Update app.py

app.py

@@ -1,20 +1,27 @@
-import os
 import gc
-import time
-import json
-import base64
+import importlib.util
+import inspect
+import re
 import tempfile
+import time
 from io import BytesIO
-from threading import Thread
+from pathlib import Path
+from threading import Lock, Thread
 
 import gradio as gr
 import spaces
 import torch
+import transformers
 from PIL import Image, ImageOps
 
+try:
+    import fitz
+except ImportError:
+    fitz = None
+
 from transformers import (
-    AutoProcessor,
     AutoModelForImageTextToText,
+    AutoProcessor,
     TextIteratorStreamer,
 )
 
@@ -22,16 +29,117 @@ MAX_MAX_NEW_TOKENS = 8192
 DEFAULT_MAX_NEW_TOKENS = 4096
 
 MODEL_PATH = "zai-org/GLM-OCR"
+MIN_TRANSFORMERS_VERSION = "5.0.0"
+UPGRADE_TRANSFORMERS_CMD = 'pip install -U "transformers>=5.0.0"'
+MODEL_CARD_TRANSFORMERS_CMD = "pip install git+https://github.com/huggingface/transformers.git"
+
+BASE_DIR = Path(__file__).resolve().parent
+DATA_DIR = BASE_DIR / "data"
+
+INPUT_MODES = ["Upload PDF", "PDF from data", "All PDFs in data"]
+DEFAULT_INPUT_MODE = "Upload PDF"
+
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 print("Using device:", device)
 
-processor = AutoProcessor.from_pretrained(MODEL_PATH, trust_remote_code=True)
-model = AutoModelForImageTextToText.from_pretrained(
-    pretrained_model_name_or_path=MODEL_PATH,
-    torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
-    device_map="auto",
-    trust_remote_code=True,
-).eval()
+processor = None
+model = None
+model_load_error = None
+model_init_lock = Lock()
+
+
+def parse_version_triplet(version_text):
+    parts = [int(part) for part in re.findall(r"\d+", str(version_text))[:3]]
+    while len(parts) < 3:
+        parts.append(0)
+    return tuple(parts)
+
+
+def build_glm_ocr_dependency_error():
+    transformers_version = getattr(transformers, "__version__", "unknown")
+    try:
+        import torchvision  # noqa: F401
+    except ImportError:
+        torch_version = getattr(torch, "__version__", "unknown")
+        return (
+            "GLM-OCR requires torchvision, but it is not installed in the active environment. "
+            f"Install a torchvision build compatible with torch {torch_version} "
+            "(for example, `pip install torchvision`) and restart the app."
+        )
+    except Exception as exc:
+        return (
+            "GLM-OCR could not import torchvision from the active environment. "
+            f"Resolve the torchvision installation issue ({exc}) and restart the app."
+        )
+
+    has_glm_ocr_support = importlib.util.find_spec("transformers.models.glm_ocr") is not None
+    if has_glm_ocr_support:
+        return None
+
+    version_triplet = parse_version_triplet(transformers_version)
+    minimum_triplet = parse_version_triplet(MIN_TRANSFORMERS_VERSION)
+    if version_triplet and version_triplet < minimum_triplet:
+        return (
+            f"GLM-OCR support is not available in transformers {transformers_version}. "
+            f"Upgrade to {MIN_TRANSFORMERS_VERSION}+ with `{UPGRADE_TRANSFORMERS_CMD}` or use the "
+            f"model card recommendation `{MODEL_CARD_TRANSFORMERS_CMD}`, then restart the app."
+        )
+
+    return (
+        f"GLM-OCR support is not available in the installed transformers build ({transformers_version}). "
+        f"Upgrade transformers with `{UPGRADE_TRANSFORMERS_CMD}` or use the model card recommendation "
+        f"`{MODEL_CARD_TRANSFORMERS_CMD}`, then restart the app."
+    )
+
+
+def load_glm_ocr_components():
+    global processor, model, model_load_error
+
+    if model_load_error is not None:
+        raise RuntimeError(model_load_error)
+
+    if processor is not None and model is not None:
+        return processor, model
+
+    with model_init_lock:
+        if model_load_error is not None:
+            raise RuntimeError(model_load_error)
+
+        if processor is not None and model is not None:
+            return processor, model
+
+        dependency_error = build_glm_ocr_dependency_error()
+        if dependency_error is not None:
+            model_load_error = dependency_error
+            print(model_load_error)
+            raise RuntimeError(model_load_error)
+
+        try:
+            processor = AutoProcessor.from_pretrained(MODEL_PATH, trust_remote_code=True)
+
+            model_kwargs = {
+                "pretrained_model_name_or_path": MODEL_PATH,
+                "torch_dtype": torch.bfloat16 if torch.cuda.is_available() else torch.float32,
+                "trust_remote_code": True,
+            }
+            if torch.cuda.is_available():
+                model_kwargs["device_map"] = "auto"
+
+            model = AutoModelForImageTextToText.from_pretrained(**model_kwargs).eval()
+            if not torch.cuda.is_available():
+                model = model.to(device)
+        except Exception as exc:
+            model_load_error = (
+                f"Failed to load {MODEL_PATH}: {exc}. "
+                "If the error mentions an unrecognized processor or model type, upgrade transformers with "
+                f"`{UPGRADE_TRANSFORMERS_CMD}` or follow the model card recommendation "
+                f"`{MODEL_CARD_TRANSFORMERS_CMD}`. Restart the app after upgrading."
+            )
+            print(model_load_error)
+            raise RuntimeError(model_load_error) from exc
+
+    return processor, model
+
 
