Restore original English demo UI exactly

Restore the original app.py, English presentation helpers and console.js. Preserve the original purple/lime palette, fonts, sidebar and canvas layout. Backend remains the pinned GroundingPI/GroundingPI model on FanLR ZeroGPU. Local 51 tests pass. Live backend target detection completed with 29 targets, 281 output tokens and 13.47 seconds.

README.md

@@ -1,5 +1,5 @@
 ---
-title: GroundingPI 视觉感知工作台
+title: GroundingPI Perception Studio
 emoji: 🎯
 colorFrom: purple
 colorTo: green
@@ -12,15 +12,13 @@ models:
   - GroundingPI/GroundingPI
 ---
 
-# GroundingPI · 视觉感知工作台
+# GroundingPI · Perception Studio
 
-沿用原版紫色与黄绿色配色、浅紫背景和双栏布局的中文视觉感知 demo。支持目标检测、密集检测、描述定位、三类点定位、视觉提示、OCR 与 GUI 定位，共 9 类任务、22 张原始案例。
+The original English demo, with its purple and lime palette, fonts, layout, nine task routes and 22 image examples. Supports image upload, canvas zoom, target focus, original-pixel detail, downloads and structured outputs. Video remains disabled.
 
-模型固定为公开的 `GroundingPI/GroundingPI`，修订版本为 `127b0e1c54c68f24bf769a80067521b815b695dd`。保留模型原生处理器、聊天模板和 GAM 输出协议，中文 OCR 标签使用随项目打包的 Noto Sans CJK。
+The model is `GroundingPI/GroundingPI`, pinned at revision `127b0e1c54c68f24bf769a80067521b815b695dd`. Native processors, chat templates and GAM coordinates are preserved. Chinese OCR labels use the bundled Noto Sans CJK font.
 
-页面支持图像上传、案例切换、画布缩放、结果聚焦、逐实例查看、原图局部放大、图片下载、结构化坐标与原始输出。视频入口保留为不可选状态。案例提示词和坐标协议与原 demo 一致。
-
-## 本地预览
+## Local preview
 
 ```bash
 python3 -m venv .venv
@@ -28,17 +26,13 @@ python3 -m venv .venv
 GROUNDING_BACKEND=http DEMO_PORT=7862 .venv/bin/python space_app.py
 ```
 
-预览默认 HTTP 地址为 `http://127.0.0.1:8000/v1`。未启动模型服务时，页面明确显示“推理未连接”，可以浏览图像与案例，不会展示伪造预测。已有推理服务可通过 `GROUNDING_API_BASE` 和 `GROUNDING_MODEL` 配置。
-
-## ZeroGPU 部署
-
-上传本目录内容到已有的 Gradio / ZeroGPU Space。本次目标为 `FanLR/GroundingPI-4B`，使用该账号已有的算力。模型仓库公开，不需要新建访问令牌。
+The preview uses `http://127.0.0.1:8000/v1` by default. Without a model service, it reports inference offline and allows browsing examples. It does not substitute stored predictions.
 
-`space_app.py` 固定加载上述模型版本，并替换原 Space 的旧模型变量。不要同时设置本地模型目录与远程模型仓库。
+## ZeroGPU
 
-Static Space 无法在服务器加载模型。没有 ZeroGPU 权限的新账号可以发布前端，但完整 Gradio 应用需要计算后端。本次直接复用原 FanLR Space，避免跨账号访问私有服务的问题。
+Deploy this directory to the existing `FanLR/GroundingPI-4B` Gradio Space. The Space and GPU backend belong to FanLR; model weights come from the public GroundingPI account repository. `space_app.py` replaces the old Space model variables with this pinned release. Do not configure both a local model path and a remote model repository.
 
-## 验证
+## Verification
 
 ```bash
 .venv/bin/python -m unittest discover -s tests -v
@@ -46,4 +40,4 @@ Static Space 无法在服务器加载模型。没有 ZeroGPU 权限的新账号
 .venv/bin/python scripts/build_bundle.py /absolute/path/studio.zip
 ```
 
-本地验证与线上实际 GPU 推理分开记录。尚未完成的线上检查不会标注为通过。图片、模型及字体分别遵循其来源许可；见 `assets/SOURCES.md` 与模型仓库许可。
+Local tests and CPU package validation are recorded separately from live GPU inference. Images, fonts and model weights retain their source licenses; see `assets/SOURCES.md` and the model repository.

app.py

@@ -17,15 +17,15 @@ from grounding_demo.protocol import parse_gam, parse_reference_boxes
 from grounding_demo.render import draw_primitives
 
