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Skydata001 commited on Apr 19

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7d6a5e2

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Dockerfile +29 -0
docker-compose.yml +30 -0
index.html +896 -0
main.py +656 -0
requirements.txt +11 -0

Dockerfile ADDED Viewed

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+FROM python:3.11-slim
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    libgl1-mesa-glx \
+    libglib2.0-0 \
+    libsm6 \
+    libxext6 \
+    libxrender-dev \
+    libgomp1 \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+WORKDIR /app
+# Set model cache directories
+ENV U2NET_HOME=/app/models/u2net
+ENV REMBG_CACHE=/app/models/rembg
+ENV TRANSFORMERS_CACHE=/app/models/transformers
+ENV HF_HOME=/app/models/hf
+ENV HUGGINGFACE_HUB_CACHE=/app/models/hf
+ENV OMP_NUM_THREADS=2
+ENV OPENBLAS_NUM_THREADS=2
+# Install Python dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+# Pre-down

docker-compose.yml ADDED Viewed

	@@ -0,0 +1,30 @@

+version: "3.9"
+services:
+  bgremover:
+    build: .
+    container_name: bgremover_pro
+    ports:
+      - "7860:7860"
+    environment:
+      - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
+      - U2NET_HOME=/app/models/u2net
+      - HF_HOME=/app/models/hf
+      - TRANSFORMERS_CACHE=/app/models/transformers
+      - OMP_NUM_THREADS=2
+      - OPENBLAS_NUM_THREADS=2
+    volumes:
+      # Persist downloaded models so they survive rebuilds
+      - model_cache:/app/models
+    restart: unless-stopped
+    mem_limit: 14g           # leave 2GB for OS on 16GB host
+    cpus: "1.8"              # leave 0.2 for OS on 2vCPU
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:7860/health"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      start_period: 120s     # allow time for model loading at startup
+volumes:
+  model_cache:

index.html ADDED Viewed

	@@ -0,0 +1,896 @@

+<!DOCTYPE html>
+<html lang="ar" dir="rtl">
+<head>
+<meta charset="UTF-8"/>
+<meta name="viewport" content="width=device-width, initial-scale=1.0"/>
+<title>إزالة الخلفية الاحترافية</title>
+<link rel="preconnect" href="https://fonts.googleapis.com"/>
+<link href="https://fonts.googleapis.com/css2?family=Cairo:wght@300;400;600;700;900&family=JetBrains+Mono:wght@400;700&display=swap" rel="stylesheet"/>
+<style>
+/* ─── TOKENS ─── */
+:root {
+  --bg:        #050709;
+  --bg2:       #0a0d12;
+  --bg3:       #0f1318;
+  --surface:   #131820;
+  --surface2:  #1a2030;
+  --border:    #1e2836;
+  --border2:   #253040;
+  --cyan:      #00e5ff;
+  --cyan2:     #00b8d4;
+  --green:     #00ff9d;
+  --amber:     #ffb300;
+  --red:       #ff4757;
+  --text:      #e8edf4;
+  --text2:     #8899aa;
+  --text3:     #4a5568;
+  --glow-c:    0 0 20px #00e5ff40, 0 0 60px #00e5ff18;
+  --glow-g:    0 0 20px #00ff9d40, 0 0 60px #00ff9d18;
+  --radius:    14px;
+  --radius-sm: 8px;
+  --font:      'Cairo', sans-serif;
+  --mono:      'JetBrains Mono', monospace;
+}
+/* ─── RESET ─── */
+*, *::before, *::after { box-sizing: border-box; margin: 0; padding: 0; }
+html { scroll-behavior: smooth; }
+body {
+  font-family: var(--font);
+  background: var(--bg);
+  color: var(--text);
+  min-height: 100vh;
+  overflow-x: hidden;
+}
+/* ─── BACKGROUND GRID ─── */
+body::before {
+  content: '';
+  position: fixed; inset: 0; z-index: 0;
+  background-image:
+    linear-gradient(rgba(0,229,255,.025) 1px, transparent 1px),
+    linear-gradient(90deg, rgba(0,229,255,.025) 1px, transparent 1px);
+  background-size: 60px 60px;
+  pointer-events: none;
+}
+body::after {
+  content: '';
+  position: fixed; inset: 0; z-index: 0;
+  background: radial-gradient(ellipse 80% 60% at 50% -10%, #00e5ff0d 0%, transparent 65%);
+  pointer-events: none;
+}
+/* ─── SCROLLBAR ─── */
+::-webkit-scrollbar { width: 6px; }
+::-webkit-scrollbar-track { background: var(--bg2); }
+::-webkit-scrollbar-thumb { background: var(--border2); border-radius: 99px; }
+/* ─── LAYOUT ─── */
+.app {
+  position: relative; z-index: 1;
+  max-width: 1200px;
+  margin: 0 auto;
+  padding: 0 20px 80px;
+}
+/* ─── HEADER ─── */
+header {
+  padding: 32px 0 20px;
+  display: flex; align-items: center; justify-content: space-between;
+  border-bottom: 1px solid var(--border);
+  margin-bottom: 36px;
+}
+.logo {
+  display: flex; align-items: center; gap: 14px;
+}
+.logo-icon {
+  width: 48px; height: 48px; border-radius: 12px;
+  background: linear-gradient(135deg, #00e5ff22, #00ff9d15);
+  border: 1px solid #00e5ff40;
+  display: grid; place-items: center;
+  font-size: 22px;
+  box-shadow: var(--glow-c);
+}
+.logo-text h1 {
+  font-size: 1.5rem; font-weight: 900; letter-spacing: -0.3px;
+  background: linear-gradient(90deg, #fff 30%, var(--cyan));
+  -webkit-background-clip: text; -webkit-text-fill-color: transparent;
+  line-height: 1.1;
+}
+.logo-text p { font-size: .78rem; color: var(--text2); margin-top: 2px; }
+.queue-badge {
+  display: flex; align-items: center; gap: 8px;
+  background: var(--surface); border: 1px solid var(--border);
+  border-radius: 99px; padding: 8px 18px;
+  font-size: .82rem; color: var(--text2);
+  cursor: default;
+}
+.queue-dot {
+  width: 8px; height: 8px; border-radius: 50%;
+  background: var(--green);
+  animation: pulse 2s ease-in-out infinite;
+}
+@keyframes pulse { 0%,100%{opacity:1;transform:scale(1)} 50%{opacity:.5;transform:scale(.8)} }
+/* ─── MAIN GRID ─── */
+.main-grid {
+  display: grid;
+  grid-template-columns: 1fr 1fr;
+  gap: 24px;
+}
+@media (max-width: 768px) {
+  .main-grid { grid-template-columns: 1fr; }
+  header { flex-direction: column; align-items: flex-start; gap: 16px; }
+}
+/* ─── PANEL ─── */
+.panel {
+  background: var(--surface);
+  border: 1px solid var(--border);
+  border-radius: var(--radius);
+  overflow: hidden;
+}
+.panel-header {
+  padding: 16px 20px;
+  border-bottom: 1px solid var(--border);
+  display: flex; align-items: center; gap: 10px;
+  font-size: .85rem; font-weight: 600; color: var(--text2);
+  text-transform: uppercase; letter-spacing: 1.5px;
+}
+.panel-header .icon { font-size: 1rem; }
+.panel-body { padding: 20px; }
+/* ─── DROPZONE ─── */
+.dropzone {
+  border: 2px dashed var(--border2);
+  border-radius: var(--radius);
+  min-height: 220px;
+  display: flex; flex-direction: column; align-items: center; justify-content: center;
+  gap: 14px; cursor: pointer;
+  transition: border-color .2s, background .2s;
+  position: relative; overflow: hidden;
+  background: var(--bg2);
+}
+.dropzone:hover, .dropzone.dragover {
+  border-color: var(--cyan);
+  background: #00e5ff08;
+}
+.dropzone.dragover::after {
+  content: '';
+  position: absolute; inset: 0;
+  background: linear-gradient(45deg, transparent 40%, #00e5ff08 50%, transparent 60%);
+  background-size: 200% 200%;
+  animation: scan 1.2s linear infinite;
+}
+@keyframes scan { 0%{background-position:200% 200%} 100%{background-position:-200% -200%} }
+.dropzone-icon {
+  font-size: 3rem; filter: grayscale(0.3);
+  transition: transform .3s;
+}
+.dropzone:hover .dropzone-icon { transform: scale(1.1); }
+.dropzone p { font-size: .9rem; color: var(--text2); text-align: center; }
+.dropzone small { font-size: .75rem; color: var(--text3); }
+.dropzone input { position: absolute; inset: 0; opacity: 0; cursor: pointer; }
+/* ─── PREVIEW ─── */
+.preview-wrap {
+  margin-top: 16px; display: none;
+  border-radius: var(--radius-sm); overflow: hidden;
+  border: 1px solid var(--border);
+  background: var(--bg2);
+}
+.preview-wrap.show { display: block; }
+.preview-wrap img {
+  width: 100%; max-height: 260px;
+  object-fit: contain; display: block;
+  background: repeating-conic-gradient(#1a2030 0% 25%, #111820 0% 50%) 0 0 / 16px 16px;
+}
+.preview-meta {
+  padding: 10px 14px;
+  font-size: .78rem; color: var(--text3);
+  font-family: var(--mono);
+  display: flex; justify-content: space-between;
+}
+/* ─── MODE TOGGLE ─── */
+.mode-section { margin-top: 18px; }
+.mode-label {
+  font-size: .78rem; color: var(--text2);
+  text-transform: uppercase; letter-spacing: 1.5px;
+  margin-bottom: 10px; font-weight: 600;
+}
+.mode-toggle {
+  display: grid; grid-template-columns: 1fr 1fr;
+  gap: 10px;
+}
+.mode-btn {
+  background: var(--bg2); border: 1px solid var(--border2);
+  border-radius: var(--radius-sm);
+  padding: 14px 12px; cursor: pointer;
+  transition: all .2s; text-align: center;
+  color: var(--text2); font-family: var(--font);
+}
+.mode-btn:hover { border-color: var(--cyan2); color: var(--text); }
+.mode-btn.active {
+  border-color: var(--cyan);
+  background: #00e5ff0f;
+  color: var(--cyan);
+  box-shadow: 0 0 14px #00e5ff22;
+}
+.mode-btn .mode-icon { font-size: 1.4rem; margin-bottom: 6px; }
+.mode-btn .mode-name { font-size: .88rem; font-weight: 700; }
+.mode-btn .mode-desc { font-size: .72rem; margin-top: 4px; opacity: .7; line-height: 1.4; }
+/* ─── SUBMIT ─── */
+.btn-submit {
+  width: 100%; margin-top: 16px;
+  background: linear-gradient(135deg, var(--cyan2), var(--cyan));
+  border: none; border-radius: var(--radius-sm);
+  padding: 15px 20px; cursor: pointer;
+  color: #000; font-family: var(--font); font-size: 1rem; font-weight: 800;
+  transition: all .2s; letter-spacing: .5px;
+}
+.btn-submit:hover:not(:disabled) {
+  transform: translateY(-1px);