 TASK_PROMPTS = {
     "Text": "Text Recognition:",
@@ -41,104 +149,193 @@ TASK_PROMPTS = {
 
 TASK_CHOICES = list(TASK_PROMPTS.keys())
 
-image_examples = [
-    {"media": "examples/1.jpg", "task": "Text"},
-    {"media": "examples/4.jpg", "task": "Text"},
-    {"media": "examples/5.webp", "task": "Formula"},
-    {"media": "examples/2.jpg", "task": "Table"},
-    {"media": "examples/3.jpg", "task": "Text"},
-]
-
-
-def pil_to_data_url(img: Image.Image, fmt="PNG"):
-    buf = BytesIO()
-    img.save(buf, format=fmt)
-    data = base64.b64encode(buf.getvalue()).decode()
-    mime = "image/png" if fmt.upper() == "PNG" else "image/jpeg"
-    return f"data:{mime};base64,{data}"
-
-
-def file_to_data_url(path):
-    if not os.path.exists(path):
-        return ""
-    ext = path.rsplit(".", 1)[-1].lower()
-    mime = {
-        "jpg": "image/jpeg",
-        "jpeg": "image/jpeg",
-        "png": "image/png",
-        "webp": "image/webp",
-    }.get(ext, "image/jpeg")
-    with open(path, "rb") as f:
-        data = base64.b64encode(f.read()).decode()
-    return f"data:{mime};base64,{data}"
-
-
-def make_thumb_b64(path, max_dim=240):
-    try:
-        img = Image.open(path).convert("RGB")
-        img.thumbnail((max_dim, max_dim))
-        return pil_to_data_url(img, "JPEG")
-    except Exception as e:
-        print("Thumbnail error:", e)
-        return ""
-
-
-def build_example_cards_html():
-    cards = ""
-    for i, ex in enumerate(image_examples):
-        thumb = make_thumb_b64(ex["media"])
-        cards += f"""
-        <div class="example-card" data-idx="{i}">
-            <div class="example-thumb-wrap">
-                {"<img src='" + thumb + "' alt=''>" if thumb else "<div class='example-thumb-placeholder'>Preview</div>"}
-                <div class="example-media-chip">IMAGE</div>
-            </div>
-            <div class="example-meta-row">
-                <span class="example-badge">{ex["task"]}</span>
-            </div>
-            <div class="example-prompt-text">GLM-OCR example · {os.path.basename(ex["media"])}</div>
-        </div>
-        """
-    return cards
-
-
-EXAMPLE_CARDS_HTML = build_example_cards_html()
-
-
-def load_example_data(idx_str):
-    try:
-        idx = int(str(idx_str).strip())
-    except Exception:
-        return gr.update(value="")
 