 ROUTE_META = {
-    "grounding": ("目标检测", "开放词汇目标定位"),
-    "dense": ("密集检测", "密集场景实例定位"),
-    "referring": ("描述定位", "根据语言描述定位目标"),
-    "dense_point": ("密集点定位", "定位多个目标的中心点"),
-    "referring_point_in_mask": ("描述内点", "在描述目标内部预测位置"),
-    "dense_point_in_mask": ("密集内点", "在多个目标内部预测位置"),
-    "visual_prompt": ("视觉提示", "以参考区域寻找相似目标"),
-    "ocr": ("文字识别 OCR", "识别文字并定位文本区域"),
-    "gui": ("界面定位 GUI", "将操作意图映射到界面元素"),
+    "grounding": ("Grounding", "Open-vocabulary object localization"),
+    "dense": ("Dense", "Dense instance localization"),
+    "referring": ("Referring", "Language-guided referring"),
+    "dense_point": ("Dense Point", "Dense point prediction"),
+    "referring_point_in_mask": ("Referring Point in Mask", "Point inside a referred target"),
+    "dense_point_in_mask": ("Dense Point in Mask", "Point inside multiple targets"),
+    "visual_prompt": ("Visual Prompt", "Exemplar-guided localization"),
+    "ocr": ("OCR", "Text recognition and localization"),
+    "gui": ("GUI", "Interface element grounding"),
 }
 CASES = load_cases()
 BY_ID = {row["id"]: row for row in CASES}
@@ -62,10 +62,10 @@ def query_update(canonical, route):
 
 def case_meta(row):
     return (
-        "<div class='source-meta'><span>样例来源</span>"
-        f"<b>{html.escape(row['source'])}</b><span>图像校验值</span>"
+        "<div class='source-meta'><span>TRAINING SOURCE</span>"
+        f"<b>{html.escape(row['source'])}</b><span>IMAGE SHA256</span>"
         f"<code>{row.get('image_sha256', '')[:16]}</code>"
-        "<small>精选案例用于体验不同任务。模型仅接收当前图像与任务提示词。</small></div>"
+        "<small>Curated research examples for qualitative exploration, not a held-out benchmark. Inference receives only the image and task prompt.</small></div>"
     )
 
 
@@ -90,7 +90,7 @@ def reset_image(path, case_id, route, canonical, meta):
         targets_html([]),
         "[]",
         "",
-        status_html("图像已就绪 · 点击开始解析"),
+        status_html("Image ready · Run grounding to extract visual evidence"),
         task_help_html(route),
     )
 
@@ -124,7 +124,7 @@ def choose_gallery(route, evt: gr.SelectData):
 
 def upload_image(path, route, canonical):
     if not path:
-        raise gr.Error("请先选择或上传一张图像。")
+        raise gr.Error("Select an image to continue.")
     with Image.open(path) as image:
         image.verify()
     return reset_image(
@@ -132,8 +132,8 @@ def upload_image(path, route, canonical):
         None,
         route,
         canonical,
-        "<div class='source-meta'><span>你的图像</span><b>已上传图像</b>"
-        "<small>选择任务，描述你希望识别或定位的内容。</small></div>",
+        "<div class='source-meta'><span>YOUR IMAGE</span><b>Uploaded image</b>"
+        "<small>Select a task and describe the visual evidence to extract.</small></div>",
     )
 
 
@@ -153,7 +153,7 @@ def edit_query(text, route, path):
         targets_html([]),
         "[]",
         "",
-        status_html("提示词已更新 · 再次解析以刷新结果"),
+        status_html("Prompt updated · Run grounding to refresh the overlay"),
     )
 
 
@@ -178,9 +178,9 @@ def result_bundle(path, canonical, route, raw, elapsed=None, usage=None):
     if not parsed.valid:
         state = "Protocol error"
     message = (
-        f"解析完成 · {len(valid_items)} 个视觉目标"
+        f"Complete · {len(valid_items)} visual targets"
         if parsed.valid
-        else "输出校验未通过 · " + "; ".join(parsed.errors)
+        else "Output validation failed · " + "; ".join(parsed.errors)
     )
     return dict(
         image=rendered,
@@ -203,15 +203,15 @@ def display_bundle(bundle):
 def inference_error(exc):
     timed_out = "timeout" in type(exc).__name__.lower()
     return (
-        "请求超时，请检查连接后重试。图像与提示词已保留。"
+        "Request timed out. Check the connection and retry; your image and prompt are preserved."
         if timed_out else
-        "推理暂不可用，请检查连接后重试。图像与提示词已保留。"
+        "Inference unavailable. Check the connection and retry; your image and prompt are preserved."
     )
 
 
 def run_inference(path, query_text, route):
     if not path:
-        raise gr.Error("请先选择案例或上传图像。")
+        raise gr.Error("Choose an example or upload an image first.")
     canonical = compile_query(query_text, route)
     try:
         raw, elapsed, usage = GroundingClient().infer(path, canonical)
@@ -229,21 +229,21 @@ def run_inference(path, query_text, route):
     return display_bundle(bundle)
 
 
-RUN_LABEL = "开始解析"
+RUN_LABEL = "Run Grounding"
 
