Upload app.py with huggingface_hub

app.py

@@ -1,1865 +1,48 @@
-import traceback
-import torch
-from torch import nn
-import torch.nn.functional as F
-from transformers import PreTrainedModel, PretrainedConfig
-from transformers.utils.generic import ModelOutput
-from functools import partial
-import math
-import copy
-from typing import Optional, Tuple, Union, List
-from torch import Tensor
-from dataclasses import dataclass
-import numpy as np
-from timm.models.resnet import resnet50d,resnet101d, resnet26d, resnet18d
-from torch.utils.data import DataLoader, Dataset
-from collections import deque, OrderedDict
+# app.py
+
 import os
 import json
-import cv2
-from pathlib import Path
-from torchvision import transforms
-from torch.utils.data import random_split
-from timm.models.registry import register_model
+import traceback
+import torch
 import gradio as gr
-import zipfile
-import tempfile
-import shutil
-import tarfile
-import gdown
-import time 
-from huggingface_hub import hf_hub_download # طريقة أفضل للتنزيل من Hub
-import requests # <-- هذا هو السطر الذي يجب إضافته
+import numpy as np
 from PIL import Image
+import cv2
+import math
 
-
-class HybridEmbed(nn.Module):
-    def __init__(
-        self,
-        backbone,
-        img_size=224,
-        patch_size=1,
-        feature_size=None,
-        in_chans=3,
-        embed_dim=768,
-    ):
-        super().__init__()
-        assert isinstance(backbone, nn.Module)
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.backbone = backbone
-        if feature_size is None:
-            with torch.no_grad():
-                training = backbone.training
-                if training:
-                    backbone.eval()
-                o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1]))
-                if isinstance(o, (list, tuple)):
-                    o = o[-1]  # last feature if backbone outputs list/tuple of features
-                feature_size = o.shape[-2:]
-                feature_dim = o.shape[1]
-                backbone.train(training)
-        else:
-            feature_size = to_2tuple(feature_size)
-            if hasattr(self.backbone, "feature_info"):
-                feature_dim = self.backbone.feature_info.channels()[-1]
-            else:
-                feature_dim = self.backbone.num_features
-
-        self.proj = nn.Conv2d(feature_dim, embed_dim, kernel_size=1, stride=1)
-
-    def forward(self, x):
-        x = self.backbone(x)
-        if isinstance(x, (list, tuple)):
-            x = x[-1]  # last feature if backbone outputs list/tuple of features
-        x = self.proj(x)
-        global_x = torch.mean(x, [2, 3], keepdim=False)[:, :, None]
-        return x, global_x
-
-
-class PositionEmbeddingSine(nn.Module):
-    """
-    This is a more standard version of the position embedding, very similar to the one
-    used by the Attention is all you need paper, generalized to work on images.
-    """
-
-    def __init__(
-        self, num_pos_feats=64, temperature=10000, normalize=False, scale=None
-    ):
-        super().__init__()
-        self.num_pos_feats = num_pos_feats
-        self.temperature = temperature
-        self.normalize = normalize
-        if scale is not None and normalize is False:
-            raise ValueError("normalize should be True if scale is passed")
-        if scale is None:
-            scale = 2 * math.pi
-        self.scale = scale
-
-    def forward(self, tensor):
-        x = tensor
-        bs, _, h, w = x.shape
-        not_mask = torch.ones((bs, h, w), device=x.device)
-        y_embed = not_mask.cumsum(1, dtype=torch.float32)
-        x_embed = not_mask.cumsum(2, dtype=torch.float32)
-        if self.normalize:
-            eps = 1e-6
-            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
-            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
-
-        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
-        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
-
-        pos_x = x_embed[:, :, :, None] / dim_t
-        pos_y = y_embed[:, :, :, None] / dim_t
-        pos_x = torch.stack(
-            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
-        ).flatten(3)
-        pos_y = torch.stack(
-            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
-        ).flatten(3)
-        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
-        return pos
-
-
-class TransformerEncoder(nn.Module):
-    def __init__(self, encoder_layer, num_layers, norm=None):
-        super().__init__()
-        self.layers = _get_clones(encoder_layer, num_layers)
-        self.num_layers = num_layers
-        self.norm = norm
-
-    def forward(
-        self,
-        src,
-        mask: Optional[Tensor] = None,
-        src_key_padding_mask: Optional[Tensor] = None,
-        pos: Optional[Tensor] = None,
-    ):
-        output = src
-
-        for layer in self.layers:
-            output = layer(
-                output,
-                src_mask=mask,
-                src_key_padding_mask=src_key_padding_mask,
-                pos=pos,
-            )
-
-        if self.norm is not None:
-            output = self.norm(output)
-
-        return output
-
-
-class SpatialSoftmax(nn.Module):
-    def __init__(self, height, width, channel, temperature=None, data_format="NCHW"):
-        super().__init__()
-
-        self.data_format = data_format
-        self.height = height
-        self.width = width
-        self.channel = channel
-
-        if temperature:
-            self.temperature = nn.Parameter(torch.ones(1) * temperature)
-        else:
-            self.temperature = 1.0
-
-        pos_x, pos_y = np.meshgrid(
-            np.linspace(-1.0, 1.0, self.height), np.linspace(-1.0, 1.0, self.width)
-        )
-        pos_x = torch.from_numpy(pos_x.reshape(self.height * self.width)).float()
-        pos_y = torch.from_numpy(pos_y.reshape(self.height * self.width)).float()
-        self.register_buffer("pos_x", pos_x)
-        self.register_buffer("pos_y", pos_y)
-
-    def forward(self, feature):
-        # Output:
-        #   (N, C*2) x_0 y_0 ...
-
-        if self.data_format == "NHWC":
-            feature = (
-                feature.transpose(1, 3)
-                .tranpose(2, 3)
-                .view(-1, self.height * self.width)
-            )
-        else:
-            feature = feature.view(-1, self.height * self.width)
-
-        weight = F.softmax(feature / self.temperature, dim=-1)
-        expected_x = torch.sum(
-            torch.autograd.Variable(self.pos_x) * weight, dim=1, keepdim=True
-        )
-        expected_y = torch.sum(
-            torch.autograd.Variable(self.pos_y) * weight, dim=1, keepdim=True
-        )
-        expected_xy = torch.cat([expected_x, expected_y], 1)
-        feature_keypoints = expected_xy.view(-1, self.channel, 2)
-        feature_keypoints[:, :, 1] = (feature_keypoints[:, :, 1] - 1) * 12
-        feature_keypoints[:, :, 0] = feature_keypoints[:, :, 0] * 12
-        return feature_keypoints
-
-
-class MultiPath_Generator(nn.Module):
-    def __init__(self, in_channel, embed_dim, out_channel):
-        super().__init__()
-        self.spatial_softmax = SpatialSoftmax(100, 100, out_channel)
-        self.tconv0 = nn.Sequential(
-            nn.ConvTranspose2d(in_channel, 256, 4, 2, 1, bias=False),
-            nn.BatchNorm2d(256),
-            nn.ReLU(True),
-        )
-        self.tconv1 = nn.Sequential(
-            nn.ConvTranspose2d(256, 256, 4, 2, 1, bias=False),
-            nn.BatchNorm2d(256),
-            nn.ReLU(True),
-        )
-        self.tconv2 = nn.Sequential(
-            nn.ConvTranspose2d(256, 192, 4, 2, 1, bias=False),
-            nn.BatchNorm2d(192),
-            nn.ReLU(True),
-        )
-        self.tconv3 = nn.Sequential(
-            nn.ConvTranspose2d(192, 64, 4, 2, 1, bias=False),
-            nn.BatchNorm2d(64),
-            nn.ReLU(True),
-        )
-        self.tconv4_list = torch.nn.ModuleList(
-            [
-                nn.Sequential(
-                    nn.ConvTranspose2d(64, out_channel, 8, 2, 3, bias=False),
-                    nn.Tanh(),
-                )
-                for _ in range(6)
-            ]
-        )
-
-        self.upsample = nn.Upsample(size=(50, 50), mode="bilinear")
-
-    def forward(self, x, measurements):
-        mask = measurements[:, :6]
-        mask = mask.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).repeat(1, 1, 1, 100, 100)
-        velocity = measurements[:, 6:7].unsqueeze(-1).unsqueeze(-1)
-        velocity = velocity.repeat(1, 32, 2, 2)
-
-        n, d, c = x.shape
-        x = x.transpose(1, 2)
-        x = x.view(n, -1, 2, 2)
-        x = torch.cat([x, velocity], dim=1)
-        x = self.tconv0(x)
-        x = self.tconv1(x)
-        x = self.tconv2(x)
-        x = self.tconv3(x)
-        x = self.upsample(x)
-        xs = []
-        for i in range(6):
-            xt = self.tconv4_list[i](x)
-            xs.append(xt)
-        xs = torch.stack(xs, dim=1)
-        x = torch.sum(xs * mask, dim=1)
-        x = self.spatial_softmax(x)
-        return x
-
-
-class LinearWaypointsPredictor(nn.Module):
-    def __init__(self, input_dim, cumsum=True):
-        super().__init__()
-        self.cumsum = cumsum
-        self.rank_embed = nn.Parameter(torch.zeros(1, 10, input_dim))
-        self.head_fc1_list = nn.ModuleList([nn.Linear(input_dim, 64) for _ in range(6)])
-        self.head_relu = nn.ReLU(inplace=True)
-        self.head_fc2_list = nn.ModuleList([nn.Linear(64, 2) for _ in range(6)])
-
-    def forward(self, x, measurements):
-        # input shape: n 10 embed_dim
-        bs, n, dim = x.shape
-        x = x + self.rank_embed
-        x = x.reshape(-1, dim)
-
-        mask = measurements[:, :6]
-        mask = torch.unsqueeze(mask, -1).repeat(n, 1, 2)
-
-        rs = []
-        for i in range(6):
-            res = self.head_fc1_list[i](x)
-            res = self.head_relu(res)
-            res = self.head_fc2_list[i](res)
-            rs.append(res)
-        rs = torch.stack(rs, 1)
-        x = torch.sum(rs * mask, dim=1)
-
-        x = x.view(bs, n, 2)
-        if self.cumsum:
-            x = torch.cumsum(x, 1)
-        return x
-
-
-class GRUWaypointsPredictor(nn.Module):
-    def __init__(self, input_dim, waypoints=10):
-        super().__init__()
-        # self.gru = torch.nn.GRUCell(input_size=input_dim, hidden_size=64)
-        self.gru = torch.nn.GRU(input_size=input_dim, hidden_size=64, batch_first=True)
-        self.encoder = nn.Linear(2, 64)
-        self.decoder = nn.Linear(64, 2)
-        self.waypoints = waypoints
-
-    def forward(self, x, target_point):
-        bs = x.shape[0]
-        z = self.encoder(target_point).unsqueeze(0)
-        output, _ = self.gru(x, z)
-        output = output.reshape(bs * self.waypoints, -1)
-        output = self.decoder(output).reshape(bs, self.waypoints, 2)
-        output = torch.cumsum(output, 1)
-        return output
-
-class GRUWaypointsPredictorWithCommand(nn.Module):
-    def __init__(self, input_dim, waypoints=10):
-        super().__init__()
-        # self.gru = torch.nn.GRUCell(input_size=input_dim, hidden_size=64)
-        self.grus = nn.ModuleList([torch.nn.GRU(input_size=input_dim, hidden_size=64, batch_first=True) for _ in range(6)])
-        self.encoder = nn.Linear(2, 64)
-        self.decoders = nn.ModuleList([nn.Linear(64, 2) for _ in range(6)])
-        self.waypoints = waypoints
-
-    def forward(self, x, target_point, measurements):
-        bs, n, dim = x.shape
-        mask = measurements[:, :6, None, None]
-        mask = mask.repeat(1, 1, self.waypoints, 2)
-
-        z = self.encoder(target_point).unsqueeze(0)
-        outputs = []
-        for i in range(6):
-            output, _ = self.grus[i](x, z)
-            output = output.reshape(bs * self.waypoints, -1)
-            output = self.decoders[i](output).reshape(bs, self.waypoints, 2)
-            output = torch.cumsum(output, 1)
-            outputs.append(output)
-        outputs = torch.stack(outputs, 1)
-        output = torch.sum(outputs * mask, dim=1)
-        return output
-
-
-class TransformerDecoder(nn.Module):
-    def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False):
-        super().__init__()
-        self.layers = _get_clones(decoder_layer, num_layers)
-        self.num_layers = num_layers
-        self.norm = norm
-        self.return_intermediate = return_intermediate
-
-    def forward(
-        self,
-        tgt,
-        memory,
-        tgt_mask: Optional[Tensor] = None,
-        memory_mask: Optional[Tensor] = None,
-        tgt_key_padding_mask: Optional[Tensor] = None,
-        memory_key_padding_mask: Optional[Tensor] = None,
-        pos: Optional[Tensor] = None,
-        query_pos: Optional[Tensor] = None,
-    ):
-        output = tgt
-
-        intermediate = []
-
-        for layer in self.layers:
-            output = layer(
-                output,
-                memory,
-                tgt_mask=tgt_mask,
-                memory_mask=memory_mask,
-                tgt_key_padding_mask=tgt_key_padding_mask,
-                memory_key_padding_mask=memory_key_padding_mask,
-                pos=pos,
-                query_pos=query_pos,
-            )
-            if self.return_intermediate:
-                intermediate.append(self.norm(output))
-
-        if self.norm is not None:
-            output = self.norm(output)
-            if self.return_intermediate:
-                intermediate.pop()
-                intermediate.append(output)
-
-        if self.return_intermediate:
-            return torch.stack(intermediate)
-
-        return output.unsqueeze(0)