-    if idx < 0 or idx >= len(image_examples):
-        return gr.update(value="")
+def list_data_pdfs():
+    if not DATA_DIR.exists():
+        return []
 
-    ex = image_examples[idx]
-    media_b64 = file_to_data_url(ex["media"])
-    if not media_b64:
-        return gr.update(value=json.dumps({"status": "error", "message": "Could not load example image"}))
+    return sorted(
+        [path for path in DATA_DIR.iterdir() if path.is_file() and path.suffix.lower() == ".pdf"],
+        key=lambda path: path.name.lower(),
+    )
 
-    return gr.update(value=json.dumps({
-        "status": "ok",
-        "media": media_b64,
-        "task": ex["task"],
-        "name": os.path.basename(ex["media"]),
-    }))
 
+def build_data_folder_note():
+    pdf_paths = list_data_pdfs()
+    folder_line = f"Data folder: `{DATA_DIR}`"
+    if not pdf_paths:
+        return folder_line + "\n\nNo PDF files were found."
 
-def b64_to_pil(b64_str):
-    if not b64_str:
-        return None
-    try:
-        if b64_str.startswith("data:"):
-            _, data = b64_str.split(",", 1)
+    lines = "\n".join(f"- `{path.name}`" for path in pdf_paths)
+    return folder_line + "\n\nAvailable PDFs:\n" + lines
+
+
+def refresh_pdf_dropdown():
+    pdf_names = [path.name for path in list_data_pdfs()]
+    value = pdf_names[0] if pdf_names else None
+    return gr.update(choices=pdf_names, value=value), build_data_folder_note()
+
+
+def resolve_data_pdf_path(pdf_name):
+    if not pdf_name:
+        raise ValueError("Please choose a PDF from the data folder.")
+
+    for path in list_data_pdfs():
+        if path.name == pdf_name:
+            return path
+
+    raise ValueError(f"Could not find `{pdf_name}` in `{DATA_DIR}`.")
+
+
+def resolve_uploaded_pdf_path(uploaded_pdf):
+    if not uploaded_pdf:
+        raise ValueError("Please upload a PDF first.")
+
+    pdf_path = Path(str(uploaded_pdf))
+    if not pdf_path.exists():
+        raise ValueError("The uploaded PDF could not be read. Please upload it again.")
+
+    if pdf_path.suffix.lower() != ".pdf":
+        raise ValueError("Please upload a PDF file.")
+
+    return pdf_path
+
+
+def cache_uploaded_pdf(uploaded_pdf):
+    if not uploaded_pdf:
+        return None, "No PDF uploaded yet."
+
+    pdf_path = resolve_uploaded_pdf_path(uploaded_pdf)
+    return str(pdf_path), f"Selected upload: `{pdf_path.name}`"
+
+
+def normalize_image(image: Image.Image):
+    if image.mode in ("RGBA", "LA", "P"):
+        image = image.convert("RGB")
+    return ImageOps.exif_transpose(image)
+
+
+def pdf_page_to_image(pdf_path, page_number):
+    if fitz is None:
+        raise RuntimeError("PDF support requires PyMuPDF (`fitz`) to be installed.")
+
+    page_number = int(page_number)
+    if page_number < 1:
+        raise ValueError("Page number must be 1 or higher.")
+
+    with fitz.open(pdf_path) as document:
+        if page_number > document.page_count:
+            raise ValueError(
+                f"Page {page_number} is outside the page count for {Path(pdf_path).name} "
+                f"({document.page_count} pages)."
+            )
+        page = document.load_page(page_number - 1)
+        pixmap = page.get_pixmap(matrix=fitz.Matrix(2, 2), alpha=False)
+        image = Image.open(BytesIO(pixmap.tobytes("png"))).convert("RGB")
+        return ImageOps.exif_transpose(image)
+
+
+def get_pdf_page_count(pdf_path):
+    if fitz is None:
+        raise RuntimeError("PDF support requires PyMuPDF (`fitz`) to be installed.")
+
+    with fitz.open(pdf_path) as document:
+        return document.page_count
+
+
+def parse_page_selection(page_selection, page_count):
+    selection = str(page_selection or "1").strip().lower()