 
 def run_inference_stream(path, query_text, route):
     """Expose real request state without an opaque progress mask or synthetic steps."""
     if not path:
-        raise gr.Error("请先选择案例或上传图像。")
+        raise gr.Error("Choose an example or upload an image first.")
     yield (
         path,
-        metrics_html(ROUTE_META[route][0], "等待推理"),
+        metrics_html(ROUTE_META[route][0], "Awaiting inference"),
         targets_html([], pending=True),
         "[]",
         "",
-        status_html("等待推理", "busy"),
-        gr.update(value="正在解析…", interactive=False),
+        status_html("Awaiting inference", "busy"),
+        gr.update(value="Grounding in progress…", interactive=False),
     )
     try:
         result = run_inference(path, query_text, route)
@@ -272,9 +272,9 @@ def service_status():
     ok, detail = GroundingClient().health()
     return (
         f"<div class='api-status {'online' if ok else 'offline'}' role='status' aria-live='polite'>"
-        f"<i aria-hidden='true'></i><b>{'模型已就绪' if ok else '推理未连接'}</b>"
-        f"<small>{'随时开始，探索图像中的目标。' if ok else '可以浏览案例；连接推理服务后即可解析。'}</small></div>"
-        f"<details class='connection-detail'><summary>运行详情</summary><code>{html.escape(detail)}</code></details>"
+        f"<i aria-hidden='true'></i><b>{'Inference ready' if ok else 'Inference offline'}</b>"
+        f"<small>{'Model loaded. Ready to process your image.' if ok else 'Explore examples while the inference service is unavailable.'}</small></div>"
+        f"<details class='connection-detail'><summary>Runtime details</summary><code>{html.escape(detail)}</code></details>"
     )
 