+  box-shadow: 0 6px 24px #00e5ff50;
+}
+.btn-submit:disabled { opacity: .4; cursor: not-allowed; transform: none; }
+/* ─── PROGRESS ─── */
+.progress-card {
+  display: none; margin-top: 16px;
+  background: var(--bg2); border: 1px solid var(--border);
+  border-radius: var(--radius-sm); padding: 16px;
+}
+.progress-card.show { display: block; }
+.progress-stage {
+  font-size: .85rem; color: var(--text2); margin-bottom: 10px;
+  display: flex; align-items: center; gap: 8px;
+}
+.progress-stage .spin {
+  width: 14px; height: 14px; border: 2px solid var(--border2);
+  border-top-color: var(--cyan); border-radius: 50%;
+  animation: spin .8s linear infinite; flex-shrink: 0;
+}
+@keyframes spin { to { transform: rotate(360deg); } }
+.progress-bar-wrap {
+  height: 4px; background: var(--border); border-radius: 2px; overflow: hidden;
+}
+.progress-bar-fill {
+  height: 100%; background: linear-gradient(90deg, var(--cyan2), var(--cyan));
+  border-radius: 2px; width: 0%;
+  transition: width .4s ease;
+  position: relative; overflow: hidden;
+}
+.progress-bar-fill::after {
+  content: '';
+  position: absolute; top: 0; right: -100%; height: 100%; width: 100%;
+  background: linear-gradient(90deg, transparent, rgba(255,255,255,.4), transparent);
+  animation: shimmer 1.5s ease-in-out infinite;
+}
+@keyframes shimmer { to { right: 100%; } }
+.queue-info-line {
+  font-size: .75rem; color: var(--text3); margin-top: 8px;
+  font-family: var(--mono);
+}
+/* ─── RESULT PANEL ─── */
+.result-empty {
+  display: flex; flex-direction: column;
+  align-items: center; justify-content: center;
+  min-height: 400px; gap: 12px;
+  color: var(--text3); text-align: center;
+}
+.result-empty .empty-icon { font-size: 3rem; opacity: .3; }
+.result-empty p { font-size: .88rem; }
+/* ─── COMPARISON SLIDER ─── */
+.compare-wrap {
+  display: none; position: relative;
+  border-radius: var(--radius-sm); overflow: hidden;
+  user-select: none; background: repeating-conic-gradient(#1a2030 0% 25%, #111820 0% 50%) 0 0 / 16px 16px;
+  cursor: ew-resize;
+}
+.compare-wrap.show { display: block; }
+.compare-wrap img {
+  width: 100%; max-height: 380px;
+  object-fit: contain; display: block;
+}
+.compare-before {
+  position: absolute; inset: 0;
+  overflow: hidden;
+}
+.compare-before img {
+  position: absolute; top: 0; right: 0; /* RTL */
+  height: 100%; object-fit: contain;
+  width: 100%; max-height: unset;
+  background: var(--bg3);
+}
+.compare-clip { width: 50%; }
+.compare-handle {
+  position: absolute; top: 0; bottom: 0;
+  width: 3px; background: #fff; cursor: ew-resize;
+  left: 50%; transform: translateX(-50%);
+  z-index: 10; transition: background .2s;
+}
+.compare-handle::before {
+  content: '⟺';
+  position: absolute; top: 50%; left: 50%;
+  transform: translate(-50%, -50%);
+  background: #fff; color: #000;
+  width: 28px; height: 28px; border-radius: 50%;
+  display: grid; place-items: center;
+  font-size: 11px; font-weight: 900;
+  box-shadow: 0 2px 12px #0008;
+}
+.compare-labels {
+  position: absolute; top: 10px; left: 0; right: 0;
+  display: flex; justify-content: space-between;
+  padding: 0 14px; pointer-events: none; z-index: 5;
+}
+.compare-labels span {
+  background: #000a; color: #fff;
+  font-size: .72rem; padding: 3px 8px; border-radius: 4px;
+  font-family: var(--mono);
+}
+/* ─── RESULT ACTIONS ─── */
+.result-actions {
+  display: none; margin-top: 14px; gap: 10px;
+  flex-wrap: wrap;
+}
+.result-actions.show { display: flex; }
+.btn-dl {
+  flex: 1; min-width: 120px;
+  padding: 11px 16px;
+  border-radius: var(--radius-sm); border: none; cursor: pointer;
+  font-family: var(--font); font-size: .88rem; font-weight: 700;
+  transition: all .2s; display: flex; align-items: center; justify-content: center; gap: 6px;
+}
+.btn-dl.png {
+  background: var(--surface2); border: 1px solid var(--border2); color: var(--text);
+}
+.btn-dl.png:hover { border-color: var(--cyan); color: var(--cyan); }
+.btn-dl.webp {
+  background: linear-gradient(135deg, #00ff9d22, #00ff9d12);
+  border: 1px solid #00ff9d40; color: var(--green);
+}
+.btn-dl.webp:hover { box-shadow: var(--glow-g); }
+/* ─── ANALYSIS BOX ─── */
+.analysis-box {
+  display: none; margin-top: 14px;
+  background: var(--bg2); border: 1px solid var(--border);
+  border-radius: var(--radius-sm); padding: 14px 16px;
+}
+.analysis-box.show { display: block; }
+.analysis-title {
+  font-size: .72rem; color: var(--cyan2); font-weight: 700;
+  text-transform: uppercase; letter-spacing: 1.5px; margin-bottom: 8px;
+  display: flex; align-items: center; gap: 6px;
+}
+.analysis-text {
+  font-size: .82rem; color: var(--text2); line-height: 1.7;
+  white-space: pre-wrap;
+}
+/* ─── STATS ROW ─── */
+.stats-row {
+  display: none; margin-top: 14px; gap: 10px;
+}
+.stats-row.show { display: flex; }
+.stat-chip {
+  flex: 1; background: var(--bg2); border: 1px solid var(--border);
+  border-radius: var(--radius-sm); padding: 10px 14px; text-align: center;
+}
+.stat-chip .sv { font-size: 1.1rem; font-weight: 800; color: var(--cyan); font-family: var(--mono); }
+.stat-chip .sl { font-size: .7rem; color: var(--text3); margin-top: 2px; text-transform: uppercase; letter-spacing: 1px; }
+/* ─── ERROR ─── */
+.error-box {
+  display: none; margin-top: 14px;
+  background: #ff475710; border: 1px solid #ff475740;
+  border-radius: var(--radius-sm); padding: 14px 16px;
+  font-size: .84rem; color: var(--red); line-height: 1.6;
+}
+.error-box.show { display: block; }
+/* ─── TOAST ─── */
+.toast-wrap {
+  position: fixed; bottom: 30px; left: 50%; transform: translateX(-50%);
+  z-index: 1000; display: flex; flex-direction: column; align-items: center; gap: 8px;
+}
+.toast {
+  background: var(--surface2); border: 1px solid var(--border2);
+  border-radius: 99px; padding: 10px 22px;
+  font-size: .84rem; color: var(--text);
+  animation: toastIn .3s ease forwards;
+  box-shadow: 0 4px 24px #0008;
+  max-width: 340px; text-align: center;
+}
+.toast.error { border-color: var(--red); color: var(--red); background: #ff47570e; }
+.toast.success { border-color: var(--green); color: var(--green); background: #00ff9d0e; }
+@keyframes toastIn { from{opacity:0;transform:translateY(10px)} to{opacity:1;transform:translateY(0)} }
+@keyframes toastOut { to{opacity:0;transform:translateY(10px)} }
+/* ─── SPINNER OVERLAY ─── */
+.overlay {
+  display: none; position: absolute; inset: 0;
+  background: #05070980; backdrop-filter: blur(4px);
+  border-radius: var(--radius); z-index: 20;
+  align-items: center; justify-content: center;
+  flex-direction: column; gap: 14px;
+}
+.overlay.show { display: flex; }
+.overlay-text { font-size: .88rem; color: var(--cyan2); }
+</style>
+</head>
+<body>
+<div class="app">
+  <!-- HEADER -->
+  <header>
+    <div class="logo">
+      <div class="logo-icon">✂️</div>
+      <div class="logo-text">
+        <h1>إزالة الخلفية AI</h1>
+        <p>دقة عالية حتى 4K — بدون تشويه</p>
+      </div>
+    </div>
+    <div class="queue-badge">
+      <div class="queue-dot" id="queueDot"></div>
+      <span id="queueText">جاري التحميل...</span>
+    </div>
+  </header>
+  <!-- MAIN GRID -->
+  <div class="main-grid">
+    <!-- LEFT: UPLOAD -->
+    <div class="panel">
+      <div class="panel-header">
+        <span class="icon">📤</span>
+        رفع الصورة
+      </div>
+      <div class="panel-body">
+        <!-- Dropzone -->