+    if not selection:
+        selection = "1"
+    if selection == "all":
+        return list(range(1, page_count + 1))
+
+    pages = []
+    for raw_part in selection.split(","):
+        part = raw_part.strip()
+        if not part:
+            continue
+        if "-" in part:
+            start_str, end_str = [token.strip() for token in part.split("-", 1)]
+            start_page = int(start_str)
+            end_page = int(end_str)
+            if start_page > end_page:
+                raise ValueError(f"Invalid page range: {part}")
+            pages.extend(range(start_page, end_page + 1))
         else:
-            data = b64_str
-        image_data = base64.b64decode(data)
-        return Image.open(BytesIO(image_data)).convert("RGB")
-    except Exception:
-        return None
+            pages.append(int(part))
+
+    if not pages:
+        raise ValueError("Please provide page numbers such as `1`, `1,3`, `2-5`, or `all`.")
+
+    deduped_pages = []
+    seen = set()
+    for page in pages:
+        if page < 1 or page > page_count:
+            raise ValueError(f"Page {page} is outside the PDF page count ({page_count}).")
+        if page not in seen:
+            deduped_pages.append(page)
+            seen.add(page)
+    return deduped_pages
+
+
+def join_output_blocks(blocks):
+    return "\n\n".join(block.strip() for block in blocks if str(block).strip()).strip()
+
+
+def stream_pdf_text(pdf_path, task, page_selection, max_new_tokens):
+    page_count = get_pdf_page_count(pdf_path)
+    selected_pages = parse_page_selection(page_selection, page_count)
+    include_page_headers = len(selected_pages) > 1
+    combined_sections = []
+
+    for page_number in selected_pages:
+        page_title = f"Page {page_number}"
+        try:
+            image = pdf_page_to_image(pdf_path, page_number)
+            page_text = ""
+            for chunk in process_image_stream(
+                image=image,
+                task=task,
+                max_new_tokens=max_new_tokens,
+            ):
+                page_text = chunk.strip()
+                current_block = f"{page_title}\n{page_text}" if include_page_headers else page_text
+                yield join_output_blocks(combined_sections + [current_block])
+
+            if not page_text.strip():
+                page_text = "[ERROR] No output was generated."
+        except Exception as exc:
+            page_text = f"[ERROR] {str(exc)}"
+
+        final_block = f"{page_title}\n{page_text}" if include_page_headers else page_text
+        combined_sections.append(final_block)
+        yield join_output_blocks(combined_sections)
+
+
+def stream_data_folder_pdfs(task, page_selection, max_new_tokens):
+    pdf_paths = list_data_pdfs()
+    if not pdf_paths:
+        yield f"[ERROR] No PDF files were found in `{DATA_DIR}`."
+        return
+
+    combined_files = []
+    for path in pdf_paths:
+        pdf_text = ""
+        try:
+            for partial in stream_pdf_text(path, task, page_selection, max_new_tokens):
+                pdf_text = partial
+                yield join_output_blocks(combined_files + [f"File: {path.name}\n{pdf_text}"])
+
+            if not pdf_text.strip():
+                pdf_text = "[ERROR] No output was generated."
+            file_block = f"File: {path.name}\n{pdf_text}"
+        except Exception as exc:
+            file_block = f"File: {path.name}\n[ERROR] {str(exc)}"
+            yield join_output_blocks(combined_files + [file_block])
+
+        combined_files.append(file_block)
+        yield join_output_blocks(combined_files)
 
 
 def calc_timeout_generic(*args, **kwargs):
@@ -151,8 +348,8 @@ def calc_timeout_generic(*args, **kwargs):
         return 60
 