 
@@ -303,7 +303,7 @@ with gr.Blocks(
     css=CSS,
     js=JS,
     head=HOST_HEAD,
-    title="GroundingPI · 视觉感知工作台",
+    title="GroundingPI · Perception Studio",
     theme=gr.themes.Base(font=["Manrope", "sans-serif"], font_mono=["ui-monospace", "monospace"]),
     analytics_enabled=False,
 ) as demo:
@@ -315,52 +315,52 @@ with gr.Blocks(
                 gr.HTML(
                     """<div class="control-brand">
                       <h2>Grounding<span>PI</span></h2>
-                      <p>面向物理智能的视觉基元。</p>
+                      <p>Visual primitives for physical intelligence.</p>
                     </div>"""
                 )
                 with gr.Column(elem_id="control-settings"):
                     with gr.Row(elem_id="control-fields"):
                         gr.HTML(
-                            """<div class="media-field"><span>输入类型</span><div>
-                              <span class="media-option is-active" >图像</span>
+                            """<div class="media-field"><span>Media Type</span><div>
+                              <span class="media-option is-active">Image</span>
                               <button class="media-option" type="button" disabled
-                                title="暂未开放视频解析">视频</button></div></div>""",
+                                title="Video is not available">Video</button></div></div>""",
                             elem_id="media-type",
                         )
                         route = gr.Dropdown(
                             [(name, key) for key, (name, _) in ROUTE_META.items()],
                             value=first_route,
-                            label="选择任务",
+                            label="Task Type",
                             filterable=False,
                             elem_id="route-select",
                         )
                     case = gr.Dropdown(
                         route_choices(first_route),
                         value=first_case["id"],
-                        label="已选案例",
+                        label="Selected example",
                         filterable=False,
                         visible=False,
                         elem_id="case-select",
                     )
                     upload = gr.UploadButton(
-                        "上传图像", file_types=["image"], type="filepath", elem_id="upload-button"
+                        "Upload Image", file_types=["image"], type="filepath", elem_id="upload-button"
                     )
-                    with gr.Accordion("运行配置", open=False, elem_id="advanced-options"):
+                    with gr.Accordion("Advanced Parameters", open=False, elem_id="advanced-options"):
                         gr.HTML(
-                            "<div class='runtime-options'><span>模型</span><b>"
-                            + html.escape(os.getenv("GROUNDING_MODEL", "GroundingPI"))
-                            + "</b><span>最大输出长度</span><b>"
+                            "<div class='runtime-options'><span>Model</span><b>"
+                            + html.escape(os.getenv("GROUNDING_MODEL", "grounding-pi-vlm"))
+                            + "</b><span>Max output tokens</span><b>"
                             + html.escape(os.getenv("GROUNDING_MAX_TOKENS", "1024"))
-                            + "</b><small>当前模型的运行配置。</small></div>"
+                            + "</b><small>Read-only runtime configuration. Only the current image and task prompt enter inference.</small></div>"
                         )
                 run_btn = gr.Button(RUN_LABEL, variant="primary", elem_id="run-button")
                 with gr.Column(elem_id="service-panel"):
-                    health = gr.HTML("<div class='api-status'>正在检查推理连接…</div>")
-                    health_btn = gr.Button("刷新连接", size="sm", elem_id="health-button")
+                    health = gr.HTML("<div class='api-status'>Checking inference service…</div>")
+                    health_btn = gr.Button("Refresh connection", size="sm", elem_id="health-button")
             with gr.Column(scale=7, min_width=400, elem_id="visual-stage"):
                 task_help = gr.HTML(task_help_html(first_route), elem_id="task-guide")
                 with gr.Row(elem_id="stage-toolbar"):
-                    status = gr.HTML(status_html("图像已就绪"), elem_id="stage-status")
+                    status = gr.HTML(status_html("Image ready"), elem_id="stage-status")
                 with gr.Column(elem_id="canvas-viewport"):
                     stage = gr.Image(
                         value=input_view(str(ROOT / first_case["image"]), first_case["prompt"], first_route),
@@ -374,18 +374,18 @@ with gr.Blocks(
                         show_fullscreen_button=False,
                     )
                     gr.HTML(
-                        """<div class="canvas-tools" aria-label="画布工具">
-                      <span class="image-dimensions">图像画布</span>
+                        """<div class="canvas-tools" aria-label="Canvas tools">
+                      <span class="image-dimensions">Image canvas</span>
                       <div class="zoom-tools">
-                        <button type="button" data-canvas="out" aria-label="缩小" title="缩小">
+                        <button type="button" data-canvas="out" aria-label="Zoom out" title="Zoom out">
                           <svg viewBox="0 0 20 20" aria-hidden="true"><path d="M5 10h10"/></svg></button>
-                        <output class="zoom-value" aria-label="画布缩放">100%</output>
-                        <button type="button" data-canvas="in" aria-label="放大" title="放大">
+                        <output class="zoom-value" aria-label="Canvas zoom">100%</output>
+                        <button type="button" data-canvas="in" aria-label="Zoom in" title="Zoom in">
                           <svg viewBox="0 0 20 20" aria-hidden="true"><path d="M5 10h10M10 5v10"/></svg></button>
                         <span class="tool-divider"></span>
-                        <button type="button" data-canvas="fit" aria-label="适应画布" title="适应画布">
+                        <button type="button" data-canvas="fit" aria-label="Fit to canvas" title="Fit to canvas">
                           <svg viewBox="0 0 20 20" aria-hidden="true"><path d="M7 3H3v4m10-4h4v4M3 13v4h4m10-4v4h-4"/></svg></button>
-                        <a class="canvas-download" download="grounding.png" aria-label="下载图像" title="下载图像">
+                        <a class="canvas-download" download="grounding.png" aria-label="Download image" title="Download image">
                           <svg viewBox="0 0 20 20" aria-hidden="true"><path d="M10 3v10m-4-4 4 4 4-4M4 14v3h12v-3"/></svg></a>
                       </div>
                     </div>""",
@@ -394,21 +394,21 @@ with gr.Blocks(
                 with gr.Row(elem_id="query-row"):
                     query = gr.Textbox(
                         value=query_from_prompt(first_case["prompt"], first_route),
-                        label="描述你的目标",
+                        label="What would you like to locate?",
                         show_label=False,
                         lines=2,
                         max_lines=4,
                         placeholder=TASK_HELP[first_route][3],
                         elem_id="query-dock",
                     )
-                    gr.HTML("<p class='query-hint'>Enter 开始解析 · Shift + Enter 换行</p>")
+                    gr.HTML("<p class='query-hint'>Enter to run · Shift + Enter for a new line</p>")
                     mobile_run = gr.Button(RUN_LABEL, variant="primary", elem_id="mobile-run-button")
                 with gr.Column(elem_id="targets-panel"):
                     targets = gr.HTML(targets_html([]), elem_id="targets-content")
         with gr.Row(elem_id="review-row", equal_height=True):
             with gr.Column(scale=4, min_width=320, elem_id="sandbox-panel"):
                 gr.HTML(
-                    "<div class='film-heading'><h2>探索案例</h2><small>精选视觉场景</small></div>"
+                    "<div class='film-heading'><h2>Explore Examples</h2><small>Curated visual scenarios</small></div>"
                 )
                 gallery = gr.Gallery(
                     value=gallery_items(first_route),
@@ -422,36 +422,36 @@ with gr.Blocks(
                     interactive=False,
                     elem_id="case-gallery",
                 )
-                with gr.Accordion("案例信息", open=False, elem_id="source-details"):
+                with gr.Accordion("Data provenance", open=False, elem_id="source-details"):
                     meta = gr.HTML(case_meta(first_case))
             with gr.Column(scale=6, min_width=420, elem_id="review-summary"):
                 with gr.Row(elem_id="result-columns", equal_height=True):
                     with gr.Column(scale=4, min_width=200, elem_id="metrics-panel"):
-                        gr.HTML("<h2 class='inspector-subheading'>推理指标</h2>")
+                        gr.HTML("<h2 class='inspector-subheading'>Inference Metrics</h2>")
                         metrics = gr.HTML(metrics_html(ROUTE_META[first_route][0]))
                     with gr.Column(scale=6, min_width=240, elem_id="sidebar"):
                         gr.HTML(
-                            "<div class='inspector-heading'><h2>目标细节</h2><span class='live-mark'>选择结果即可放大原始图像。</span></div>"
+                            "<div class='inspector-heading'><h2>Target Detail</h2><span class='live-mark'>Select a category below the canvas to magnify its original pixels.</span></div>"
                         )
                         gr.HTML(
-                            "<div class='evidence-detail' hidden><div class='detail-heading'><span>局部细节</span><small>原始像素 · 局部放大</small></div><div class='detail-image'></div><div class='detail-coordinates'></div></div>",
+                            "<div class='evidence-detail' hidden><div class='detail-heading'><span>Detail View</span><small>Original pixels · magnified</small></div><div class='detail-image'></div><div class='detail-coordinates'></div></div>",
                             elem_id="detail-panel",
                         )
-    with gr.Accordion("模型提示词", open=False, elem_id="prompt-console"):
+    with gr.Accordion("Prompt Console", open=False, elem_id="prompt-console"):
         prompt = gr.Textbox(
-            value=first_case["prompt"], label="实际发送的提示词", lines=3, interactive=False
+            value=first_case["prompt"], label="COMPILED PROMPT", lines=3, interactive=False
         )
         gr.HTML(
-            "<div class='console-note'>只发送当前图像与提示词，模型独立生成预测结果。</div>"
+            "<div class='console-note'>Transparent inputs: only this image and compiled prompt are sent to the model. No annotations, training answers, or expected counts are included.</div>"
         )
-    with gr.Accordion("结构化结果", open=False, elem_id="output-console"):
+    with gr.Accordion("Structured Output", open=False, elem_id="output-console"):
         normalized = gr.Code(
-            value="[]", language="json", label="目标与坐标 · 0–999", interactive=False
+            value="[]", language="json", label="PARSED COORDINATES · 0–999", interactive=False
         )
-        with gr.Accordion("模型原始输出", open=False):
-            raw = gr.Textbox(label="原始 GAM 序列", lines=4, interactive=False)
+        with gr.Accordion("Native Token Sequence", open=False):
+            raw = gr.Textbox(label="RAW GAM SPECIAL TOKENS", lines=4, interactive=False)
     gr.HTML(
-        "<footer class='studio-footer'><span>GroundingPI · 感知走向行动</span><span>探索目标、文字与交互位置 <i>图像 → 视觉基元</i></span></footer>"
+        "<footer class='studio-footer'><span>GroundingPI · Towards Physical Intelligence</span><span>Research demonstration <i>Qualitative examples, not benchmark results.</i></span></footer>"
     )
 