-
-
-class TransformerEncoderLayer(nn.Module):
-    def __init__(
-        self,
-        d_model,
-        nhead,
-        dim_feedforward=2048,
-        dropout=0.1,
-        activation=nn.ReLU(),
-        normalize_before=False,
-    ):
-        super().__init__()
-        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
-        # Implementation of Feedforward model
-        self.linear1 = nn.Linear(d_model, dim_feedforward)
-        self.dropout = nn.Dropout(dropout)
-        self.linear2 = nn.Linear(dim_feedforward, d_model)
-
-        self.norm1 = nn.LayerNorm(d_model)
-        self.norm2 = nn.LayerNorm(d_model)
-        self.dropout1 = nn.Dropout(dropout)
-        self.dropout2 = nn.Dropout(dropout)
-
-        self.activation = activation()
-        self.normalize_before = normalize_before
-
-    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
-        return tensor if pos is None else tensor + pos
-
-    def forward_post(
-        self,
-        src,
-        src_mask: Optional[Tensor] = None,
-        src_key_padding_mask: Optional[Tensor] = None,
-        pos: Optional[Tensor] = None,
-    ):
-        q = k = self.with_pos_embed(src, pos)
-        src2 = self.self_attn(
-            q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask
-        )[0]
-        src = src + self.dropout1(src2)
-        src = self.norm1(src)
-        src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
-        src = src + self.dropout2(src2)
-        src = self.norm2(src)
-        return src
-
-    def forward_pre(
-        self,
-        src,
-        src_mask: Optional[Tensor] = None,
-        src_key_padding_mask: Optional[Tensor] = None,
-        pos: Optional[Tensor] = None,
-    ):
-        src2 = self.norm1(src)
-        q = k = self.with_pos_embed(src2, pos)
-        src2 = self.self_attn(
-            q, k, value=src2, attn_mask=src_mask, key_padding_mask=src_key_padding_mask
-        )[0]
-        src = src + self.dropout1(src2)
-        src2 = self.norm2(src)
-        src2 = self.linear2(self.dropout(self.activation(self.linear1(src2))))
-        src = src + self.dropout2(src2)
-        return src
-
-    def forward(
-        self,
-        src,
-        src_mask: Optional[Tensor] = None,
-        src_key_padding_mask: Optional[Tensor] = None,
-        pos: Optional[Tensor] = None,
-    ):
-        if self.normalize_before:
-            return self.forward_pre(src, src_mask, src_key_padding_mask, pos)
-        return self.forward_post(src, src_mask, src_key_padding_mask, pos)
-
-
-class TransformerDecoderLayer(nn.Module):
-    def __init__(
-        self,
-        d_model,
-        nhead,
-        dim_feedforward=2048,
-        dropout=0.1,
-        activation=nn.ReLU(),
-        normalize_before=False,
-    ):
-        super().__init__()
-        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
-        self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
-        # Implementation of Feedforward model
-        self.linear1 = nn.Linear(d_model, dim_feedforward)
-        self.dropout = nn.Dropout(dropout)
-        self.linear2 = nn.Linear(dim_feedforward, d_model)
-
-        self.norm1 = nn.LayerNorm(d_model)
-        self.norm2 = nn.LayerNorm(d_model)
-        self.norm3 = nn.LayerNorm(d_model)
-        self.dropout1 = nn.Dropout(dropout)
-        self.dropout2 = nn.Dropout(dropout)
-        self.dropout3 = nn.Dropout(dropout)
-
-        self.activation = activation()
-        self.normalize_before = normalize_before
-
-    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
-        return tensor if pos is None else tensor + pos
-
-    def forward_post(
-        self,
-        tgt,
-        memory,
-        tgt_mask: Optional[Tensor] = None,
-        memory_mask: Optional[Tensor] = None,
-        tgt_key_padding_mask: Optional[Tensor] = None,
-        memory_key_padding_mask: Optional[Tensor] = None,
-        pos: Optional[Tensor] = None,
-        query_pos: Optional[Tensor] = None,
-    ):
-        q = k = self.with_pos_embed(tgt, query_pos)
-        tgt2 = self.self_attn(
-            q, k, value=tgt, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask
-        )[0]
-        tgt = tgt + self.dropout1(tgt2)
-        tgt = self.norm1(tgt)
-        tgt2 = self.multihead_attn(
-            query=self.with_pos_embed(tgt, query_pos),
-            key=self.with_pos_embed(memory, pos),
-            value=memory,
-            attn_mask=memory_mask,
-            key_padding_mask=memory_key_padding_mask,
-        )[0]
-        tgt = tgt + self.dropout2(tgt2)
-        tgt = self.norm2(tgt)
-        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
-        tgt = tgt + self.dropout3(tgt2)
-        tgt = self.norm3(tgt)
-        return tgt
-
-    def forward_pre(
-        self,
-        tgt,
-        memory,
-        tgt_mask: Optional[Tensor] = None,
-        memory_mask: Optional[Tensor] = None,
-        tgt_key_padding_mask: Optional[Tensor] = None,
-        memory_key_padding_mask: Optional[Tensor] = None,
-        pos: Optional[Tensor] = None,
-        query_pos: Optional[Tensor] = None,
-    ):
-        tgt2 = self.norm1(tgt)
-        q = k = self.with_pos_embed(tgt2, query_pos)
-        tgt2 = self.self_attn(
-            q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask
-        )[0]
-        tgt = tgt + self.dropout1(tgt2)
-        tgt2 = self.norm2(tgt)
-        tgt2 = self.multihead_attn(
-            query=self.with_pos_embed(tgt2, query_pos),
-            key=self.with_pos_embed(memory, pos),
-            value=memory,
-            attn_mask=memory_mask,
-            key_padding_mask=memory_key_padding_mask,
-        )[0]
-        tgt = tgt + self.dropout2(tgt2)
-        tgt2 = self.norm3(tgt)
-        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
-        tgt = tgt + self.dropout3(tgt2)
-        return tgt
-
-    def forward(
-        self,
-        tgt,
-        memory,
-        tgt_mask: Optional[Tensor] = None,
-        memory_mask: Optional[Tensor] = None,
-        tgt_key_padding_mask: Optional[Tensor] = None,
-        memory_key_padding_mask: Optional[Tensor] = None,
-        pos: Optional[Tensor] = None,
-        query_pos: Optional[Tensor] = None,
-    ):
-        if self.normalize_before:
-            return self.forward_pre(
-                tgt,
-                memory,
-                tgt_mask,
-                memory_mask,
-                tgt_key_padding_mask,
-                memory_key_padding_mask,
-                pos,
-                query_pos,
-            )
-        return self.forward_post(
-            tgt,
-            memory,
-            tgt_mask,
-            memory_mask,
-            tgt_key_padding_mask,
-            memory_key_padding_mask,
-            pos,
-            query_pos,
-        )
-
-
-class Interfuser(nn.Module):
-    def __init__(
-        self,
-        img_size=224,
-        multi_view_img_size=112,
-        patch_size=8,
-        in_chans=3,
-        embed_dim=768,
-        enc_depth=6,
-        dec_depth=6,
-        dim_feedforward=2048,
-        normalize_before=False,
-        rgb_backbone_name="r26",
-        lidar_backbone_name="r26",
-        num_heads=8,
-        norm_layer=None,
-        dropout=0.1,
-        end2end=False,
-        direct_concat=True,
-        separate_view_attention=False,
-        separate_all_attention=False,
-        act_layer=None,
-        weight_init="",
-        freeze_num=-1,
-        with_lidar=False,
-        with_right_left_sensors=True,
-        with_center_sensor=False,
-        traffic_pred_head_type="det",
-        waypoints_pred_head="heatmap",
-        reverse_pos=True,
-        use_different_backbone=False,
-        use_view_embed=True,
-        use_mmad_pretrain=None,
-    ):
-        super().__init__()
-        self.traffic_pred_head_type = traffic_pred_head_type
-        self.num_features = (
-            self.embed_dim
-        ) = embed_dim  # num_features for consistency with other models
-        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
-        act_layer = act_layer or nn.GELU
-
-        self.reverse_pos = reverse_pos
-        self.waypoints_pred_head = waypoints_pred_head
-        self.with_lidar = with_lidar
-        self.with_right_left_sensors = with_right_left_sensors
-        self.with_center_sensor = with_center_sensor
-
-        self.direct_concat = direct_concat
-        self.separate_view_attention = separate_view_attention
-        self.separate_all_attention = separate_all_attention
-        self.end2end = end2end
-        self.use_view_embed = use_view_embed
-
-        if self.direct_concat:
-            in_chans = in_chans * 4
-            self.with_center_sensor = False
-            self.with_right_left_sensors = False
-
-        if self.separate_view_attention:
-            self.attn_mask = build_attn_mask("seperate_view")
-        elif self.separate_all_attention:
-            self.attn_mask = build_attn_mask("seperate_all")
-        else:
-            self.attn_mask = None
-
-        if use_different_backbone:
-            if rgb_backbone_name == "r50":
-                self.rgb_backbone = resnet50d(
-                    pretrained=True,
-                    in_chans=in_chans,
-                    features_only=True,
-                    out_indices=[4],
-                )
-            elif rgb_backbone_name == "r26":
-                self.rgb_backbone = resnet26d(
-                    pretrained=True,
-                    in_chans=in_chans,
-                    features_only=True,
-                    out_indices=[4],
-                )
-            elif rgb_backbone_name == "r18":
-                self.rgb_backbone = resnet18d(
-                    pretrained=True,
-                    in_chans=in_chans,
-                    features_only=True,
-                    out_indices=[4],
-                )
-            if lidar_backbone_name == "r50":
-                self.lidar_backbone = resnet50d(
-                    pretrained=False,
-                    in_chans=in_chans,
-                    features_only=True,
-                    out_indices=[4],
-                )
-            elif lidar_backbone_name == "r26":
-                self.lidar_backbone = resnet26d(
-                    pretrained=False,
-                    in_chans=in_chans,
-                    features_only=True,
-                    out_indices=[4],
-                )
-            elif lidar_backbone_name == "r18":
-                self.lidar_backbone = resnet18d(
-                    pretrained=False, in_chans=3, features_only=True, out_indices=[4]
-                )
-            rgb_embed_layer = partial(HybridEmbed, backbone=self.rgb_backbone)
-            lidar_embed_layer = partial(HybridEmbed, backbone=self.lidar_backbone)
-
-            if use_mmad_pretrain:
-                params = torch.load(use_mmad_pretrain)["state_dict"]
-                updated_params = OrderedDict()
-                for key in params:
-                    if "backbone" in key:
-                        updated_params[key.replace("backbone.", "")] = params[key]
-                self.rgb_backbone.load_state_dict(updated_params)
-
-            self.rgb_patch_embed = rgb_embed_layer(
-                img_size=img_size,
-                patch_size=patch_size,
-                in_chans=in_chans,
-                embed_dim=embed_dim,
-            )
-            self.lidar_patch_embed = lidar_embed_layer(
-                img_size=img_size,
-                patch_size=patch_size,
-                in_chans=3,
-                embed_dim=embed_dim,
-            )
-        else:
-            if rgb_backbone_name == "r50":
-                self.rgb_backbone = resnet50d(
-                    pretrained=True, in_chans=3, features_only=True, out_indices=[4]
-                )
-            elif rgb_backbone_name == "r101":
-                self.rgb_backbone = resnet101d(
-                    pretrained=True, in_chans=3, features_only=True, out_indices=[4]
-                )
-            elif rgb_backbone_name == "r26":
-                self.rgb_backbone = resnet26d(
-                    pretrained=True, in_chans=3, features_only=True, out_indices=[4]
-                )
-            elif rgb_backbone_name == "r18":
-                self.rgb_backbone = resnet18d(
-                    pretrained=True, in_chans=3, features_only=True, out_indices=[4]
-                )
-            embed_layer = partial(HybridEmbed, backbone=self.rgb_backbone)
-
-            self.rgb_patch_embed = embed_layer(
-                img_size=img_size,
-                patch_size=patch_size,
-                in_chans=in_chans,
-                embed_dim=embed_dim,
-            )
-            self.lidar_patch_embed = embed_layer(
-                img_size=img_size,
-                patch_size=patch_size,
-                in_chans=in_chans,
-                embed_dim=embed_dim,
-            )
-
-        self.global_embed = nn.Parameter(torch.zeros(1, embed_dim, 5))
-        self.view_embed = nn.Parameter(torch.zeros(1, embed_dim, 5, 1))
-
-        if self.end2end:
-            self.query_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, 4))
-            self.query_embed = nn.Parameter(torch.zeros(4, 1, embed_dim))
-        elif self.waypoints_pred_head == "heatmap":
-            self.query_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, 5))
-            self.query_embed = nn.Parameter(torch.zeros(400 + 5, 1, embed_dim))
-        else:
-            self.query_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, 11))
-            self.query_embed = nn.Parameter(torch.zeros(400 + 11, 1, embed_dim))
-
-        if self.end2end:
-            self.waypoints_generator = GRUWaypointsPredictor(embed_dim, 4)
-        elif self.waypoints_pred_head == "heatmap":
-            self.waypoints_generator = MultiPath_Generator(
-                embed_dim + 32, embed_dim, 10
-            )
-        elif self.waypoints_pred_head == "gru":
-            self.waypoints_generator = GRUWaypointsPredictor(embed_dim)
-        elif self.waypoints_pred_head == "gru-command":
-            self.waypoints_generator = GRUWaypointsPredictorWithCommand(embed_dim)
-        elif self.waypoints_pred_head == "linear":
-            self.waypoints_generator = LinearWaypointsPredictor(embed_dim)
-        elif self.waypoints_pred_head == "linear-sum":
-            self.waypoints_generator = LinearWaypointsPredictor(embed_dim, cumsum=True)
-
-        self.junction_pred_head = nn.Linear(embed_dim, 2)
-        self.traffic_light_pred_head = nn.Linear(embed_dim, 2)
-        self.stop_sign_head = nn.Linear(embed_dim, 2)
-
-        if self.traffic_pred_head_type == "det":
-            self.traffic_pred_head = nn.Sequential(
-                *[
-                    nn.Linear(embed_dim + 32, 64),
-                    nn.ReLU(),
-                    nn.Linear(64, 7),
-                    nn.Sigmoid(),
-                ]
-            )
-        elif self.traffic_pred_head_type == "seg":
-            self.traffic_pred_head = nn.Sequential(
-                *[nn.Linear(embed_dim, 64), nn.ReLU(), nn.Linear(64, 1), nn.Sigmoid()]
-            )
-
-        self.position_encoding = PositionEmbeddingSine(embed_dim // 2, normalize=True)
-
-        encoder_layer = TransformerEncoderLayer(
-            embed_dim, num_heads, dim_feedforward, dropout, act_layer, normalize_before
-        )
-        self.encoder = TransformerEncoder(encoder_layer, enc_depth, None)
-
-        decoder_layer = TransformerDecoderLayer(
-            embed_dim, num_heads, dim_feedforward, dropout, act_layer, normalize_before
-        )
-        decoder_norm = nn.LayerNorm(embed_dim)
-        self.decoder = TransformerDecoder(
-            decoder_layer, dec_depth, decoder_norm, return_intermediate=False
-        )
-        self.reset_parameters()
-
-    def reset_parameters(self):
-        nn.init.uniform_(self.global_embed)
-        nn.init.uniform_(self.view_embed)
-        nn.init.uniform_(self.query_embed)
-        nn.init.uniform_(self.query_pos_embed)
-
-    def forward_features(
-        self,
-        front_image,
-        left_image,
-        right_image,
-        front_center_image,
-        lidar,
-        measurements,
-    ):
-        features = []
-
-        # Front view processing
-        front_image_token, front_image_token_global = self.rgb_patch_embed(front_image)
-        if self.use_view_embed:
-            front_image_token = (
-                front_image_token
-                + self.view_embed[:, :, 0:1, :]
-                + self.position_encoding(front_image_token)
-            )
-        else:
-            front_image_token = front_image_token + self.position_encoding(
-                front_image_token
-            )
-        front_image_token = front_image_token.flatten(2).permute(2, 0, 1)
-        front_image_token_global = (
-            front_image_token_global
-            + self.view_embed[:, :, 0, :]
-            + self.global_embed[:, :, 0:1]
-        )
-        front_image_token_global = front_image_token_global.permute(2, 0, 1)
-        features.extend([front_image_token, front_image_token_global])
-
-        if self.with_right_left_sensors:
-            # Left view processing
-            left_image_token, left_image_token_global = self.rgb_patch_embed(left_image)
-            if self.use_view_embed:
-                left_image_token = (
-                    left_image_token
-                    + self.view_embed[:, :, 1:2, :]
-                    + self.position_encoding(left_image_token)
-                )
-            else:
-                left_image_token = left_image_token + self.position_encoding(
-                    left_image_token
-                )
-            left_image_token = left_image_token.flatten(2).permute(2, 0, 1)
-            left_image_token_global = (
-                left_image_token_global
-                + self.view_embed[:, :, 1, :]
-                + self.global_embed[:, :, 1:2]
-            )
-            left_image_token_global = left_image_token_global.permute(2, 0, 1)
-
-            # Right view processing
-            right_image_token, right_image_token_global = self.rgb_patch_embed(
-                right_image
-            )
-            if self.use_view_embed:
-                right_image_token = (
-                    right_image_token
-                    + self.view_embed[:, :, 2:3, :]
-                    + self.position_encoding(right_image_token)
-                )
-            else:
-                right_image_token = right_image_token + self.position_encoding(
-                    right_image_token
-                )
-            right_image_token = right_image_token.flatten(2).permute(2, 0, 1)
-            right_image_token_global = (
-                right_image_token_global
-                + self.view_embed[:, :, 2, :]
-                + self.global_embed[:, :, 2:3]
-            )
-            right_image_token_global = right_image_token_global.permute(2, 0, 1)
-
-            features.extend(
-                [
-                    left_image_token,
-                    left_image_token_global,
-                    right_image_token,
-                    right_image_token_global,
-                ]
-            )
-
-        if self.with_center_sensor:
-            # Front center view processing
-            (
-                front_center_image_token,
-                front_center_image_token_global,
-            ) = self.rgb_patch_embed(front_center_image)
-            if self.use_view_embed:
-                front_center_image_token = (
-                    front_center_image_token
-                    + self.view_embed[:, :, 3:4, :]
-                    + self.position_encoding(front_center_image_token)
-                )
-            else:
-                front_center_image_token = (
-                    front_center_image_token
-                    + self.position_encoding(front_center_image_token)
-                )
-
-            front_center_image_token = front_center_image_token.flatten(2).permute(
-                2, 0, 1
-            )
-            front_center_image_token_global = (
-                front_center_image_token_global
-                + self.view_embed[:, :, 3, :]
-                + self.global_embed[:, :, 3:4]
-            )
-            front_center_image_token_global = front_center_image_token_global.permute(
-                2, 0, 1
-            )
-            features.extend([front_center_image_token, front_center_image_token_global])
-
-        if self.with_lidar:
-            lidar_token, lidar_token_global = self.lidar_patch_embed(lidar)
-            if self.use_view_embed:
-                lidar_token = (
-                    lidar_token
-                    + self.view_embed[:, :, 4:5, :]
-                    + self.position_encoding(lidar_token)
-                )
-            else:
-                lidar_token = lidar_token + self.position_encoding(lidar_token)
-            lidar_token = lidar_token.flatten(2).permute(2, 0, 1)
-            lidar_token_global = (
-                lidar_token_global
-                + self.view_embed[:, :, 4, :]
-                + self.global_embed[:, :, 4:5]
-            )
-            lidar_token_global = lidar_token_global.permute(2, 0, 1)
-            features.extend([lidar_token, lidar_token_global])
-
-        features = torch.cat(features, 0)
-        return features
-
-    def forward(self, x):
-        front_image = x["rgb"]
-        left_image = x["rgb_left"]
-        right_image = x["rgb_right"]
-        front_center_image = x["rgb_center"]
-        measurements = x["measurements"]
-        target_point = x["target_point"]
-        lidar = x["lidar"]
-
-        if self.direct_concat:
-            img_size = front_image.shape[-1]
-            left_image = torch.nn.functional.interpolate(
-                left_image, size=(img_size, img_size)
-            )
-            right_image = torch.nn.functional.interpolate(
-                right_image, size=(img_size, img_size)
-            )
-            front_center_image = torch.nn.functional.interpolate(
-                front_center_image, size=(img_size, img_size)
-            )
-            front_image = torch.cat(
-                [front_image, left_image, right_image, front_center_image], dim=1
-            )
-        features = self.forward_features(
-            front_image,
-            left_image,
-            right_image,
-            front_center_image,
-            lidar,
-            measurements,
-        )
-
-        bs = front_image.shape[0]
-
-        if self.end2end:
-            tgt = self.query_pos_embed.repeat(bs, 1, 1)
-        else:
-            tgt = self.position_encoding(
-                torch.ones((bs, 1, 20, 20), device=x["rgb"].device)
-            )
-            tgt = tgt.flatten(2)
-            tgt = torch.cat([tgt, self.query_pos_embed.repeat(bs, 1, 1)], 2)
-        tgt = tgt.permute(2, 0, 1)
-
-        memory = self.encoder(features, mask=self.attn_mask)
-        hs = self.decoder(self.query_embed.repeat(1, bs, 1), memory, query_pos=tgt)[0]
-
-        hs = hs.permute(1, 0, 2)  # Batchsize ,  N, C
-        if self.end2end:
-            waypoints = self.waypoints_generator(hs, target_point)
-            return waypoints
-
-        if self.waypoints_pred_head != "heatmap":
-            traffic_feature = hs[:, :400]
-            is_junction_feature = hs[:, 400]
-            traffic_light_state_feature = hs[:, 400]
-            stop_sign_feature = hs[:, 400]
-            waypoints_feature = hs[:, 401:411]
-        else:
-            traffic_feature = hs[:, :400]
-            is_junction_feature = hs[:, 400]
-            traffic_light_state_feature = hs[:, 400]
-            stop_sign_feature = hs[:, 400]
-            waypoints_feature = hs[:, 401:405]
-
-        if self.waypoints_pred_head == "heatmap":
-            waypoints = self.waypoints_generator(waypoints_feature, measurements)
-        elif self.waypoints_pred_head == "gru":
-            waypoints = self.waypoints_generator(waypoints_feature, target_point)
-        elif self.waypoints_pred_head == "gru-command":
-            waypoints = self.waypoints_generator(waypoints_feature, target_point, measurements)
-        elif self.waypoints_pred_head == "linear":
-            waypoints = self.waypoints_generator(waypoints_feature, measurements)
-        elif self.waypoints_pred_head == "linear-sum":
-            waypoints = self.waypoints_generator(waypoints_feature, measurements)
-
-        is_junction = self.junction_pred_head(is_junction_feature)
-        traffic_light_state = self.traffic_light_pred_head(traffic_light_state_feature)
-        stop_sign = self.stop_sign_head(stop_sign_feature)
-
-        velocity = measurements[:, 6:7].unsqueeze(-1)
-        velocity = velocity.repeat(1, 400, 32)
-        traffic_feature_with_vel = torch.cat([traffic_feature, velocity], dim=2)
-        traffic = self.traffic_pred_head(traffic_feature_with_vel)
-        return traffic, waypoints, is_junction, traffic_light_state, stop_sign, traffic_feature
-
-# ================== 3. فئة التتبع ==================
-class TrackedObject:
-    def __init__(self):
-        self.last_step = 0
-        self.last_pos = [0, 0]
-        # استخدام deque يعطينا كفاءة أفضل ويحدد حجمًا أقصى للذاكرة
-        self.historical_pos = deque(maxlen=10) 
-        self.historical_steps = deque(maxlen=10)
-        self.historical_features = deque(maxlen=10)
-
-    # هذه هي الدالة المفقودة التي يجب إضافتها
-    def update(self, step, object_info):
-        """
-        تحديث حالة الكائن بالبيانات الجديدة من الإطار الحالي.
-        """
-        self.last_step = step
-        self.last_pos = object_info[:2]
-        
-        # إضافة البيانات الجديدة إلى السجل التاريخي
-        self.historical_pos.append(self.last_pos)
-        self.historical_steps.append(step)
-        
-        # التأكد من وجود ميزات إضافية قبل إضافتها
-        if len(object_info) > 2:
-            self.historical_features.append(object_info[2])
-
-class Tracker:
-    def __init__(self, frequency=10):
-        self.tracks = []
-        self.alive_ids = []
-        self.frequency = frequency
-
-    def update_and_predict(self, det_data, pos, theta, frame_num):
-        det_data_weighted = det_data * reweight_array
-        detected_objects = find_peak_box(det_data_weighted)
-        objects_info = []
-        R = np.array([[np.cos(-theta), -np.sin(-theta)], [np.sin(-theta), np.cos(-theta)]])
-
-        for obj in detected_objects:
-            i, j = obj['coords']
-            obj_data = obj['raw_data']
-
-            center_y, center_x = convert_grid_to_xy(i, j)
-            center_x += obj_data[1]
-            center_y += obj_data[2]
-
-            loc = R.T.dot(np.array([center_x, center_y]))
-            objects_info.append([loc[0] + pos[0], loc[1] + pos[1], obj_data[1:]])  # [x, y, features...]
-
-        updates_ids = self._update(objects_info, frame_num)
-        speed_results, heading_results = self._predict(updates_ids)
-
-        for k, poi in enumerate(updates_ids):
-            i, j = poi
-            if heading_results[k] is not None:
-                factor = MERGE_PERCENT * 0.1
-                det_data[i, j, 3] = heading_results[k] * factor + det_data[i, j, 3] * (1 - factor)
-            if speed_results[k] is not None:
-                factor = MERGE_PERCENT * 0.1
-                det_data[i, j, 6] = speed_results[k] * factor + det_data[i, j, 6] * (1 - factor)
-        return det_data
-
-    def _update(self, objects_info, step):
-        latest_ids = []
-        if len(self.tracks) == 0:
-            for object_info in objects_info:
-                to = TrackedObject()
-                to.update(step, object_info)
-                self.tracks.append(to)
-                latest_ids.append(len(self.tracks) - 1)
-        else:
-            matched_ids = set()
-            for idx, object_info in enumerate(objects_info):
-                min_id, min_error = -1, float('inf')
-                pos_x, pos_y = object_info[:2]
-                for _id in self.alive_ids:
-                    if _id in matched_ids:
-                        continue
-                    track_pos = self.tracks[_id].last_pos
-                    distance = np.sqrt((track_pos[0] - pos_x)**2 + (track_pos[1] - pos_y)**2)
-                    if distance < 2.0 and distance < min_error:
-                        min_error = distance
-                        min_id = _id
-                if min_id != -1:
-                    self.tracks[min_id].update(step, objects_info[idx])
-                    latest_ids.append(min_id)
-                    matched_ids.add(min_id)
-                else:
-                    to = TrackedObject()
-                    to.update(step, object_info)
-                    self.tracks.append(to)
-                    latest_ids.append(len(self.tracks) - 1)
-        self.alive_ids = [i for i, track in enumerate(self.tracks) if track.last_step > step - 6]
-        return latest_ids
-
-    def _match(self, objects_info):
-        results = []
-        matched_ids = set()
-        for object_info in objects_info:
-            min_id, min_error = -1, float('inf')
-            pos_x, pos_y = object_info[:2]
-            for _id in self.alive_ids:
-                if _id in matched_ids:
-                    continue
-                track_pos = self.tracks[_id].last_pos
-                distance = np.sqrt((track_pos[0] - pos_x)**2 + (track_pos[1] - pos_y)**2)
-                if distance < min_error:
-                    min_error = distance
-                    min_id = _id
-            results.append(min_id)
-            if min_id != -1:
-                matched_ids.add(min_id)
-        return results
-
-    def _predict(self, updates_ids):
-        speed_results, heading_results = [], []
-        for each_id in updates_ids:
-            to = self.tracks[each_id]
-            avg_speed, avg_heading = [], []
-            for feature in to.historical_features:
-                avg_speed.append(feature[2])
-                avg_heading.append(feature[:2])
-            if len(avg_speed) < 2:
-                speed_results.append(None)
-                heading_results.append(None)
-                continue
-            avg_speed = np.mean(avg_speed)
-            avg_heading = np.mean(np.stack(avg_heading), axis=0)
-            yaw_angle = get_yaw_angle(avg_heading)
-            heading_results.append((4 - yaw_angle / np.pi) % 2)
-            speed_results.append(avg_speed)
-        return speed_results, heading_results
-
-
-
-
-
-# ================== 0. تعريف PID Controller ==================
-class PIDController:
-    def __init__(self, K_P=1.0, K_I=0.0, K_D=0.0, n=20):
-        self._K_P = K_P
-        self._K_I = K_I
-        self._K_D = K_D
-        self._window = deque([0 for _ in range(n)], maxlen=n)
-        self._max = 0.0
-        self._min = 0.0
-
-    def step(self, error):
-        self._window.append(error)
-        self._max = max(self._max, abs(error))
-        self._min = -abs(self._max)
-
-        if len(self._window) >= 2:
-            integral = np.mean(self._window)
-            derivative = self._window[-1] - self._window[-2]
-        else:
-            integral = 0.0
-            derivative = 0.0
-
-        return self._K_P * error + self._K_I * integral + self._K_D * derivative
-# ================== 4. فئة المتحكم ==================
-class InterfuserController:
-    def __init__(self, config):
-        self.turn_controller = PIDController(
-            K_P=config.turn_KP,
-            K_I=config.turn_KI,
-            K_D=config.turn_KD,
-            n=config.turn_n,
-        )
-        self.speed_controller = PIDController(
-            K_P=config.speed_KP,
-            K_I=config.speed_KI,
-            K_D=config.speed_KD,
-            n=config.speed_n,
-        )
-        self.config = config
-        self.collision_buffer = np.array(config.collision_buffer)
-        self.detect_threshold = config.detect_threshold
-        self.stop_steps = 0
-        self.forced_forward_steps = 0
-        self.red_light_steps = 0
-        self.block_red_light = 0
-        self.in_stop_sign_effect = False
-        self.block_stop_sign_distance = 0
-        self.stop_sign_timer = 0
-        self.stop_sign_trigger_times = 0
-
-    def run_step(
-        self, speed, waypoints, junction, traffic_light_state, stop_sign, meta_data
-    ):
-        # --- تحديث حالة التوقف ---
-        if speed < 0.2:
-            self.stop_steps += 1
-        else:
-            self.stop_steps = max(0, self.stop_steps - 10)
-
-        if speed < 0.06 and self.in_stop_sign_effect:
-            self.in_stop_sign_effect = False
-
-        if junction < 0.3:
-            self.stop_sign_trigger_times = 0
-
-        if traffic_light_state > 0.7:
-            self.red_light_steps += 1
-        else:
-            self.red_light_steps = 0
-
-        if self.red_light_steps > 1000:
-            self.block_red_light = 80
-            self.red_light_steps = 0
-
-        if self.block_red_light > 0:
-            self.block_red_light -= 1
-            traffic_light_state = 0.01
-
-        if stop_sign < 0.6 and self.block_stop_sign_distance < 0.1:
-            self.in_stop_sign_effect = True
-            self.block_stop_sign_distance = 2.0
-            self.stop_sign_trigger_times = 3
-
-        self.block_stop_sign_distance = max(
-            0, self.block_stop_sign_distance - 0.05 * speed
-        )
-
-        if self.block_stop_sign_distance < 0.1:
-            if self.stop_sign_trigger_times > 0:
-                self.block_stop_sign_distance = 2.0
-                self.stop_sign_trigger_times -= 1
-                self.in_stop_sign_effect = True
-
-        # --- حساب زاوية الانعطاف ---
-        aim = (waypoints[1] + waypoints[0]) / 2.0
-        angle = np.degrees(np.pi / 2 - np.arctan2(aim[1], aim[0])) / 90
-        if speed < 0.01:
-            angle = 0
-        steer = self.turn_controller.step(angle)
-        steer = np.clip(steer, -1.0, 1.0)
-
-        brake = False
-        throttle = 0.0
-        desired_speed = 0.0
-
-        downsampled_waypoints = downsample_waypoints(waypoints)
-
-        d_0 = get_max_safe_distance(
-            meta_data,
-            downsampled_waypoints,
-            t=0,
-            collision_buffer=self.collision_buffer,
-            threshold=self.detect_threshold,
-        )
-        d_05 = get_max_safe_distance(
-            meta_data,
-            downsampled_waypoints,
-            t=0.5,
-            collision_buffer=self.collision_buffer,
-            threshold=self.detect_threshold,
-        )
-        d_075 = get_max_safe_distance(
-            meta_data,
-            downsampled_waypoints,
-            t=0.75,
-            collision_buffer=self.collision_buffer,
-            threshold=self.detect_threshold,
-        )
-        d_1 = get_max_safe_distance(
-            meta_data,
-            downsampled_waypoints,
-            t=1,
-            collision_buffer=self.collision_buffer,
-            threshold=self.detect_threshold,
-        )
-        d_15 = get_max_safe_distance(
-            meta_data,
-            downsampled_waypoints,
-            t=1.5,
-            collision_buffer=self.collision_buffer,
-            threshold=self.detect_threshold,
-        )
-        d_2 = get_max_safe_distance(
-            meta_data,
-            downsampled_waypoints,
-            t=2,
-            collision_buffer=self.collision_buffer,
-            threshold=self.detect_threshold,
-        )
-
-        d_05 = min(d_0, d_05, d_075)
-        d_1 = min(d_05, d_075, d_15, d_2)
-
-        safe_dis = min(d_05, d_1)
-        d_0 = max(0, d_0 - 2.0)
-        d_05 = max(0, d_05 - 2.0)
-        d_1 = max(0, d_1 - 2.0)
-
-        # --- تفعيل الفرملة فقط إذا كانت الإشارة حمراء أو هناك علامة Stop ---
-        if traffic_light_state > 0.5:
-            brake = True
-            desired_speed = 0.0
-        elif stop_sign > 0.6 and traffic_light_state <= 0.5:
-            if self.stop_sign_timer < 20:
-                brake = True
-                desired_speed = 0.0
-                self.stop_sign_timer += 1
-            else:
-                brake = False
-                desired_speed = max(0, min(self.config.max_speed, speed + 0.2))
-        else:
-            brake = False
-            desired_speed = max(0, min(self.config.max_speed, speed + 0.2))
-
-        delta = np.clip(desired_speed - speed, 0.0, self.config.clip_delta)
-        throttle = self.speed_controller.step(delta)
-        throttle = np.clip(throttle, 0.0, self.config.max_throttle)
-
-        # --- إذا كانت السرعة أعلى من 1.1 مرة السرعة المستهدفة، نفرم ---
-        if speed > desired_speed * self.config.brake_ratio:
-            brake = True
-
-        # --- إعداد معلومات التشخيص ---
-        meta_info_1 = f"speed: {speed:.2f}, target_speed: {desired_speed:.2f}"
-        meta_info_2 = f"on_road_prob: {junction:.2f}, red_light_prob: {traffic_light_state:.2f}, stop_sign_prob: {1 - stop_sign:.2f}"
-        meta_info_3 = f"stop_steps: {self.stop_steps}, block_stop_sign_distance: {self.block_stop_sign_distance:.1f}"
-
-        # --- حالة خاصة بعد فترة طويلة من التوقف ---
-        if self.stop_steps > 1200:
-            self.forced_forward_steps = 12
-            self.stop_steps = 0
-        if self.forced_forward_steps > 0:
-            throttle = 0.8
-            brake = False
-            self.forced_forward_steps -= 1
-        if self.in_stop_sign_effect:
-            throttle = 0
-            brake = True
-
-        return steer, throttle, brake, (meta_info_1, meta_info_2, meta_info_3, safe_dis)
-
-
-class ControllerConfig:
-    turn_KP, turn_KI, turn_KD, turn_n = 1.0, 0.1, 0.1, 20
-    speed_KP, speed_KI, speed_KD, speed_n = 0.5, 0.05, 0.1, 20
-    max_speed, max_throttle, clip_delta = 6.0, 0.75, 0.25
-    collision_buffer, detect_threshold = [0.0, 0.0], 0.04
-    brake_speed, brake_ratio = 0.4, 1.1
-
-
-# ================== 5. واجهة العرض ==================
-class DisplayInterface:
-    def __init__(self, width=1200, height=600):
-        self._width = width
-        self._height = height
-
-    def run_interface(self, data):
-        dashboard = np.zeros((self._height, self._width, 3), dtype=np.uint8)
-        font = cv2.FONT_HERSHEY_SIMPLEX
-        dashboard[:, :800] = cv2.resize(data.get('camera_view'), (800, 600))
-        dashboard[:400, 800:1200] = cv2.resize(data['map_t0'], (400, 400))
-        dashboard[400:600, 800:1000] = cv2.resize(data['map_t1'], (200, 200))
-        dashboard[400:600, 1000:1200] = cv2.resize(data['map_t2'], (200, 200))
-
-        # خطوط فصل
-        cv2.line(dashboard, (800, 0), (800, 600), (255, 255, 255), 2)
-        cv2.line(dashboard, (800, 400), (1200, 400), (255, 255, 255), 2)
-        cv2.line(dashboard, (1000, 400), (1000, 600), (255, 255, 255), 2)
-
-        y_pos = 40
-        for key, text in data['text_info'].items():
-            cv2.putText(dashboard, text, (820, y_pos), font, 0.6, (255, 255, 255), 1)
-            y_pos += 30
-
-        y_pos += 10
-        for t, counts in data['object_counts'].items():
-            count_str = f"{t}: C={counts['car']} B={counts['bike']} P={counts['pedestrian']}"
-            cv2.putText(dashboard, count_str, (820, y_pos), font, 0.5, (255, 255, 255), 1)
-            y_pos += 20
-
-        cv2.putText(dashboard, "t0", (1160, 30), font, 0.8, (0, 255, 255), 2)
-        cv2.putText(dashboard, "t1", (960, 430), font, 0.8, (0, 255, 255), 2)
-        cv2.putText(dashboard, "t2", (1160, 430), font, 0.8, (0, 255, 255), 2)
-
-        return dashboard
-
-# --- تحديد التحوّلات ---
-transform = transforms.Compose([
-    # الخطوة الأولى الآن هي تغيير الحجم مباشرة
-    transforms.Resize((224, 224)),
-    transforms.ToTensor(),
-    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
-])
-
-lidar_transform = transforms.Compose([
-    # الخطوة الأولى الآن هي تغيير الحجم مباشرة
-    transforms.Resize((112, 112)),
-    transforms.ToTensor(),
-    transforms.Normalize(mean=[0.5], std=[0.5]),
-])
-
-class LMDriveDataset(Dataset):
-    def __init__(self, data_dir, transform=None, lidar_transform=None):
-        self.data_dir = Path(data_dir)
-        self.transform = transform
-        self.lidar_transform = lidar_transform
-        self.samples = []
-
-        measurement_dir = self.data_dir / "measurements"
-        image_dir = self.data_dir / "rgb_full"
-
-        measurement_files = sorted([f for f in os.listdir(measurement_dir) if f.endswith(".json")])
-        image_files = sorted([f for f in os.listdir(image_dir) if f.endswith(".jpg")])
-
-        num_samples = min(len(measurement_files), len(image_files))
-
-        for i in range(num_samples):
-            frame_id = i
-            measurement_path = str(measurement_dir / f"{frame_id:04d}.json")
-            image_name = f"{frame_id:04d}.jpg"
-            image_path = str(image_dir / image_name)
-
-            if not os.path.exists(measurement_path) or not os.path.exists(image_path):
-                continue
-