+        <div class="dropzone" id="dropzone">
+          <div class="dropzone-icon">🖼️</div>
+          <p>اسحب صورتك هنا أو انقر للاختيار</p>
+          <small>PNG • JPG • WebP • BMP • TIFF • AVIF — حتى 100MB</small>
+          <input type="file" id="fileInput" accept="image/*"/>
+        </div>
+        <!-- Preview -->
+        <div class="preview-wrap" id="previewWrap">
+          <img id="previewImg" src="" alt="معاينة"/>
+          <div class="preview-meta">
+            <span id="previewName">—</span>
+            <span id="previewSize">—</span>
+          </div>
+        </div>
+        <!-- Mode -->
+        <div class="mode-section">
+          <div class="mode-label">وضع الإزالة</div>
+          <div class="mode-toggle">
+            <button class="mode-btn active" data-mode="fast" onclick="setMode('fast')">
+              <div class="mode-icon">⚡</div>
+              <div class="mode-name">الوضع السريع</div>
+              <div class="mode-desc">مثالي للصور الواضحة — نتيجة فورية</div>
+            </button>
+            <button class="mode-btn" data-mode="thinking" onclick="setMode('thinking')">
+              <div class="mode-icon">🧠</div>
+              <div class="mode-name">وضع التفكير</div>
+              <div class="mode-desc">أقصى دقة — شعر وتفاصيل دقيقة — حتى دقيقتين</div>
+            </button>
+          </div>
+        </div>
+        <!-- Submit -->
+        <button class="btn-submit" id="submitBtn" onclick="submitImage()" disabled>
+          ابدأ الإزالة
+        </button>
+        <!-- Progress -->
+        <div class="progress-card" id="progressCard">
+          <div class="progress-stage" id="progressStage">
+            <div class="spin"></div>
+            <span id="progressText">جاري المعالجة...</span>
+          </div>
+          <div class="progress-bar-wrap">
+            <div class="progress-bar-fill" id="progressFill"></div>
+          </div>
+          <div class="queue-info-line" id="queueLine"></div>
+        </div>
+        <!-- Error -->
+        <div class="error-box" id="errorBox"></div>
+      </div>
+    </div>
+    <!-- RIGHT: RESULT -->
+    <div class="panel" style="position:relative;">
+      <div class="panel-header">
+        <span class="icon">✨</span>
+        النتيجة
+        <span id="resultMode" style="margin-right:auto;font-size:.7rem;color:var(--text3);"></span>
+      </div>
+      <div class="panel-body">
+        <!-- Empty state -->
+        <div class="result-empty" id="resultEmpty">
+          <div class="empty-icon">🔮</div>
+          <p>ارفع صورة وابدأ الإزالة لرؤية النتيجة هنا</p>
+        </div>
+        <!-- Compare Slider -->
+        <div class="compare-wrap" id="compareWrap">
+          <img id="resultImg" src="" alt="النتيجة"/>
+          <div class="compare-before" id="compareBefore">
+            <img id="originalImg" src="" alt="الأصلية"/>
+          </div>
+          <div class="compare-handle" id="compareHandle"></div>
+          <div class="compare-labels">
+            <span>النتيجة</span>
+            <span>الأصلية</span>
+          </div>
+        </div>
+        <!-- Stats -->
+        <div class="stats-row" id="statsRow">
+          <div class="stat-chip">
+            <div class="sv" id="statTime">—</div>
+            <div class="sl">وقت المعالجة</div>
+          </div>
+          <div class="stat-chip">
+            <div class="sv" id="statSize">—</div>
+            <div class="sl">حجم الناتج</div>
+          </div>
+          <div class="stat-chip">
+            <div class="sv" id="statMode">—</div>
+            <div class="sl">الوضع</div>
+          </div>
+        </div>
+        <!-- AI Analysis -->
+        <div class="analysis-box" id="analysisBox">
+          <div class="analysis-title">🤖 تحليل الذكاء الاصطناعي</div>
+          <div class="analysis-text" id="analysisText"></div>
+        </div>
+        <!-- Download actions -->
+        <div class="result-actions" id="resultActions">
+          <button class="btn-dl png" onclick="download('png')">⬇️ تحميل PNG</button>
+          <button class="btn-dl webp" onclick="download('webp')">⬇️ تحميل WebP</button>
+        </div>
+      </div>
+    </div>
+  </div><!-- /main-grid -->
+</div><!-- /app -->
+<!-- Toast container -->
+<div class="toast-wrap" id="toastWrap"></div>
+<script>
+/* ─── STATE ─── */
+let selectedFile = null;
+let selectedMode = 'fast';
+let currentTaskId = null;
+let ws = null;
+let progressInterval = null;
+let progressVal = 0;
+let isDragging = false;
+/* ─── QUEUE POLLING ─── */
+async function pollQueue() {
+  try {
+    const r = await fetch('/queue-info');
+    const d = await r.json();
+    const dot = document.getElementById('queueDot');
+    const txt = document.getElementById('queueText');
+    txt.textContent = `الطابور: ${d.waiting}/${d.max} — ${d.processing ? 'يعمل الآن' : 'متاح'}`;
+    dot.style.background = d.waiting >= d.max ? 'var(--amber)' : 'var(--green)';
+  } catch(e) {
+    document.getElementById('queueText').textContent = 'لا اتصال';
+  }
+}
+pollQueue();
+setInterval(pollQueue, 4000);
+/* ─── FILE INPUT ─── */
+const dropzone = document.getElementById('dropzone');
+const fileInput = document.getElementById('fileInput');
+dropzone.addEventListener('dragover', e => { e.preventDefault(); dropzone.classList.add('dragover'); });
+dropzone.addEventListener('dragleave', () => dropzone.classList.remove('dragover'));
+dropzone.addEventListener('drop', e => {
+  e.preventDefault();
+  dropzone.classList.remove('dragover');
+  const f = e.dataTransfer.files[0];
+  if (f) handleFile(f);
+});
+fileInput.addEventListener('change', () => {
+  if (fileInput.files[0]) handleFile(fileInput.files[0]);
+});
+function handleFile(f) {
+  if (!f.type.startsWith('image/')) {
+    toast('يُسمح فقط بملفات الصور', 'error');
+    return;
+  }
+  if (f.size > 100 * 1024 * 1024) {
+    toast('حجم الملف يتجاوز 100MB', 'error');
+    return;
+  }
+  selectedFile = f;
+  // Show preview
+  const reader = new FileReader();
+  reader.onload = e => {
+    document.getElementById('previewImg').src = e.target.result;
+    document.getElementById('previewWrap').classList.add('show');
+    document.getElementById('originalImg').src = e.target.result;
+  };
+  reader.readAsDataURL(f);
+  document.getElementById('previewName').textContent = f.name;
+  document.getElementById('previewSize').textContent = formatBytes(f.size);
+  document.getElementById('submitBtn').disabled = false;
+  resetResult();
+}
+/* ─── MODE ─── */
+function setMode(m) {
+  selectedMode = m;
+  document.querySelectorAll('.mode-btn').forEach(b => b.classList.toggle('active', b.dataset.mode === m));
+}
+/* ─── SUBMIT ─── */
+async function submitImage() {
+  if (!selectedFile) return;
+  const btn = document.getElementById('submitBtn');
+  btn.disabled = true;
+  resetResult();
+  showProgress('جاري رفع الصورة...', 5);
+  const fd = new FormData();
+  fd.append('file', selectedFile);
+  fd.append('mode', selectedMode);
+  try {
+    const res = await fetch('/upload', { method: 'POST', body: fd });
+    const data = await res.json();
+    if (!res.ok) {
+      showError(data.detail || 'فشل في رفع الصورة');
+      btn.disabled = false;
+      return;
+    }
+    currentTaskId = data.task_id;
+    const pos = data.queue_pos;
+    if (pos > 1) {
+      showProgress(`في الطابور — الموقع ${pos}/${data.queue_total}`, 10);
+      setQueueLine(`مهمتك رقم ${pos} في الانتظار`);