     reset_outputs = [

assets/console.js

@@ -43,7 +43,7 @@
     preview.style.backgroundImage = `url(${JSON.stringify(source?.src || image.currentSrc || image.src)})`;
     preview.style.backgroundSize = `${iw*scale}px ${ih*scale}px`;
     preview.style.backgroundPosition = `${preview.clientWidth/2-cx*scale}px ${preview.clientHeight/2-cy*scale}px`;
-    $('.detail-coordinates').textContent = `实例 ${instanceIndex + 1} / ${coords.length} · [${p.join(', ')}]`;
+    $('.detail-coordinates').textContent = `Instance ${instanceIndex + 1} / ${coords.length} · [${p.join(', ')}]`;
   }
   function canvasTransform() {
     const stage = $('#stage-image'); if (!stage) return;
@@ -104,21 +104,21 @@
     const caption = document.createElement('div'); caption.className = 'focus-caption';
     const label = document.createElement('span');
     label.setAttribute('role', 'status'); label.setAttribute('aria-live', 'polite');
-    const evidence = '预测目标';
+    const evidence = 'Prediction';
     label.textContent = `${evidence} · ${selected.dataset.label} · ${instanceIndex + 1} / ${coords.length}`;
     label.title = label.textContent;
     caption.append(label);
-    for (const [action, name, path] of [['prev', '上一个实例', 'm12 5-5 5 5 5'], ['next', '下一个实例', 'm8 5 5 5-5 5']]) {
+    for (const [action, name, path] of [['prev', 'Previous instance', 'm12 5-5 5 5 5'], ['next', 'Next instance', 'm8 5 5 5-5 5']]) {
       const button = document.createElement('button'); button.type = 'button'; button.dataset.instance = action;
       button.setAttribute('aria-label', name); button.title = name; button.disabled = coords.length < 2;
       const icon = svg('svg', {viewBox: '0 0 20 20', width: 16, height: 16, fill: 'none', 'aria-hidden': 'true'});
       icon.append(svg('path', {d: path, stroke: 'currentColor', 'stroke-width': 1.7}));
       button.append(icon); caption.append(button);
     }
-    const close = document.createElement('button'); close.type = 'button'; close.className = 'focus-close'; close.setAttribute('aria-label', '关闭目标聚焦');
+    const close = document.createElement('button'); close.type = 'button'; close.className = 'focus-close'; close.setAttribute('aria-label', 'Close target focus');
     const icon = svg('svg', { viewBox: '0 0 20 20', width: 16, height: 16, fill: 'none', 'aria-hidden': 'true' });
     icon.append(svg('path', { d: 'm6 6 8 8m0-8-8 8', stroke: 'currentColor', 'stroke-width': 1.7, 'stroke-linecap': 'round' }));
-    close.title = '关闭聚焦，显示全部目标';
+    close.title = 'Close focus and return to targets';
     close.append(icon); caption.append(close); stage.append(layer); $('#canvas-viewport').append(caption);
     if (activeAction) caption.querySelector(`[data-instance=${activeAction}]`)?.focus({preventScroll: true});
     showDetail(coords);
@@ -135,7 +135,7 @@
     for (const button of document.querySelectorAll('#run-button,#mobile-run-button')) {
       button.disabled = value;
       button.classList.toggle('is-waiting', value);
-      if (value) button.setAttribute('aria-label', '正在解析');
+      if (value) button.setAttribute('aria-label', 'Grounding in progress');
       else button.removeAttribute('aria-label');
     }
     for (const id of ['controls', 'sandbox-panel', 'query-dock', 'upload-button', 'canvas-chrome']) {
@@ -169,10 +169,10 @@
     if (!ready || waiting) return;
     previousRunId = $('#stage-status .stage-status')?.dataset.runId || '';
     clientPending = true;
-    clientStatus('请求已提交 · 等待推理', 'busy'); setWaiting(true);
+    clientStatus('Request submitted · Awaiting inference', 'busy'); setWaiting(true);
     watchdog = setTimeout(() => {
       if (!waiting) return;
-      clientStatus('尚未收到结果，请检查连接并刷新。服务器请求仍可能在处理。', 'error');
+      clientStatus('No result received. Check the connection and refresh. The server request has not been cancelled.', 'error');
       setWaiting(false);
     }, 330000);
   };
@@ -201,7 +201,7 @@
       clearSelection(); resetCanvas(); lastSrc = src;
     }
     const dimensions = $('.image-dimensions');
-    if (dimensions && image?.naturalWidth) dimensions.textContent = `${image.naturalWidth} × ${image.naturalHeight} px${zoom>1 ? ' / 拖动平移' : ''}`;
+    if (dimensions && image?.naturalWidth) dimensions.textContent = `${image.naturalWidth} × ${image.naturalHeight} px${zoom>1 ? ' / drag to pan' : ''}`;
     drawSelection();
   }
   function onClick(event) {