-            with open(measurement_path, "r") as f:
-                measurements_data = json.load(f)
-
-            self.samples.append({
-                "image_path": image_path,
-                "measurement_path": measurement_path,
-                "frame_id": frame_id,
-                "measurements": measurements_data
-            })
-
-    def __len__(self):
-        return len(self.samples)
-
-    def __getitem__(self, idx):
-        sample = self.samples[idx]
-
-        # قراءة الصورة الكاملة (2400x800)
-        full_image = cv2.imread(sample["image_path"])
-        if full_image is None:
-            raise ValueError(f"Failed to load image: {sample['image_path']}")
-        full_image = cv2.cvtColor(full_image, cv2.COLOR_BGR2RGB)
-
-        # تقسيم الصورة إلى أجزاء (كل جزء 600x800)
-        front_image = full_image[:600, :800]      # الجزء الأول
-        left_image = full_image[600:1200, :800]   # الجزء الثاني
-        right_image = full_image[1200:1800, :800] # الجزء الثالث
-        center_image = full_image[1800:2400, :800]# الجزء الرابع
-
-        # تطبيق التحويل على كل صورة
-        front_image_tensor = self.transform(front_image)
-        left_image_tensor = self.transform(left_image)
-        right_image_tensor = self.transform(right_image)
-        center_image_tensor = self.transform(center_image)
-
-        # تحميل الليدار
-        lidar_path = str(self.data_dir / "lidar" / f"{sample['frame_id']:04d}.png")
-        lidar = cv2.imread(lidar_path)
-
-        if lidar is None:
-            lidar = np.zeros((112, 112, 3), dtype=np.uint8)  # مكان فارغ
-        else:
-            if len(lidar.shape) == 2:
-                lidar = cv2.cvtColor(lidar, cv2.COLOR_GRAY2BGR)
-            lidar = cv2.cvtColor(lidar, cv2.COLOR_BGR2RGB)
-
-        lidar_tensor = self.lidar_transform(lidar)
-
-        # استخراج القياسات
-        measurements_data = sample["measurements"]
-
-        x = measurements_data.get("x", 0.0)
-        y = measurements_data.get("y", 0.0)
-        theta = measurements_data.get("theta", 0.0)
-        speed = measurements_data.get("speed", 0.0)
-        steer = measurements_data.get("steer", 0.0)
-        throttle = measurements_data.get("throttle", 0.0)
-        brake = int(measurements_data.get("brake", False))
-        command = measurements_data.get("command", 0)
-        is_junction = int(measurements_data.get("is_junction", False))
-        should_brake = int(measurements_data.get("should_brake", 0))
-        x_command = measurements_data.get("x_command", 0.0)
-        y_command = measurements_data.get("y_command", 0.0)
-
-        target_point = torch.tensor([x_command, y_command], dtype=torch.float32)
-
-        measurements = torch.tensor(
-            [x, y, theta, speed, steer, throttle, brake, command, is_junction, should_brake],
-            dtype=torch.float32
-        )
-
-        return {
-            "rgb": front_image_tensor,
-            "rgb_left": left_image_tensor,
-            "rgb_right": right_image_tensor,
-            "rgb_center": center_image_tensor,
-            "lidar": lidar_tensor,
-            "measurements": measurements,
-            "target_point": target_point
-        }
-
-
-
-
-SAVE_VIDEO = True
-FPS = 10
-WAYPOINT_SCALE_FACTOR = 5.0
-T1_FUTURE_TIME = 1.0
-T2_FUTURE_TIME = 2.0
-TRACKER_FREQUENCY = 10
-MERGE_PERCENT = 0.4
-PIXELS_PER_METER = 8
-MAX_DISTANCE = 32
-IMG_SIZE = MAX_DISTANCE * PIXELS_PER_METER * 2
-EGO_CAR_X = IMG_SIZE // 2
-EGO_CAR_Y = IMG_SIZE - (4.0 * PIXELS_PER_METER)
-reweight_array = np.ones((20, 20, 7))
-last_valid_waypoints = None
-last_valid_theta = 0.0
-
-def to_2tuple(x):
-    if isinstance(x, tuple): return x
-    return (x, x)
-
-
-def _get_clones(module, N):
-    return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
-
-
-def _get_activation_fn(activation):
-    """Return an activation function given a string"""
-    if activation == "relu":
-        return F.relu
-    if activation == "gelu":
-        return F.gelu
-    if activation == "glu":
-        return F.glu
-    raise RuntimeError(f"activation should be relu/gelu, not {activation}.")
-
-
-def build_attn_mask(mask_type):
-    mask = torch.ones((151, 151), dtype=torch.bool).cuda()
-    if mask_type == "seperate_all":
-        mask[:50, :50] = False
-        mask[50:67, 50:67] = False
-        mask[67:84, 67:84] = False
-        mask[84:101, 84:101] = False
-        mask[101:151, 101:151] = False
-    elif mask_type == "seperate_view":
-        mask[:50, :50] = False
-        mask[50:67, 50:67] = False
-        mask[67:84, 67:84] = False
-        mask[84:101, 84:101] = False
-        mask[101:151, :] = False
-        mask[:, 101:151] = False
-    return mask
-
-
-def get_yaw_angle(forward_vector):
-    forward_vector = forward_vector / np.linalg.norm(forward_vector)
-    yaw = math.atan2(forward_vector[1], forward_vector[0])
-    return yaw
-
-
-@register_model
-def interfuser_baseline(**kwargs):
-    model = Interfuser(
-        enc_depth=6,
-        dec_depth=6,
-        embed_dim=256,
-        rgb_backbone_name="r50",
-        lidar_backbone_name="r18",
-        waypoints_pred_head="gru",
-        use_different_backbone=True,
-    )
-    # model.save_pretrained("/content/t")
-    return model
-
-def ensure_rgb(image):
-    """تحويل الصورة إلى RGB إذا كانت grayscale."""
-    if len(image.shape) == 2 or image.shape[2] == 1:
-        return cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
-    return image
-def process_camera_image(tensor_image):
-    """تحويل صورة الكاميرا من Tensor إلى NumPy Array."""
-    image_np = tensor_image.permute(1, 2, 0).cpu().numpy()
-    image_np = (image_np * np.array([0.229, 0.224, 0.225])) + np.array([0.485, 0.456, 0.406])
-    image_np = np.clip(image_np, 0, 1)
-    return (image_np * 255).astype(np.uint8)[:, :, ::-1]  # BGR
-
-
-def convert_grid_to_xy(i, j):
-    """تحويل الشبكة إلى إحداثيات x, y."""
-    return (j - 9.5) * 1.6, (19.5 - i) * 1.6
-
-
-def add_rect(img, loc, ori, box, value, color):
-    """
-    إضافة مستطيل إلى الخريطة.
-    """
-    center_x = int(loc[0] * PIXELS_PER_METER + MAX_DISTANCE * PIXELS_PER_METER)
-    center_y = int(loc[1] * PIXELS_PER_METER + MAX_DISTANCE * PIXELS_PER_METER)
-
-    size_px = (
-        int(box[0] * PIXELS_PER_METER),
-        int(box[1] * PIXELS_PER_METER)
-    )
-
-    angle_deg = -np.degrees(math.atan2(ori[1], ori[0]))
-
-    box_points = cv2.boxPoints(((center_x, center_y), size_px, angle_deg))
-    box_points = np.int32(box_points)
-
-    adjusted_color = [int(x * value) for x in color]
-    cv2.fillConvexPoly(img, box_points, adjusted_color)
-    return img
-
-def find_peak_box(data):
-    """
-    اكتشاف القمم في البيانات وتصنيفها.
-    """
-    det_data = np.zeros((22, 22, 7))
-    det_data[1:21, 1:21] = data
-    detected_objects = []
-
-    for i in range(1, 21):
-        for j in range(1, 21):
-            if det_data[i, j, 0] > 0.6 and (
-                det_data[i, j, 0] > det_data[i, j - 1, 0]
-                and det_data[i, j, 0] > det_data[i, j + 1, 0]
-                and det_data[i, j, 0] > det_data[i - 1, j, 0]
-                and det_data[i, j, 0] > det_data[i + 1, j, 0]
-            ):
-                length = det_data[i, j, 4]
-                width = det_data[i, j, 5]
-                confidence = det_data[i, j, 0]
-
-                obj_type = 'unknown'
-                if length > 4.0:
-                    obj_type = 'car'
-                elif length / width > 1.5:
-                    obj_type = 'bike'
-                else:
-                    obj_type = 'pedestrian'
-
-                detected_objects.append({
-                    'coords': (i - 1, j - 1),
-                    'type': obj_type,
-                    'confidence': confidence,
-                    'raw_data': det_data[i, j]
-                })
-
-    return detected_objects
-
-
-def render(det_data, t=0):
-    """
-    رسم كائنات الكشف على الخريطة BEV.
-    """
-    CLASS_COLORS = {'car': (0, 0, 255), 'bike': (0, 255, 0), 'pedestrian': (255, 0, 0), 'unknown': (128, 128, 128)}
-    det_weighted = det_data * reweight_array
-    detected_objects = find_peak_box(det_weighted)
-    counts = {cls: 0 for cls in CLASS_COLORS.keys()}
-    [counts.update({obj['type']: counts.get(obj['type'], 0) + 1}) for obj in detected_objects]
-    img = np.zeros((IMG_SIZE, IMG_SIZE, 3), np.uint8)
-
-    for obj in detected_objects:
-        i, j = obj['coords']
-        obj_data = obj['raw_data']
-        speed = obj_data[6]
-        center_x, center_y = convert_grid_to_xy(i, j)
-        theta = obj_data[3] * np.pi
-        ori = np.array([math.cos(theta), math.sin(theta)])
-        loc_x = center_x + obj_data[1] + t * speed * ori[0]
-        loc_y = center_y + obj_data[2] - t * speed * ori[1]
-        box = np.array([obj_data[4], obj_data[5]])
-        if obj['type'] == 'pedestrian':
-            box *= 1.5
-        add_rect(
-            img,
-            loc=np.array([loc_x, loc_y]),
-            ori=ori,
-            box=box,
-            value=obj['confidence'],
-            color=CLASS_COLORS[obj['type']]
-        )
-    return img, counts
-
-
-def render_self_car(loc, ori, box, pixels_per_meter=PIXELS_PER_METER):
-    """
-    رسم السيارة الذاتية على الخريطة BEV.
-    Args:
-        loc: موقع السيارة [x, y] في النظام العالمي.
-        ori: اتجاه السيارة [cos(theta), sin(theta)].
-        box: أبعاد السيارة [طول, عرض].
-        pixels_per_meter: عدد البكسلات لكل متر.
-    Returns:
-        self_car_map: خريطة السيارة ذاتية القيادة (RGB - 3 قنوات).
-    """
-    img = np.zeros((IMG_SIZE, IMG_SIZE, 3), np.uint8)
-    center_x = int(loc[0] * pixels_per_meter + MAX_DISTANCE * pixels_per_meter)
-    center_y = int(loc[1] * pixels_per_meter + MAX_DISTANCE * pixels_per_meter)
-    size_px = (
-        int(box[0] * pixels_per_meter),
-        int(box[1] * pixels_per_meter)
-    )
-    angle_deg = -np.degrees(math.atan2(ori[1], ori[0]))
-    box_points = cv2.boxPoints(((center_x, center_y), size_px, angle_deg))
-    box_points = np.int32(box_points)
-    ego_color = (0, 255, 255)  # أصفر
-    cv2.fillConvexPoly(img, box_points, ego_color)
-    return img  # ← نرجع الصورة بأكملها وليس جزءًا منها
-
-def render_waypoints(waypoints, pixels_per_meter=PIXELS_PER_METER):
-    global last_valid_waypoints
-    img = np.zeros((IMG_SIZE, IMG_SIZE, 3), np.uint8)
-    current_waypoints = waypoints
-    if waypoints is not None and len(waypoints) > 2:
-        last_valid_waypoints = waypoints
-    else:
-        current_waypoints = last_valid_waypoints
-    if current_waypoints is None:
-        return img
-    origin_x, origin_y = EGO_CAR_X, EGO_CAR_Y
-    for i, point in enumerate(current_waypoints):
-        px = int(origin_x + point[1] * pixels_per_meter)
-        py = int(origin_y - point[0] * pixels_per_meter)
-        color = (0, 0, 255) if i == len(current_waypoints) - 1 else (0, 255, 0)
-        cv2.circle(img, (px, py), 4, color, -1)
-    return img
-
-
-def collision_detections(map1, map2, threshold=0.04):
-    """
-    تحقق من وجود تداخل بين خريطة البيئة ونموذج السيارة.
-    """
-    print("map1 shape:", map1.shape)
-    print("map2 shape:", map2.shape)
-
-    # تحويل map2 إلى grayscale إذا كانت تحتوي على 3 قنوات (RGB)
-    if len(map2.shape) == 3 and map2.shape[2] == 3:
-        map2 = cv2.cvtColor(map2, cv2.COLOR_BGR2GRAY)
-
-    # التأكد من أن map1 و map2 لها نفس الأبعاد
-    assert map1.shape == map2.shape
-
-    overlap_map = (map1 > 0.01) & (map2 > 0.01)
-    ratio = float(np.sum(overlap_map)) / np.sum(map2 > 0)
-    return ratio < threshold
-
-def get_max_safe_distance(meta_data, downsampled_waypoints, t, collision_buffer, threshold):
-    """
-    حساب أقصى مسافة آمنة قبل حدوث تصادم.
-    """
-    surround_map = meta_data.reshape(20, 20, 7)[..., :3][..., 0]
-    if np.sum(surround_map) < 1:
-        return np.linalg.norm(downsampled_waypoints[-3])
-    hero_bounding_box = np.array([2.45, 1.0]) + collision_buffer
-    safe_distance = 0.0
-    for i in range(len(downsampled_waypoints) - 2):
-        aim = (downsampled_waypoints[i + 1] + downsampled_waypoints[i + 2]) / 2.0
-        loc = downsampled_waypoints[i]
-        ori = aim - loc
-        self_car_map = render_self_car(loc=loc, ori=ori, box=hero_bounding_box, pixels_per_meter=PIXELS_PER_METER)
-        # تصغير الخريطة والتحويل إلى grayscale
-        self_car_map_resized = cv2.resize(self_car_map, (20, 20))
-        self_car_map_gray = cv2.cvtColor(self_car_map_resized, cv2.COLOR_BGR2GRAY)
-        if not collision_detections(surround_map, self_car_map_gray, threshold):
-            break
-        safe_distance = max(safe_distance, np.linalg.norm(loc))
-    return safe_distance
-
-def downsample_waypoints(waypoints, precision=0.2):
-    """
-    تقليل عدد نقاط المسار.
-    """
-    downsampled_waypoints = []
-    last_waypoint = np.array([0.0, 0.0])
-    for i in range(len(waypoints)):
-        now_waypoint = waypoints[i]
-        dis = np.linalg.norm(now_waypoint - last_waypoint)
-        if dis > precision:
-            interval = int(dis / precision)
-            move_vector = (now_waypoint - last_waypoint) / (interval + 1)
-            for j in range(interval):
-                downsampled_waypoints.append(last_waypoint + move_vector * (j + 1))
-        downsampled_waypoints.append(now_waypoint)
-        last_waypoint = now_waypoint
-    return downsampled_waypoints
-
-
-
+# --- استيراد من الملفات التي أنشأناها ---
+from model import interfuser_baseline
+from logic import (
+    transform, lidar_transform, InterfuserController, ControllerConfig,
+    Tracker, DisplayInterface, render, render_waypoints, render_self_car,
+    ensure_rgb, WAYPOINT_SCALE_FACTOR, T1_FUTURE_TIME, T2_FUTURE_TIME
+)
 