 
-@spaces.GPU(duration=calc_timeout_generic)
 def process_image_stream(image, task, max_new_tokens=DEFAULT_MAX_NEW_TOKENS, gpu_timeout=60):
+    del gpu_timeout
     tmp_path = None
     try:
         if image is None:
@@ -163,17 +360,15 @@ def process_image_stream(image, task, max_new_tokens=DEFAULT_MAX_NEW_TOKENS, gpu
             yield "[ERROR] Invalid OCR task selected."
             return
 
-        if image.mode in ("RGBA", "LA", "P"):
-            image = image.convert("RGB")
-        image = ImageOps.exif_transpose(image)
+        processor_obj, model_obj = load_glm_ocr_components()
+        image = normalize_image(image)
 
         tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
         image.save(tmp.name, "PNG")
         tmp_path = tmp.name
         tmp.close()
 
-        prompt = TASK_PROMPTS.get(task, "Text Recognition:")
-
+        prompt = TASK_PROMPTS[task]
         messages = [
             {
                 "role": "user",
@@ -184,7 +379,7 @@ def process_image_stream(image, task, max_new_tokens=DEFAULT_MAX_NEW_TOKENS, gpu
             }
         ]
 
-        inputs = processor.apply_chat_template(
+        inputs = processor_obj.apply_chat_template(
             messages,
             tokenize=True,
             add_generation_prompt=True,
@@ -193,33 +388,32 @@ def process_image_stream(image, task, max_new_tokens=DEFAULT_MAX_NEW_TOKENS, gpu
         )
 
         inputs.pop("token_type_ids", None)
-        inputs = {k: v.to(model.device) if hasattr(v, "to") else v for k, v in inputs.items()}
+        inputs = {key: value.to(model_obj.device) if hasattr(value, "to") else value for key, value in inputs.items()}
 
         streamer = TextIteratorStreamer(
-            processor.tokenizer if hasattr(processor, "tokenizer") else processor,
+            processor_obj.tokenizer if hasattr(processor_obj, "tokenizer") else processor_obj,
             skip_prompt=True,
             skip_special_tokens=True,
         )
 
         generation_error = {"error": None}
-
         generation_kwargs = {
             **inputs,
             "streamer": streamer,
             "max_new_tokens": int(max_new_tokens),
         }
 
-        def _run_generation():
+        def run_generation():
             try:
-                model.generate(**generation_kwargs)
-            except Exception as e:
-                generation_error["error"] = e
+                model_obj.generate(**generation_kwargs)
+            except Exception as exc:
+                generation_error["error"] = exc
                 try:
                     streamer.end()
                 except Exception:
                     pass
 
-        thread = Thread(target=_run_generation, daemon=True)
+        thread = Thread(target=run_generation, daemon=True)
         thread.start()
 
         buffer = ""
@@ -231,22 +425,21 @@ def process_image_stream(image, task, max_new_tokens=DEFAULT_MAX_NEW_TOKENS, gpu
         thread.join(timeout=1.0)
 
         if generation_error["error"] is not None:
-            err_msg = f"[ERROR] Inference failed: {str(generation_error['error'])}"
+            error_message = f"[ERROR] Inference failed: {generation_error['error']}"
             if buffer.strip():
-                yield buffer.strip() + "\n\n" + err_msg
+                yield buffer.strip() + "\n\n" + error_message
             else:
-                yield err_msg
+                yield error_message
             return
 
         if not buffer.strip():
             yield "[ERROR] No output was generated."
-
-    except Exception as e:
-        yield f"[ERROR] {str(e)}"
+    except Exception as exc:
+        yield f"[ERROR] {str(exc)}"
     finally:
-        if tmp_path and os.path.exists(tmp_path):
+        if tmp_path and Path(tmp_path).exists():
             try:
-                os.unlink(tmp_path)
+                Path(tmp_path).unlink()
             except Exception:
                 pass
         gc.collect()
@@ -254,928 +447,202 @@ def process_image_stream(image, task, max_new_tokens=DEFAULT_MAX_NEW_TOKENS, gpu
             torch.cuda.empty_cache()
 