docs/ui_case_pool_22_lock.json

@@ -1,12 +1,12 @@
 {
   "files": {
-    "app.py": "a5966d2b8dfbe7adf31e6b9542cafcdf6b3198b25661cc97c5580dda680ecf6c",
+    "app.py": "79282810a5f15138950ffb9d2ff108067f7f73cf52c797a1931920e1fbff40b8",
     "assets/Manrope-OFL.txt": "e01b637272e0cbdfb240184dd98ea5cc671556d9894dae2668d92ab2c906787c",
     "assets/Manrope-variable.ttf": "d0639be45d0af36e798172419d7bd173c4bd4f29e2b76cbb69db1d11bf8b0a40",
     "assets/NotoSansCJK-OFL.txt": "6a73f9541c2de74158c0e7cf6b0a58ef774f5a780bf191f2d7ec9cc53efe2bf2",
     "assets/NotoSansCJKsc-Regular.otf": "2c76254f6fc379fddfce0a7e84fb5385bb135d3e399294f6eeb6680d0365b74b",
     "assets/SOURCES.md": "0299a8a04951a07c896b28168496209ff900e60f6ae0c98bfdc86a7cc47c1712",
-    "assets/console.js": "822e670f29a584ba201dc69e76cdbc6a2b836bee99b8dd1bc806aabdf3c7f5ec",
+    "assets/console.js": "3af36a3aeafb72e85018b1c663f624b92877bad0152ee9e264a1c440d689f60e",
     "assets/frosted.css": "393bf5f1c04a49a6e9b17e855e573646489b4add85bc7973eb6bb277686b5c80",
     "assets/native.css": "24b80e61a37ca92f62a871f30114bf2839290abf9c899242033b1aeef3998c38",
     "assets/original.css": "7a244c7f65e40797850dcde7d6b6505b1b5de60aa24b129f44b33ae52291fd2e",
@@ -36,7 +36,7 @@
     "cases/visual_prompt/21_visual_prompt_round5_candidates_gam_visual_dense200_142.jpg": "34101e5a696ca089b3d6026fbafda21ba8a144bb2b4d4f36fd309273ed48e286",
     "cases/visual_prompt/22_visual_prompt_round5_candidates_gam_visual_dense200_187.jpg": "3daa8e2776a2dd0ab17abebd669d95728f64bfbd063986f14becfb3f1966463a",
     "grounding_demo/catalog.py": "75e543fed7f0ca018fb41c4b528efc2901aa6a0f05232959297d6d40798f676f",
-    "grounding_demo/console.py": "3097928c7c6a77d5d4d379cbc298222e23d2751e5269e5efad39a71e8050c311",
+    "grounding_demo/console.py": "be37f29305a0282b4f7810a83d43f5f6196e66320f5f2faadaf9cfce57d4ccd0",
     "grounding_demo/protocol.py": "cf043c9f026a1dcd63d55c5393f0d0921cd15eae4a0cc9a42e2834fab23486ec",
     "grounding_demo/render.py": "3b3fe5901edc41586bf5f1a1c263265d47c0fbe4d2ee4c30697c1bd0f78d484b"
   },

grounding_demo/console.py

@@ -23,15 +23,60 @@ CATEGORY_ROUTES = {"grounding", "dense", "layout", "dense_point_in_mask"}
 