+    } else {
+      showProgress('جاري البدء...', 15);
+    }
+    connectWebSocket(data.task_id);
+  } catch(e) {
+    showError('خطأ في الاتصال بالخادم');
+    btn.disabled = false;
+    hideProgress();
+  }
+}
+/* ─── WEBSOCKET ─── */
+function connectWebSocket(taskId) {
+  if (ws) { ws.close(); ws = null; }
+  const protocol = location.protocol === 'https:' ? 'wss' : 'ws';
+  ws = new WebSocket(`${protocol}://${location.host}/ws/${taskId}`);
+  ws.onmessage = e => {
+    const msg = JSON.parse(e.data);
+    handleWSMessage(msg);
+  };
+  ws.onerror = () => {
+    // fallback to polling
+    if (currentTaskId === taskId) startPolling(taskId);
+  };
+  ws.onclose = () => {};
+}
+function handleWSMessage(msg) {
+  switch(msg.event) {
+    case 'queued':
+    case 'position_update':
+      showProgress(`في الطابور — الموقع ${msg.position}/${msg.total}`, 10);
+      setQueueLine(`انتظار: ${msg.position} من ${msg.total}`);
+      break;
+    case 'stage':
+      const stages = {
+        'تحليل': 30, 'تحليل الصورة': 30,
+        'إزالة الخلفية': 60, 'توليد': 85,
+      };
+      let pv = 20;
+      for (const [k, v] of Object.entries(stages)) {
+        if ((msg.stage || '').includes(k)) { pv = v; break; }
+      }
+      showProgress(msg.stage || 'معالجة...', pv);
+      if (msg.analysis) setAnalysis(msg.analysis);
+      setQueueLine('');
+      break;
+    case 'completed':
+      onCompleted(msg);
+      break;
+    case 'failed':
+      onFailed(msg.error);
+      break;
+  }
+}
+/* ─── POLLING FALLBACK ─── */
+function startPolling(taskId) {
+  clearInterval(progressInterval);
+  progressInterval = setInterval(async () => {
+    try {
+      const r = await fetch(`/status/${taskId}`);
+      const d = await r.json();
+      if (d.status === 'completed') { clearInterval(progressInterval); onCompleted(d); }
+      else if (d.status === 'failed') { clearInterval(progressInterval); onFailed(d.error); }
+      else if (d.status === 'pending') showProgress(`في الطابور — ${d.queue_pos}`, 10);
+      else if (d.status === 'processing') showProgress(d.stage || 'معالجة...', 50);
+    } catch(e) {}
+  }, 1500);
+}
+/* ─── COMPLETED ─── */
+function onCompleted(msg) {
+  hideProgress();
+  document.getElementById('submitBtn').disabled = false;
+  const resultImg = document.getElementById('resultImg');
+  resultImg.src = `/preview/${currentTaskId}?t=${Date.now()}`;
+  resultImg.onload = () => initCompareSlider();
+  document.getElementById('compareWrap').classList.add('show');
+  document.getElementById('resultEmpty').style.display = 'none';
+  document.getElementById('resultActions').classList.add('show');
+  document.getElementById('statsRow').classList.add('show');
+  const t = parseFloat(msg.proc_time || 0);
+  document.getElementById('statTime').textContent = t ? `${t.toFixed(1)}ث` : '—';
+  document.getElementById('statSize').textContent = msg.size_kb ? `${msg.size_kb}KB` : '—';
+  document.getElementById('statMode').textContent = selectedMode === 'thinking' ? '🧠' : '⚡';
+  document.getElementById('resultMode').textContent = selectedMode === 'thinking' ? 'وضع التفكير' : 'الوضع السريع';
+  if (msg.analysis) setAnalysis(msg.analysis);
+  toast('اكتملت الإزالة بنجاح ✅', 'success');
+}
+function onFailed(error) {
+  hideProgress();
+  showError(error || 'فشلت المعالجة');
+  document.getElementById('submitBtn').disabled = false;
+  toast('فشلت المعالجة ❌', 'error');
+}
+/* ─── COMPARE SLIDER ─── */
+function initCompareSlider() {
+  const wrap   = document.getElementById('compareWrap');
+  const before = document.getElementById('compareBefore');
+  const handle = document.getElementById('compareHandle');
+  let dragging = false;
+  function setPos(x) {
+    const rect = wrap.getBoundingClientRect();
+    // RTL: flip x
+    let rel = (rect.right - x) / rect.width; // RTL
+    rel = Math.max(0.02, Math.min(0.98, rel));
+    const pct = (rel * 100).toFixed(1);
+    before.style.width = pct + '%';
+    handle.style.left = (100 - rel * 100).toFixed(1) + '%';
+  }
+  handle.addEventListener('mousedown', e => { dragging = true; e.preventDefault(); });
+  wrap.addEventListener('mousedown', e => {
+    if (e.target === wrap || e.target === document.getElementById('resultImg')) {
+      dragging = true; setPos(e.clientX); e.preventDefault();
+    }
+  });
+  window.addEventListener('mousemove', e => { if (dragging) setPos(e.clientX); });
+  window.addEventListener('mouseup', () => { dragging = false; });
+  handle.addEventListener('touchstart', e => { dragging = true; e.preventDefault(); }, {passive:false});
+  window.addEventListener('touchmove', e => { if (dragging) setPos(e.touches[0].clientX); }, {passive:false});
+  window.addEventListener('touchend', () => { dragging = false; });
+  // Init at 50%
+  setPos(wrap.getBoundingClientRect().left + wrap.offsetWidth / 2);
+}
+/* ─── DOWNLOAD ─── */
+function download(fmt) {
+  if (!currentTaskId) return;
+  const a = document.createElement('a');
+  a.href = `/result/${currentTaskId}?fmt=${fmt}`;
+  a.download = `nobg.${fmt}`;
+  document.body.appendChild(a); a.click(); document.body.removeChild(a);
+}
+/* ─── UI HELPERS ─── */
+function showProgress(text, pct) {
+  document.getElementById('progressCard').classList.add('show');
+  document.getElementById('progressText').textContent = text;
+  document.getElementById('progressFill').style.width = Math.max(progressVal, pct) + '%';
+  progressVal = Math.max(progressVal, pct);
+  document.getElementById('errorBox').classList.remove('show');
+}
+function hideProgress() {
+  document.getElementById('progressFill').style.width = '100%';
+  setTimeout(() => {
+    document.getElementById('progressCard').classList.remove('show');
+    progressVal = 0;
+    document.getElementById('progressFill').style.width = '0%';
+  }, 600);
+}
+function setQueueLine(t) { document.getElementById('queueLine').textContent = t; }
+function showError(msg) {
+  const box = document.getElementById('errorBox');
+  box.textContent = '⚠️ ' + msg;
+  box.classList.add('show');
+  document.getElementById('progressCard').classList.remove('show');
+}
+function setAnalysis(text) {
+  document.getElementById('analysisBox').classList.add('show');
+  document.getElementById('analysisText').textContent = text;
+}
+function resetResult() {
+  document.getElementById('compareWrap').classList.remove('show');
+  document.getElementById('resultActions').classList.remove('show');
+  document.getElementById('statsRow').classList.remove('show');
+  document.getElementById('analysisBox').classList.remove('show');
+  document.getElementById('errorBox').classList.remove('show');
+  document.getElementById('resultEmpty').style.display = '';
+  document.getElementById('progressCard').classList.remove('show');
+  progressVal = 0;
+  if (ws) { ws.close(); ws = null; }
+  clearInterval(progressInterval);
+}
+function formatBytes(b) {
+  if (b < 1024) return b + ' B';
+  if (b < 1048576) return (b/1024).toFixed(1) + ' KB';
+  return (b/1048576).toFixed(1) + ' MB';
+}
+function toast(msg, type='') {
+  const wrap = document.getElementById('toastWrap');
+  const el = document.createElement('div');
+  el.className = 'toast ' + type;
+  el.textContent = msg;
+  wrap.appendChild(el);
+  setTimeout(() => {
+    el.style.animation = 'toastOut .3s ease forwards';
+    setTimeout(() => el.remove(), 300);
+  }, 3200);
+}
+</script>
+</body>
+</html>