 # ==============================================================================
-#           Gradio Application Logic
+#           1. تحميل النموذج (يتم مرة واحدة)
 # ==============================================================================
-
-# --- Load the Model (do this once globally) ---
 print("Loading the Interfuser model...")
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model = interfuser_baseline() # This function needs to be defined from your script
-# Ensure the model file is in the same directory in your Hugging Face Space
-model_path = "interfuser_best_model.pth"
+model = interfuser_baseline()
+model_path = "model/interfuser_best_model.pth"
 if not os.path.exists(model_path):
-    raise FileNotFoundError(f"Model file not found at {model_path}. Please upload it to the Space.")
+    raise FileNotFoundError(f"Model file not found at {model_path}. Please upload it.")
 
-state_dic = torch.load(model_path, map_location=device, weights_only=True)
+# استخدام weights_only=True لزيادة الأمان عند تحميل الملفات من مصادر غير موثوقة
+try:
+    state_dic = torch.load(model_path, map_location=device, weights_only=True)
+except:
+    state_dic = torch.load(model_path, map_location=device)
+    
 model.load_state_dict(state_dic)
 model.to(device)
 model.eval()
 print("Model loaded successfully.")
 
+
+# ==============================================================================
+#           2. دالة التشغيل الرئيسية لـ Gradio
+# ==============================================================================
 def run_single_frame(
     rgb_image_path: str,
     rgb_left_image_path: str,
@@ -1867,7 +50,7 @@ def run_single_frame(
     rgb_center_image_path: str,
     lidar_image_path: str,
     measurements_path: str,
-    target_point_list: list 
+    target_point_list: list
 ):
     """
     تعالج إطارًا واحدًا من البيانات، وتُنشئ لوحة تحكم مرئية كاملة،
@@ -1878,46 +61,36 @@ def run_single_frame(
         #           1. قراءة ومعالجة المدخلات من المسارات
         # ==========================================================
         if not rgb_image_path:
-          raise gr.Error("الرجاء توفير مسار الصورة الأمامية (RGB).")        
-        # --- أ. قراءة الصور ---
-        # <<< تصحيح: استخدام .name لقراءة الملفات >>>
-        rgb_image_pil = Image.open(rgb_image_path.name)
-    
-        # إذا كانت مسارات الصور الأخرى غير متوفرة، استخدم الصورة الأمامية
-        # نتحقق من وجود الكائن نفسه ثم نستخدم .name
-        rgb_left_pil = Image.open(rgb_left_image_path.name) if rgb_left_image_path else rgb_image_pil
-        rgb_right_pil = Image.open(rgb_right_image_path.name) if rgb_right_image_path else rgb_image_pil
-        rgb_center_pil = Image.open(rgb_center_image_path.name) if rgb_center_image_path else rgb_image_pil
-    
-        # --- ب. قراءة ومعالجة الليدار ---
+          raise gr.Error("الرجاء توفير مسار الصورة الأمامية (RGB).")
+
+        rgb_image_pil = Image.open(rgb_image_path.name).convert("RGB")
+        rgb_left_pil = Image.open(rgb_left_image_path.name).convert("RGB") if rgb_left_image_path else rgb_image_pil
+        rgb_right_pil = Image.open(rgb_right_image_path.name).convert("RGB") if rgb_right_image_path else rgb_image_pil
+        rgb_center_pil = Image.open(rgb_center_image_path.name).convert("RGB") if rgb_center_image_path else rgb_image_pil
+
         if lidar_image_path:
-            # <<< تصحيح: استخدام .name لقراءة ملف .npy >>>
             lidar_array = np.load(lidar_image_path.name)
             if lidar_array.max() > 0:
                 lidar_array = (lidar_array / lidar_array.max()) * 255.0
             lidar_pil = Image.fromarray(lidar_array.astype(np.uint8))
-            lidar_image_pil = lidar_pil.convert('RGB') if lidar_pil.mode != 'RGB' else lidar_pil
+            lidar_image_pil = lidar_pil.convert('RGB')
         else:
             lidar_image_pil = Image.fromarray(np.zeros((112, 112, 3), dtype=np.uint8))
-    
-        # --- ج. تحويل الصور إلى تنسورات ---
+
         rgb_tensor = transform(rgb_image_pil).unsqueeze(0).to(device)
         rgb_left_tensor = transform(rgb_left_pil).unsqueeze(0).to(device)
         rgb_right_tensor = transform(rgb_right_pil).unsqueeze(0).to(device)
         rgb_center_tensor = transform(rgb_center_pil).unsqueeze(0).to(device)
         lidar_tensor = lidar_transform(lidar_image_pil).unsqueeze(0).to(device)
-        
-        # --- د. قراءة البيانات الرقمية ---
-        # <<< تصحيح: استخدام .name لقراءة ملف JSON >>>
+
         with open(measurements_path.name, 'r') as f:
             measurements_dict = json.load(f)
 