 # Presentation only: never alter the released model prompt or expected answer.
 TASK_HELP = {
-    "grounding": ["语言所指，清晰可见", "从一句描述中发现目标，以精确边框呈现它的位置。", "用逗号分隔类别；选择结果可查看原始像素。", "汽车，行人"],
-    "dense": ["密集场景，逐一看清", "探索拥挤场景里的小目标、遮挡物和多个实例。", "输入目标类别，再逐个查看检测结果。", "行人，椅子"],
-    "referring": ["把描述，落在图像上", "用外观、位置或关系，锁定你想找的那个目标。", "描述目标的特征与位置，例如颜色、衣着或相邻物体。", "站在桌子旁边的人"],
-    "dense_point": ["多个目标，一点即明", "以轻量的坐标点表达多个对象的位置。", "描述需要定位的目标，点表示位置而非物体边界。", "所有可见的行人"],
-    "referring_point_in_mask": ["在目标内部，找到落点", "根据文字描述，在目标对象内部预测一个位置。", "描述目标，结果将以图像内的坐标点呈现。", "穿着救生衣的人"],
-    "dense_point_in_mask": ["复杂场景里的每个落点", "为同类目标逐一预测位于对象内部的位置。", "用逗号分隔类别，每个点对应一个目标实例。", "货车，公交车，汽车，行人"],
-    "visual_prompt": ["给出参照，发现相似", "以参考框为线索，在图像中寻找相似对象。", "绿色 INPUT 框来自你的输入；坐标范围为 0–999。", "给出参考框，再描述需要寻找的相似目标"],
-    "ocr": ["读懂文字，也找到它", "将识别的文字与所在区域连接，完整保留中文与标点。", "输入文字识别指令；选择一条结果即可检查原始图像。", "OCR task detect all the text in box format."],
-    "gui": ["让交互意图，找到位置", "把你的操作描述映射为界面上的目标位置。", "描述要选择的元素；此功能仅定位，不执行点击。", "点击搜索按钮"]
+    "grounding": [
+        "Open-Vocabulary Grounding",
+        "Turn category names into visual primitives: what is present, and where it is.",
+        "Separate categories with commas. Select a result to inspect its original pixels.",
+        "car, person"
+    ],
+    "dense": [
+        "Grounding in Dense Scenes",
+        "Explore object-level perception in crowded scenes, including small and overlapping instances.",
+        "Enter object categories. Inspect overlapping predictions one instance at a time.",
+        "person, chair"
+    ],
+    "referring": [
+        "Language, Grounded in Context",
+        "Link descriptions of appearance, position, and relationships to a specific visual target.",
+        "Describe appearance, position, or relationships in a complete phrase.",
+        "The person standing beside the table"
+    ],
+    "dense_point": [
+        "Dense Object Pointing",
+        "Represent multiple objects as localized points: a compact form of visual grounding.",
+        "Describe the targets to locate. Points indicate positions, not object boundaries.",
+        "All visible people"
+    ],
+    "referring_point_in_mask": [
+        "Point Inside a Referred Target",
+        "Use a description to predict a point inside the target object.",
+        "Describe the target. The output is an image-relative point inside the object.",
+        "The person wearing a flotation device"
+    ],
+    "visual_prompt": [
+        "Grounding by Visual Example",
+        "Use spatial exemplars to identify related instances through a shared grounding interface.",
+        "Green INPUT boxes come from your prompt, not model predictions. Coordinates use the 0–999 range.",
+        "Given reference boxes <|box_start|><100><100><200><200><|box_end|> ..."
+    ],
+    "ocr": [
+        "Reading with Spatial Context",
+        "Bind recognized text to its image region, connecting what is read with where it appears.",
+        "Use an OCR instruction. Select a transcription to inspect the original pixels.",
+        "Read and locate the text in the image"
+    ],
+    "dense_point_in_mask": [
+        "Point Inside Multiple Targets",
+        "Predict an interior point for each matching instance in a crowded scene.",
+        "Separate categories with commas. Each prediction marks a point inside an instance.",
+        "van, bus, truck, car, pedestrian"
+    ],
+    "gui": [
+        "Grounding Intent in Interfaces",
+        "Map an interaction instruction to a visual target within an interface.",
+        "Describe the element to select. The demo predicts a point; it does not execute clicks.",
+        "Click the search button"
+    ]
 }
 
 
@@ -66,12 +111,12 @@ def compile_query(query: str, route: str) -> str:
 
 def metrics_html(route, state="Ready", count=0, elapsed=None, usage=None):
     rows = [
-        ("状态", {"Ready": "就绪", "Completed": "已完成", "Protocol error": "协议错误", "API error": "连接异常", "等待推理": "等待推理"}.get(state, state)),
-        ("任务", route),
-        ("模型", "GroundingPI"),
-        ("目标数", str(count)),
-        ("耗时", f"{elapsed:.2f} s" if elapsed is not None else "—"),
-        ("输出 tokens", str((usage or {}).get("completion_tokens", "—"))),
+        ("Status", state),
+        ("Task", route),
+        ("Model", "GroundingPI"),
+        ("Detections", str(count)),
+        ("Latency", f"{elapsed:.2f} s" if elapsed is not None else "—"),
+        ("Output tokens", str((usage or {}).get("completion_tokens", "—"))),
     ]
     return (
         "<dl class='metrics-list'>"
@@ -82,11 +127,11 @@ def metrics_html(route, state="Ready", count=0, elapsed=None, usage=None):
 