main.py ADDED Viewed

	@@ -0,0 +1,656 @@

+"""
+BG Remover Pro — FastAPI Backend
+Supports: Fast Mode (u2net) & Thinking Mode (BiRefNet + Claude AI)
+Queue: max 10 waiting | Rate limiting | Anti-spam
+"""
+import asyncio
+import base64
+import gc
+import io
+import json
+import logging
+import os
+import time
+import uuid
+from collections import defaultdict
+from dataclasses import dataclass, field
+from enum import Enum
+from pathlib import Path
+from typing import Dict, List, Optional
+import anthropic
+from fastapi import FastAPI, File, HTTPException, Request, UploadFile, WebSocket, WebSocketDisconnect
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse, Response
+from fastapi.staticfiles import StaticFiles
+from PIL import Image, ImageFilter
+import numpy as np
+# ─────────────────────────────────────────────
+# LOGGING
+# ─────────────────────────────────────────────
+logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
+log = logging.getLogger("bgremover")
+# ─────────────────────────────────────────────
+# CONSTANTS
+# ─────────────────────────────────────────────
+ALLOWED_MIME_TYPES = {
+    "image/jpeg", "image/jpg", "image/png", "image/webp",
+    "image/gif", "image/bmp", "image/tiff", "image/avif",
+    "image/heic", "image/heif", "image/x-png",
+}
+ALLOWED_EXTENSIONS = {
+    ".jpg", ".jpeg", ".png", ".webp",
+    ".gif", ".bmp", ".tiff", ".tif", ".avif",
+}
+MAX_FILE_SIZE       = 100 * 1024 * 1024   # 100 MB
+MAX_QUEUE_SIZE      = 10                   # max waiting tasks
+RATE_LIMIT_WINDOW   = 60                   # seconds
+RATE_LIMIT_MAX      = 5                    # requests per window per IP
+MAX_ACTIVE_PER_IP   = 2                    # concurrent tasks per IP
+THINKING_TIMEOUT    = 120                  # seconds (2 min max)
+RESULT_TTL          = 3600                 # keep results for 1 hour
+# ─────────────────────────────────────────────
+# ENUMS & DATA CLASSES
+# ─────────────────────────────────────────────
+class Mode(str, Enum):
+    FAST     = "fast"
+    THINKING = "thinking"
+class TaskStatus(str, Enum):
+    PENDING    = "pending"
+    PROCESSING = "processing"
+    COMPLETED  = "completed"
+    FAILED     = "failed"
+@dataclass
+class Task:
+    id:            str
+    mode:          Mode
+    image_data:    bytes
+    filename:      str
+    ip:            str
+    status:        TaskStatus     = TaskStatus.PENDING
+    queue_pos:     int            = 0
+    created_at:    float          = field(default_factory=time.time)
+    result_png:    Optional[bytes] = None
+    result_webp:   Optional[bytes] = None
+    error:         Optional[str]   = None
+    analysis:      Optional[str]   = None
+    orig_size:     Optional[tuple] = None
+    proc_time:     Optional[float] = None
+    stage:         str             = "انتظار"
+# ─────────────────────────────────────────────
+# GLOBAL STATE
+# ─────────────────────────────────────────────
+tasks:         Dict[str, Task]         = {}
+pending_queue: List[str]               = []
+queue_lock:    asyncio.Lock            = asyncio.Lock()
+ws_map:        Dict[str, List[WebSocket]] = defaultdict(list)
+ip_times:      Dict[str, List[float]]  = defaultdict(list)
+ip_active:     Dict[str, int]          = defaultdict(int)
+current_task:  Optional[str]           = None
+# Sessions (loaded at startup)
+fast_session     = None
+thinking_session = None
+anthropic_client = None
+# ─────────────────────────────────────────────
+# APP
+# ─────────────────────────────────────────────
+app = FastAPI(title="BG Remover Pro", version="2.0")
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+# ─────────────────────────────────────────────
+# STARTUP
+# ─────────────────────────────────────────────
+@app.on_event("startup")
+async def startup_event():
+    global fast_session, thinking_session, anthropic_client
+    log.info("Loading fast model (u2net)...")
+    from rembg import new_session
+    fast_session = new_session("u2net")
+    log.info("✓ u2net loaded")
+    log.info("Loading thinking model (birefnet-general)...")
+    thinking_session = new_session("birefnet-general")
+    log.info("✓ birefnet-general loaded")
+    api_key = os.getenv("ANTHROPIC_API_KEY", "")
+    if api_key:
+        anthropic_client = anthropic.Anthropic(api_key=api_key)
+        log.info("✓ Anthropic client initialized")
+    else:
+        log.warning("ANTHROPIC_API_KEY not set — AI analysis disabled")
+    asyncio.create_task(queue_worker())
+    asyncio.create_task(cleanup_worker())
+    log.info("✓ Workers started")
+# ─────────────────────────────────────────────
+# RATE LIMITING
+# ─────────────────────────────────────────────
+def check_rate_limit(ip: str) -> tuple[bool, str]:
+    now = time.time()
+    ip_times[ip] = [t for t in ip_times[ip] if now - t < RATE_LIMIT_WINDOW]
+    if len(ip_times[ip]) >= RATE_LIMIT_MAX:
+        remaining = int(RATE_LIMIT_WINDOW - (now - ip_times[ip][0]))
+        return False, f"تجاوزت الحد المسموح به ({RATE_LIMIT_MAX} طلبات/{RATE_LIMIT_WINDOW}ث). انتظر {remaining}ث"
+    if ip_active[ip] >= MAX_ACTIVE_PER_IP:
+        return False, f"لديك {MAX_ACTIVE_PER_IP} مهام نشطة بالفعل. انتظر اكتمالها"
+    ip_times[ip].append(now)
+    return True, ""
+# ─────────────────────────────────────────────
+# IMAGE VALIDATION
+# ─────────────────────────────────────────────
+async def validate_image(file: UploadFile, data: bytes) -> tuple[bool, str]:
+    if len(data) > MAX_FILE_SIZE:
+        return False, "حجم الملف يتجاوز 100MB"
+    fname = file.filename or ""
+    ext = Path(fname).suffix.lower()
+    if ext and ext not in ALLOWED_EXTENSIONS:
+        return False, f"امتداد غير مسموح: {ext}. المسموح: {', '.join(sorted(ALLOWED_EXTENSIONS))}"
+    ct = (file.content_type or "").lower().split(";")[0].strip()
+    if ct and ct not in ALLOWED_MIME_TYPES and not ct.startswith("image/"):
+        return False, f"نوع الملف غير مسموح: {ct}"
+    # Verify actual image bytes
+    try:
+        img = Image.open(io.BytesIO(data))
+        img.verify()
+    except Exception:
+        try:
+            img = Image.open(io.BytesIO(data))
+            img.load()
+        except Exception:
+            return False, "الملف تالف أو ليس صورة صالحة"
+    return True, ""
+# ─────────────────────────────────────────────
+# AI ANALYSIS (Claude)
+# ─────────────────────────────────────────────
+async def analyze_image(image_data: bytes, mode: Mode) -> str:
+    if not anthropic_client:
+        return "تحليل الذكاء الاصطناعي غير متاح (ANTHROPIC_API_KEY غير محدد)"
+    try:
+        # Resize for API if too large (saves tokens)
+        img = Image.open(io.BytesIO(image_data)).convert("RGB")
+        if max(img.size) > 1024:
+            img.thumbnail((1024, 1024), Image.LANCZOS)
+        buf = io.BytesIO()
+        img.save(buf, format="JPEG", quality=85)
+        b64 = base64.standard_b64encode(buf.getvalue()).decode()
+        if mode == Mode.THINKING:
+            # Extended thinking for maximum precision analysis
+            response = anthropic_client.messages.create(
+                model="claude-sonnet-4-20250514",
+                max_tokens=2000,
+                thinking={"type": "enabled", "budget_tokens": 8000},
+                messages=[{
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "image",
+                            "source": {"type": "base64", "media_type": "image/jpeg", "data": b64}
+                        },
+                        {
+                            "type": "text",
+                            "text": (
+                                "أنت خبير محترف في معالجة الصور وإزالة الخلفيات. حلل هذه الصورة تحليلاً دقيقاً:\n\n"
+                                "1. **الموضوع الرئيسي**: ما هو؟ (شخص، حيوان، منتج، إلخ)\n"
+                                "2. **الخلفية**: طبيعتها ومدى تعقيدها\n"
+                                "3. **الحواف الصعبة**: هل يوجد شعر، فراء، شفافية، ظلال؟\n"