         measurements_values = [
-            measurements_dict.get('x', 0.0), measurements_dict.get('y', 0.0),
-            measurements_dict.get('theta', 0.0), measurements_dict.get('speed', 5.0),
-            measurements_dict.get('steer', 0.0), measurements_dict.get('throttle', 0.0),
-            measurements_dict.get('brake', 0.0), measurements_dict.get('command', 2.0),
-            measurements_dict.get('is_junction', 0.0), measurements_dict.get('should_brake', 0.0)
+            measurements_dict.get('command', 2.0), measurements_dict.get('command', 2.0),
+            measurements_dict.get('command', 2.0), measurements_dict.get('command', 2.0),
+            measurements_dict.get('command', 2.0), measurements_dict.get('command', 2.0),
+            measurements_dict.get('speed', 5.0)
         ]
         measurements_tensor = torch.tensor([measurements_values], dtype=torch.float32).to(device)
         target_point_tensor = torch.tensor([target_point_list], dtype=torch.float32).to(device)
@@ -1936,7 +109,7 @@ def run_single_frame(
             traffic, waypoints, is_junction, traffic_light, stop_sign, _ = outputs
 
         measurements_np = measurements_tensor[0].cpu().numpy()
-        pos, theta, speed = measurements_np[:2], measurements_np[2], measurements_np[3]
+        pos, theta, speed = [0,0], 0, measurements_np[6]
         