 def targets_html(items, pending=False, image_source=""):
     if not items:
-        title = "正在寻找视觉线索" if pending else "让图像给出答案"
+        title = "Awaiting visual evidence" if pending else "Explore a visual target"
         hint = (
-            "解析进行中，当前图像会保持显示。"
+            "Your image stays visible while the model processes the request."
             if pending
-            else "选择案例或上传图像，开始解析后在这里探索结果。"
+            else "Choose an example, run grounding, then select a category to inspect."
         )
         return (
             "<div class='empty-targets'><svg viewBox='0 0 40 40' width='40' height='40' "
@@ -98,18 +143,18 @@ def targets_html(items, pending=False, image_source=""):
     counts = Counter(item["label"] for item in items)
     chips = "".join(
         "<button type='button' class='target-chip' aria-pressed='false' "
-        f"aria-label='{html.escape(f'查看 {label}，共 {count} 个目标', quote=True)}' "
+        f"aria-label='{html.escape(f'Inspect {label}, {count} target' + ('s' if count != 1 else ''), quote=True)}' "
         f"data-label='{html.escape(label, quote=True)}' "
         f"data-coordinates='{html.escape(json.dumps([item['coordinates'] for item in items if item['label'] == label]), quote=True)}'>"
         f"<i aria-hidden='true' style='--category-color:rgb{PALETTE[idx % len(PALETTE)]}'></i><span>{html.escape(label)}</span><b>{count}</b></button>"
         for idx, (label, count) in enumerate(counts.items())
     )
-    caption = "模型实时预测 · 选择目标查看细节"
+    caption = "Live model predictions · select a target or category to inspect"
     return (
         (f"<img class='focus-source' hidden alt='' src='{html.escape(image_source, quote=True)}'>" if image_source else "")
-        + f"<div class='targets-summary'><b>{len(items)}</b> 个目标 <span>{len(counts)} 个类别</span></div>"
+        + f"<div class='targets-summary'><b>{len(items)}</b> {'target' if len(items) == 1 else 'targets'} <span>{len(counts)} {'category' if len(counts) == 1 else 'categories'}</span></div>"
         f"<div class='target-chips'>{chips}</div>"
-        "<div class='focus-help'><span>选择目标，查看局部细节</span>"
-        "<button type='button' class='clear-focus' hidden>显示全部</button></div>"
+        "<div class='focus-help'><span>Inspect individual instances</span>"
+        "<button type='button' class='clear-focus' hidden>Show all</button></div>"
         f"<div class='targets-caption'>{caption}</div>"
     )

tests/test_console.py

@@ -44,12 +44,12 @@ class ConsoleTest(unittest.TestCase):
                 self.assertEqual(len(choices["choices"]), len(app.BY_ROUTE[route]))
                 self.assertEqual(len(gallery["value"]), len(app.BY_ROUTE[route]))
                 self.assertEqual(loaded[8], "[]")
-                self.assertIn("图像已就绪", loaded[10])
+                self.assertIn("Image ready", loaded[10])
 
     def test_case_switch_resets_prediction(self):
         output = app.load_case(app.CASES[1]["id"])
         self.assertEqual(output[8], "[]")
-        self.assertIn("图像已就绪", output[10])
+        self.assertIn("Image ready", output[10])
 
     def test_upload_cannot_reveal_unrelated_gt(self):
         row = app.CASES[0]
@@ -70,7 +70,7 @@ class ConsoleTest(unittest.TestCase):
             str(app.ROOT / row["image"]), row["prompt"], row["route"], valid + invalid
         )
         self.assertEqual(result["normalized"], "[]")
-        self.assertIn("协议错误", result["metrics"])
+        self.assertIn("Protocol error", result["metrics"])
 
     def test_actual_request_uses_latest_query_without_reference(self):
         row = app.CASES[0]
@@ -81,7 +81,7 @@ class ConsoleTest(unittest.TestCase):
         infer.assert_called_once_with(path, compile_query("car", row["route"]))
         self.assertIsInstance(output[0], app.Image.Image)
         self.assertEqual(len(output), 6)
-        self.assertIn("1 个视觉目标", output[-1])
+        self.assertIn("1 visual targets", output[-1])
 
     def test_labels_are_html_escaped(self):
         self.assertIn(
@@ -97,7 +97,7 @@ class ConsoleTest(unittest.TestCase):
         ])
         self.assertIn(label, result["normalized"])
         self.assertIn(label, result["targets"])
-        self.assertIn("解析完成", result["status"])
+        self.assertIn("Complete", result["status"])
 
     def test_focus_buttons_carry_exact_safe_geometry(self):
         class Buttons(HTMLParser):
@@ -138,7 +138,7 @@ class ConsoleTest(unittest.TestCase):
             self.assertIn("data-phase='busy'", pending[-2])
             done = next(stream)
             self.assertTrue(done[-1]["interactive"])
-            self.assertIn("1 个视觉目标", done[-2])
+            self.assertIn("1 visual targets", done[-2])
             infer.assert_called_once_with(path, compile_query("car", row["route"]))
 
     def test_stream_error_releases_action_without_stale_prediction(self):
@@ -178,14 +178,14 @@ class ConsoleTest(unittest.TestCase):
         row = app.CASES[0]
         with patch.object(app.GroundingClient, "infer", side_effect=ConnectionError("test detail")):
             result = app.run_inference(str(app.ROOT / row["image"]), "car", "grounding")
-        self.assertIn("请检查连接后重试", result[-1])
+        self.assertIn("Check the connection and retry", result[-1])
         self.assertNotIn("test detail", result[-1])
         self.assertIn("test detail", result[-2])
 
     def test_connection_status_explicit_and_escaped(self):
         with patch.object(app.GroundingClient, "health", return_value=(False, "<unsafe>")):
             output = app.service_status()
-        self.assertIn("推理未连接", output)
+        self.assertIn("Inference offline", output)
         self.assertIn("&lt;unsafe&gt;", output)
 
     def test_both_run_buttons_share_real_stream(self):