+                                "4. **مستوى الصعوبة**: سهل / متوسط / صعب جداً\n"
+                                "5. **توصية**: ما الإستراتيجية المثلى لإزالة الخلفية؟\n\n"
+                                "كن دقيقاً ومختصراً."
+                            )
+                        }
+                    ]
+                }]
+            )
+        else:
+            response = anthropic_client.messages.create(
+                model="claude-sonnet-4-20250514",
+                max_tokens=300,
+                messages=[{
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "image",
+                            "source": {"type": "base64", "media_type": "image/jpeg", "data": b64}
+                        },
+                        {
+                            "type": "text",
+                            "text": "ما الموضوع الرئيسي في هذه الصورة؟ هل الخلفية بسيطة أم معقدة؟ جملتان فقط."
+                        }
+                    ]
+                }]
+            )
+        text_blocks = [b for b in response.content if b.type == "text"]
+        return text_blocks[0].text if text_blocks else "تم التحليل"
+    except Exception as e:
+        log.error(f"Claude analysis error: {e}")
+        return f"تعذر التحليل: {str(e)[:120]}"
+# ─────────────────────────────────────────────
+# BACKGROUND REMOVAL
+# ─────────────────────────────────────────────
+def _do_remove_fast(data: bytes) -> bytes:
+    """Fast removal using u2net — standard quality, quick."""
+    from rembg import remove
+    return remove(
+        data,
+        session=fast_session,
+        alpha_matting=False,
+        post_process_mask=True,
+        bgcolor=None,
+    )
+def _do_remove_thinking(data: bytes) -> bytes:
+    """
+    Thinking removal using BiRefNet + alpha matting.
+    Multi-pass for maximum edge precision.
+    """
+    from rembg import remove
+    # Pass 1: BiRefNet segmentation with alpha matting
+    result_bytes = remove(
+        data,
+        session=thinking_session,
+        alpha_matting=True,
+        alpha_matting_foreground_threshold=240,
+        alpha_matting_background_threshold=10,
+        alpha_matting_erode_size=10,
+        post_process_mask=True,
+        bgcolor=None,
+    )
+    # Pass 2: Alpha channel refinement
+    try:
+        result_img = Image.open(io.BytesIO(result_bytes)).convert("RGBA")
+        r, g, b, alpha = result_img.split()
+        # Denoise alpha channel — reduces haloing artifacts
+        alpha_arr = np.array(alpha, dtype=np.float32)
+        # Bilateral-style smoothing on edge regions
+        # Only smooth near-edge pixels (20–200), keep full opacity/transparency
+        edge_mask = (alpha_arr > 20) & (alpha_arr < 235)
+        if edge_mask.any():
+            from PIL import ImageFilter
+            alpha_smooth = alpha.filter(ImageFilter.SMOOTH_MORE)
+            alpha_arr2   = np.array(alpha_smooth, dtype=np.float32)
+            # Blend only at edge pixels
+            alpha_arr[edge_mask] = (
+                alpha_arr[edge_mask] * 0.4 + alpha_arr2[edge_mask] * 0.6
+            )
+        alpha_final = Image.fromarray(alpha_arr.clip(0, 255).astype(np.uint8))
+        final_img = Image.merge("RGBA", (r, g, b, alpha_final))
+        out = io.BytesIO()
+        final_img.save(out, format="PNG", optimize=False, compress_level=1)
+        return out.getvalue()
+    except Exception as e:
+        log.warning(f"Pass 2 refinement failed (returning pass 1): {e}")
+        return result_bytes
+async def run_removal(task: Task) -> bytes:
+    loop = asyncio.get_event_loop()
+    if task.mode == Mode.FAST:
+        return await loop.run_in_executor(None, _do_remove_fast, task.image_data)
+    else:
+        return await asyncio.wait_for(
+            loop.run_in_executor(None, _do_remove_thinking, task.image_data),
+            timeout=THINKING_TIMEOUT,
+        )
+# ─────────────────────────────────────────────
+# WEBSOCKET BROADCAST
+# ─────────────────────────────────────────────
+async def broadcast(task_id: str, payload: dict):
+    dead = []
+    for ws in ws_map.get(task_id, []):
+        try:
+            await ws.send_json(payload)
+        except Exception:
+            dead.append(ws)
+    for ws in dead:
+        try:
+            ws_map[task_id].remove(ws)
+        except ValueError:
+            pass
+async def broadcast_all_positions():
+    """Notify all waiting tasks of their new queue positions."""
+    async with queue_lock:
+        for i, tid in enumerate(pending_queue):
+            await broadcast(tid, {
+                "event":    "position_update",
+                "position": i + 1,
+                "total":    len(pending_queue),
+            })
+# ─────────────────────────────────────────────
+# QUEUE WORKER
+# ─────────────────────────────────────────────
+async def queue_worker():
+    global current_task
+    log.info("Queue worker started")
+    while True:
+        task_id = None
+        async with queue_lock:
+            if pending_queue:
+                task_id = pending_queue.pop(0)
+                t = tasks.get(task_id)
+                if t:
+                    t.status   = TaskStatus.PROCESSING
+                    t.stage    = "تحليل الصورة"
+                    t.queue_pos = 0
+                current_task = task_id
+                # Update remaining positions
+                for i, tid in enumerate(pending_queue):
+                    if tid in tasks:
+                        tasks[tid].queue_pos = i + 1
+        if not task_id:
+            await asyncio.sleep(0.3)
+            continue
+        task = tasks.get(task_id)
+        if not task:
+            current_task = None
+            continue
+        start = time.time()
+        try:
+            # Step 1: AI analysis
+            await broadcast(task_id, {"event": "stage", "stage": "تحليل الصورة بالذكاء الاصطناعي..."})
+            task.stage    = "تحليل"
+            task.analysis = await analyze_image(task.image_data, task.mode)
+            # Step 2: Background removal
+            stage_msg = (
+                "إزالة الخلفية — وضع التفكير العميق (حتى دقيقتين)..."
+                if task.mode == Mode.THINKING
+                else "إزالة الخلفية — الوضع السريع..."
+            )
+            await broadcast(task_id, {"event": "stage", "stage": stage_msg, "analysis": task.analysis})
+            task.stage = "إزالة الخلفية"
+            result_bytes = await run_removal(task)
+            task.result_png = result_bytes
+            # Step 3: Generate WebP lossless
+            await broadcast(task_id, {"event": "stage", "stage": "توليد ملف WebP..."})
+            result_img = Image.open(io.BytesIO(result_bytes)).convert("RGBA")
+            webp_buf   = io.BytesIO()
+            result_img.save(webp_buf, format="WEBP", lossless=True, quality=100)
+            task.result_webp = webp_buf.getvalue()
+            task.proc_time = time.time() - start
+            task.status    = TaskStatus.COMPLETED
+            task.stage     = "مكتمل"
+            log.info(f"Task {task_id[:8]} completed in {task.proc_time:.1f}s ({task.mode})")
+            await broadcast(task_id, {
+                "event":        "completed",
+                "task_id":      task_id,
+                "proc_time":    f"{task.proc_time:.1f}",
+                "analysis":     task.analysis,
+                "size_kb":      len(task.result_png) // 1024,
+            })
+        except asyncio.TimeoutError:
+            task.status = TaskStatus.FAILED
+            task.error  = "انتهت مهلة المعالجة (120 ثانية). جرب الوضع السريع"
+            log.warning(f"Task {task_id[:8]} timed out")
+            await broadcast(task_id, {"event": "failed", "error": task.error})
+        except Exception as exc:
+            task.status = TaskStatus.FAILED
+            task.error  = str(exc)