         traffic_np = traffic[0].detach().cpu().numpy().reshape(20, 20, -1)
         waypoints_np = waypoints[0].detach().cpu().numpy() * WAYPOINT_SCALE_FACTOR
@@ -1992,66 +165,58 @@ def run_single_frame(
             "metadata": {"speed_info": metadata_tuple[0], "perception_info": metadata_tuple[1], "stop_info": metadata_tuple[2], "safe_distance": metadata_tuple[3]}
         }
         
-        # تحويل صورة numpy إلى PIL Image قبل إرجاعها
         return Image.fromarray(dashboard_image), result_dict
 
     except Exception as e:
         print(traceback.format_exc())
         raise gr.Error(f"Error processing single frame: {e}")
 
-with gr.Blocks() as demo:
+
+# ==============================================================================
+#           4. تعريف واجهة Gradio
+# ==============================================================================
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
     gr.Markdown("# 🚗 محاكاة القيادة الذاتية باستخدام Interfuser")
     
     with gr.Tabs():
-               with gr.TabItem("نقطة نهاية API (إطار واحد)", id=1):
-                    gr.Markdown("### اختبار النموذج بإدخال مباشر (Single Frame Inference)")
-                    gr.Markdown("هذه الواجهة مخصصة للمطورين. قم برفع الملفات المطلوبة لتشغيل النموذج على إطار واحد.")
-                    
-                    with gr.Row():
-                        with gr.Column(scale=1):
-                            gr.Markdown("#### ملفات الصور")
-                            api_rgb_image_path = gr.File(label="RGB (Front) File (.jpg, .png)")
-                            api_rgb_left_image_path = gr.File(label="RGB (Left) File (Optional)")
-                            api_rgb_right_image_path = gr.File(label="RGB (Right) File (Optional)")
-                            api_rgb_center_image_path = gr.File(label="RGB (Center) File (Optional)")
-                            api_lidar_image_path = gr.File(label="LiDAR File (.npy, Optional)")
-                        
-                        with gr.Column(scale=2):
-                            gr.Markdown("#### ملفات ومحتويات البيانات")
-                            api_measurements_path = gr.File(label="Measurements File (.json)")
-                            api_target_point_list = gr.JSON(label="Target Point (List [x, y])", value=[0.0, 100.0])
-                            api_output_image = gr.Image(label="Dashboard Result", type="pil")
-                            api_output_json = gr.JSON(label="نتائج النموذج (JSON)")
-                            gr.Markdown("---")
-                            api_run_button = gr.Button("🚀 تشغيل إطار واحد", variant="primary")
-                    gr.Markdown("---")
+        with gr.TabItem("نقطة نهاية API (إطار واحد)", id=1):
+            gr.Markdown("### اختبار النموذج بإدخال مباشر (Single Frame Inference)")
+            gr.Markdown("هذه الواجهة مخصصة للمطورين. قم برفع الملفات المطلوبة لتشغيل النموذج على إطار واحد.")
+            
+            with gr.Row():
+                with gr.Column(scale=1):
+                    gr.Markdown("#### ملفات الصور والبيانات")
+                    api_rgb_image_path = gr.File(label="RGB (Front) File (.jpg, .png)")
+                    api_rgb_left_image_path = gr.File(label="RGB (Left) File (Optional)")
+                    api_rgb_right_image_path = gr.File(label="RGB (Right) File (Optional)")
+                    api_rgb_center_image_path = gr.File(label="RGB (Center) File (Optional)")
+                    api_lidar_image_path = gr.File(label="LiDAR File (.npy, Optional)")
+                    api_measurements_path = gr.File(label="Measurements File (.json)")
+                    api_target_point_list = gr.JSON(label="Target Point (List [x, y])", value=[0.0, 100.0])
+                    api_run_button = gr.Button("🚀 تشغيل إطار واحد", variant="primary")
+
+                with gr.Column(scale=2):
                     gr.Markdown("#### المخرجات")
-                    
-                    # <<< بداية التعديل: إضافة مكون لعرض الصورة الناتجة >>>
-                    with gr.Row():
-                        # سيتم عرض لوحة التحكم هنا
-                        api_output_image = gr.Image(label="Dashboard Result", type="pil")
-                        # سيتم عرض بيانات JSON هنا
-                        api_output_json = gr.JSON(label="نتائج النموذج (JSON)")
+                    api_output_image = gr.Image(label="Dashboard Result", type="pil", interactive=False)
+                    api_output_json = gr.JSON(label="نتائج النموذج (JSON)")
         
-       api_run_button.click(
-            fn=run_single_frame,
-            inputs=[
-                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
-            ],
-            # الآن نربط المخرجين ا��لذين تُرجعهما الدالة بالمكونين الصحيحين
-            outputs=[api_output_image, api_output_json],
-            api_name="run_single_frame"
-        )
+            api_run_button.click(
+                fn=run_single_frame,
+                inputs=[
+                    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_output_json],
+                api_name="run_single_frame"
+            )
 
 # ==============================================================================
-#           7. تشغيل التطبيق
+#           5. تشغيل التطبيق
 # ==============================================================================
 if __name__ == "__main__":
     demo.queue().launch(debug=True)