convert("RGB") if rgb_left_image_path else rgb_image_pil
        rgb_right_pil = Image.open(rgb_right_image_path).convert("RGB") if rgb_right_image_path else rgb_image_pil
        rgb_center_pil = Image.open(rgb_center_image_path).convert("RGB") if rgb_center_image_path else rgb_image_pil
        
        front_tensor = transform(rgb_image_pil).unsqueeze(0).to(device)
        left_tensor = transform(rgb_left_pil).unsqueeze(0).to(device)
        right_tensor = transform(rgb_right_pil).unsqueeze(0).to(device)
        center_tensor = transform(rgb_center_pil).unsqueeze(0).to(device)

        if lidar_image_path:
            lidar_array = np.load(lidar_image_path)
            if lidar_array.max() > 0: lidar_array = (lidar_array / lidar_array.max()) * 255.0
            lidar_pil = Image.fromarray(lidar_array.astype(np.uint8)).convert('RGB')
        else:
            lidar_pil = Image.fromarray(np.zeros((112, 112, 3), dtype=np.uint8))
        lidar_tensor = lidar_transform(lidar_pil).unsqueeze(0).to(device)

        with open(measurements_path, 'r') as f: m_dict = json.load(f)

        measurements_tensor = torch.tensor([[
            m_dict.get('x',0.0), m_dict.get('y',0.0), m_dict.get('theta',0.0), m_dict.get('speed',5.0),
            m_dict.get('steer',0.0), m_dict.get('throttle',0.0), float(m_dict.get('brake',0.0)),
            m_dict.get('command',2.0), float(m_dict.get('is_junction',0.0)), float(m_dict.get('should_brake',0.0))
        ]], dtype=torch.float32).to(device)
        
        target_point_tensor = torch.tensor([target_point_list], dtype=torch.float32).to(device)
        
        inputs = {'rgb': front_tensor, 'rgb_left': left_tensor, 'rgb_right': right_tensor, 'rgb_center': center_tensor, 'lidar': lidar_tensor, 'measurements': measurements_tensor, 'target_point': target_point_tensor}

        # --- 2. تشغيل النموذج ---
        with torch.no_grad():
            outputs = model_to_use(inputs)
            traffic, waypoints, is_junction, traffic_light, stop_sign, _ = outputs
        
        # --- 3. المعالجة اللاحقة والتصوّر ---
        speed = m_dict.get('speed', 5.0)
        controller = InterfuserController(ControllerConfig())
        steer, throttle, brake, metadata_str = controller.run_step(speed, waypoints, is_junction.sigmoid()[0,1].item(), traffic_light.sigmoid()[0,0].item(), stop_sign.sigmoid()[0,1].item(), {})
        
        map_t0, _ = render(traffic[0])
        map_t1, _ = render(traffic[0], t=T1_FUTURE_TIME)
        map_t2, _ = render(traffic[0], t=T2_FUTURE_TIME)
        wp_map = render_waypoints(waypoints[0])
        map_t0 = cv2.add(map_t0, wp_map)
        map_t0 = render_self_car(map_t0)
        
        display = DisplayInterface()
        interface_data = {'camera_view': np.array(rgb_image_pil),'map_t0': map_t0,'map_t1': map_t1,'map_t2': map_t2,
            'text_info': {'Control': f"S:{steer:.2f} T:{throttle:.2f} B:{int(brake)}", 'Metadata': metadata_str}}
        dashboard_image = display.run_interface(interface_data)
        
        # --- 4. تجهيز المخرجات ---
        control_commands_dict = {"steer": steer, "throttle": throttle, "brake": bool(brake)}
        return Image.fromarray(dashboard_image), control_commands_dict
    
    except Exception as e:
        logging.error(traceback.format_exc())
        raise gr.Error(f"حدث خطأ أثناء معالجة الإطار: {e}")

# ==============================================================================
#           3. تعريف واجهة Gradio (لا تغيير هنا)
# ==============================================================================
available_models = find_available_models()

with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky"), css=".gradio-container {max-width: 95% !important;}") as demo:
    model_state = gr.State(value=None)
    
    gr.Markdown("# 🚗 محاكاة القيادة الذاتية باستخدام Interfuser")
    gr.Markdown("مرحباً بك في واجهة اختبار نموذج Interfuser.")

    with gr.Row():
        with gr.Column(scale=1):
            with gr.Group():
                gr.Markdown("## ⚙️ الخطوة 1: اختر النموذج")
                with gr.Row():
                    model_selector = gr.Dropdown(label="النماذج المتاحة", choices=available_models, value=available_models[0] if available_models else "لم يتم العثور على نماذج")
                    status_textbox = gr.Textbox(label="حالة النموذج", interactive=False)
            
            with gr.Group():
                gr.Markdown("## 🗂️ الخطوة 2: ارفع ملفات السيناريو")
                with gr.Group():
                    gr.Markdown("**(مطلوب)**")
                    api_rgb_image_path = gr.File(label="صورة الكاميرا الأمامية (RGB)", type="filepath")
                    api_measurements_path = gr.File(label="ملف القياسات (JSON)", type="filepath")
                with gr.Accordion("📷 مدخلات اختيارية", open=False):
                    api_rgb_left_image_path = gr.File(label="كاميرا اليسار (RGB)", type="filepath")
                    api_rgb_right_image_path = gr.File(label="كاميرا اليمين (RGB)", type="filepath")
                    api_rgb_center_image_path = gr.File(label="كاميرا الوسط (RGB)", type="filepath")
                    api_lidar_image_path = gr.File(label="بيانات الليدار (NPY)", type="filepath")
                api_target_point_list = gr.JSON(label="📍 النقطة المستهدفة (x, y)", value=[0.0, 100.0])
                api_run_button = gr.Button("🚀 شغل المحاكاة", variant="primary", scale=2)

            with gr.Group():
                gr.Markdown("### ✨ أمثلة جاهزة")
                gr.Examples(
                  examples=[
                        [os.path.join(EXAMPLES_DIR, "sample1", "rgb.jpg"), os.path.join(EXAMPLES_DIR, "sample1", "measurements.json")],
                        [os.path.join(EXAMPLES_DIR, "sample2", "rgb.jpg"), os.path.join(EXAMPLES_DIR, "sample1", "measurements.json")]
                    ],
                    inputs=[api_rgb_image_path, api_measurements_path], label="اختر سيناريو اختبار")

        with gr.Column(scale=2):
            with gr.Group():
                gr.Markdown("## 📊 الخطوة 3: شاهد النتائج")
                api_output_image = gr.Image(label="لوحة التحكم المرئية (Dashboard)", type="pil", interactive=False)
                api_control_json = gr.JSON(label="أوامر التحكم (JSON)")

    if available_models:
        demo.load(fn=load_model, inputs=model_selector, outputs=[model_state, status_textbox])
    model_selector.change(fn=load_model, inputs=model_selector, outputs=[model_state, status_textbox])
    
    api_run_button.click(
        fn=run_single_frame,
        inputs=[model_state, api_rgb_image_path, api_rgb_left_image_path, api_rgb_right_image_path, 
                api_rgb_center_image_path, api_lidar_image_path, api_measurements_path, api_target_point_list],
        outputs=[api_output_image, api_control_json],
        api_name="run_single_frame"
    )

# ==============================================================================
#           4. تشغيل التطبيق
# ==============================================================================
if __name__ == "__main__":
    if not available_models:
        logging.warning("لم يتم العثور على أي ملفات نماذج (.pth) في مجلد 'model/weights'.")
    demo.queue().launch(debug=True, share=True)