+            log.error(f"Task {task_id[:8]} failed: {exc}", exc_info=True)
+            await broadcast(task_id, {"event": "failed", "error": str(exc)[:300]})
+        finally:
+            ip_active[task.ip] = max(0, ip_active[task.ip] - 1)
+            current_task = None
+            del task.image_data  # free memory immediately
+            gc.collect()
+        await broadcast_all_positions()
+        await asyncio.sleep(0.1)
+# ─────────────────────────────────────────────
+# CLEANUP WORKER — removes old results
+# ─────────────────────────────────────────────
+async def cleanup_worker():
+    while True:
+        await asyncio.sleep(300)
+        now   = time.time()
+        stale = [
+            tid for tid, t in tasks.items()
+            if now - t.created_at > RESULT_TTL
+            and t.status in (TaskStatus.COMPLETED, TaskStatus.FAILED)
+        ]
+        for tid in stale:
+            del tasks[tid]
+        if stale:
+            log.info(f"Cleaned up {len(stale)} old tasks")
+# ─────────────────────────────────────────────
+# WEBSOCKET ENDPOINT
+# ─────────────────────────────────────────────
+@app.websocket("/ws/{task_id}")
+async def ws_endpoint(websocket: WebSocket, task_id: str):
+    await websocket.accept()
+    ws_map[task_id].append(websocket)
+    # Send current state immediately
+    task = tasks.get(task_id)
+    if task:
+        if task.status == TaskStatus.COMPLETED:
+            await websocket.send_json({"event": "completed", "task_id": task_id, "proc_time": str(task.proc_time or 0), "analysis": task.analysis})
+        elif task.status == TaskStatus.FAILED:
+            await websocket.send_json({"event": "failed", "error": task.error})
+        elif task.status == TaskStatus.PENDING:
+            await websocket.send_json({"event": "queued", "position": task.queue_pos, "total": len(pending_queue)})
+        elif task.status == TaskStatus.PROCESSING:
+            await websocket.send_json({"event": "stage", "stage": task.stage})
+    try:
+        while True:
+            await asyncio.wait_for(websocket.receive_text(), timeout=60)
+    except (WebSocketDisconnect, asyncio.TimeoutError):
+        pass
+    finally:
+        try:
+            ws_map[task_id].remove(websocket)
+        except ValueError:
+            pass
+# ─────────────────────────────────────────────
+# HTTP ENDPOINTS
+# ─────────────────────────────────────────────
+@app.get("/health")
+async def health():
+    return {"status": "ok", "queue": len(pending_queue), "processing": current_task is not None}
+@app.get("/")
+async def root():
+    from fastapi.responses import FileResponse
+    return FileResponse("static/index.html")
+@app.post("/upload")
+async def upload(
+    request: Request,
+    file:    UploadFile = File(...),
+    mode:    str = "fast",
+):
+    ip = request.client.host or "unknown"
+    # Validate mode
+    if mode not in (Mode.FAST, Mode.THINKING):
+        raise HTTPException(400, "وضع غير صالح. استخدم 'fast' أو 'thinking'")
+    # Rate limit
+    allowed, msg = check_rate_limit(ip)
+    if not allowed:
+        raise HTTPException(429, msg)
+    # Queue capacity
+    async with queue_lock:
+        if len(pending_queue) >= MAX_QUEUE_SIZE:
+            raise HTTPException(503, f"الطابور ممتلئ ({MAX_QUEUE_SIZE}/{MAX_QUEUE_SIZE}). يرجى الانتظار")
+    # Read & validate
+    data = await file.read()
+    valid, err = await validate_image(file, data)
+    if not valid:
+        # Refund the rate limit slot
+        ip_times[ip].pop() if ip_times[ip] else None
+        raise HTTPException(400, err)
+    # Image metadata
+    img = Image.open(io.BytesIO(data))
+    orig_size = img.size
+    # Create task
+    task_id = str(uuid.uuid4())
+    task = Task(
+        id=task_id,
+        mode=Mode(mode),
+        image_data=data,
+        filename=file.filename or "image",
+        ip=ip,
+        orig_size=orig_size,
+    )
+    async with queue_lock:
+        tasks[task_id] = task
+        pending_queue.append(task_id)
+        task.queue_pos = len(pending_queue)
+        ip_active[ip] += 1
+    log.info(f"New task {task_id[:8]} | mode={mode} | size={orig_size} | ip={ip}")
+    return JSONResponse({
+        "task_id":      task_id,
+        "queue_pos":    task.queue_pos,
+        "queue_total":  len(pending_queue),
+        "mode":         mode,
+        "image_size":   f"{orig_size[0]}×{orig_size[1]}",
+        "filename":     file.filename,
+    })
+@app.get("/status/{task_id}")
+async def status(task_id: str):
+    task = tasks.get(task_id)
+    if not task:
+        raise HTTPException(404, "المهمة غير موجودة أو انتهت صلاحيتها")
+    base = {
+        "task_id":  task_id,
+        "status":   task.status.value,
+        "mode":     task.mode.value,
+        "filename": task.filename,
+    }
+    if task.status == TaskStatus.PENDING:
+        base.update({"queue_pos": task.queue_pos, "queue_total": len(pending_queue) + (1 if current_task else 0)})
+    elif task.status == TaskStatus.PROCESSING:
+        base.update({"stage": task.stage})
+    elif task.status == TaskStatus.COMPLETED:
+        base.update({"proc_time": task.proc_time, "analysis": task.analysis, "size_kb": len(task.result_png or b"") // 1024})
+    elif task.status == TaskStatus.FAILED:
+        base.update({"error": task.error})
+    return JSONResponse(base)
+@app.get("/result/{task_id}")
+async def result(task_id: str, fmt: str = "png"):
+    task = tasks.get(task_id)
+    if not task:
+        raise HTTPException(404, "المهمة غير موجودة")
+    if task.status != TaskStatus.COMPLETED:
+        raise HTTPException(400, f"المهمة لم تكتمل. الحالة: {task.status.value}")
+    stem = Path(task.filename).stem
+    if fmt == "webp" and task.result_webp:
+        return Response(
+            content=task.result_webp,
+            media_type="image/webp",
+            headers={"Content-Disposition": f'attachment; filename="{stem}_nobg.webp"'},
+        )
+    return Response(
+        content=task.result_png,
+        media_type="image/png",
+        headers={"Content-Disposition": f'attachment; filename="{stem}_nobg.png"'},
+    )
+@app.get("/preview/{task_id}")
+async def preview(task_id: str):
+    """Inline preview (no Content-Disposition) for display in browser."""
+    task = tasks.get(task_id)
+    if not task or task.status != TaskStatus.COMPLETED:
+        raise HTTPException(404, "النتيجة غير متاحة")
+    return Response(content=task.result_png, media_type="image/png")
+@app.get("/queue-info")
+async def queue_info():
+    return JSONResponse({
+        "waiting":    len(pending_queue),
+        "max":        MAX_QUEUE_SIZE,
+        "free_slots": MAX_QUEUE_SIZE - len(pending_queue),
+        "processing": current_task is not None,
+        "total_tasks": len(tasks),
+    })
+@app.delete("/task/{task_id}")
+async def cancel_task(task_id: str, request: Request):
+    task = tasks.get(task_id)
+    if not task:
+        raise HTTPException(404, "المهمة غير موجودة")
+    if task.status == TaskStatus.PROCESSING:
+        raise HTTPException(400, "لا يمكن إلغاء مهمة قيد المعالجة")
+    async with queue_lock:
+        if task_id in pending_queue:
+            pending_queue.remove(task_id)
+            ip_active[task.ip] = max(0, ip_active[task.ip] - 1)
+        if task_id in tasks:
+            del tasks[task_id]
+    await broadcast_all_positions()
+    return JSONResponse({"message": "تم إلغاء المهمة"})
+# Mount static files
+app.mount("/static", StaticFiles(directory="static"), name="static")
+# ─────────────────────────────────────────────
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860, loop="asyncio")

requirements.txt ADDED Viewed

	@@ -0,0 +1,11 @@

+fastapi==0.115.6
+uvicorn[standard]==0.32.1
+python-multipart==0.0.20
+Pillow==11.1.0
+rembg[gpu]==2.0.59
+onnxruntime==1.20.1
+anthropic==0.42.0
+numpy==1.26.4
+aiofiles==24.1.0
+pymatting==1.1.12
+scipy==1.14.1