 
-def run_router(task, image_b64, max_new_tokens_v, gpu_timeout_v):
-    try:
-        image = b64_to_pil(image_b64)
-        yield from process_image_stream(
-            image=image,
-            task=task,
-            max_new_tokens=max_new_tokens_v,
-            gpu_timeout=gpu_timeout_v,
+def toggle_input_mode(mode):
+    mode = str(mode or DEFAULT_INPUT_MODE)
+    if mode == "Upload PDF":
+        help_text = "Click the upload button to choose a PDF, then enter pages like `1`, `1,3`, `2-5`, or `all`."
+        return (
+            gr.update(visible=True),
+            gr.update(visible=True),
+            gr.update(visible=False),
+            gr.update(visible=True),
+            gr.update(visible=False),
+            gr.update(visible=False),
+            gr.update(value=help_text),
         )
-    except Exception as e:
-        yield f"[ERROR] {str(e)}"
-
-
-def noop():
-    return None
-
-
-css = r"""
-@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800&family=JetBrains+Mono:wght@400;500;600&display=swap');
-*{box-sizing:border-box;margin:0;padding:0}
-html,body{height:100%;overflow-x:hidden}
-body,.gradio-container{
-    background:#0f0f13!important;
-    font-family:'Inter',system-ui,-apple-system,sans-serif!important;
-    font-size:14px!important;color:#e4e4e7!important;min-height:100vh;overflow-x:hidden;
-}
-.dark body,.dark .gradio-container{background:#0f0f13!important;color:#e4e4e7!important}
-footer{display:none!important}
-.hidden-input{display:none!important;height:0!important;overflow:hidden!important;margin:0!important;padding:0!important}
-
-#gradio-run-btn,#example-load-btn{
-    position:absolute!important;left:-9999px!important;top:-9999px!important;
-    width:1px!important;height:1px!important;opacity:0.01!important;
-    pointer-events:none!important;overflow:hidden!important;
-}
-
-.app-shell{
-    background:#18181b;border:1px solid #27272a;border-radius:16px;
-    margin:12px auto;max-width:1450px;overflow:hidden;
-    box-shadow:0 25px 50px -12px rgba(0,0,0,.6),0 0 0 1px rgba(255,255,255,.03);
-}
-.app-header{
-    background:linear-gradient(135deg,#18181b,#1e1e24);border-bottom:1px solid #27272a;
-    padding:14px 24px;display:flex;align-items:center;justify-content:space-between;flex-wrap:wrap;gap:12px;
-}
-.app-header-left{display:flex;align-items:center;gap:12px}
-.app-logo{
-    width:38px;height:38px;background:linear-gradient(135deg,#FF1493,#ff3cad,#ff70c6);
-    border-radius:10px;display:flex;align-items:center;justify-content:center;
-    box-shadow:0 4px 12px rgba(255,20,147,.35);
-}
-.app-logo svg{width:22px;height:22px;fill:#fff;flex-shrink:0}
-.app-title{
-    font-size:18px;font-weight:700;background:linear-gradient(135deg,#f5f5f5,#bdbdbd);
-    -webkit-background-clip:text;-webkit-text-fill-color:transparent;letter-spacing:-.3px;
-}
-.app-badge{
-    font-size:11px;font-weight:600;padding:3px 10px;border-radius:20px;
-    background:rgba(255,20,147,.12);color:#ff8fcf;border:1px solid rgba(255,20,147,.25);letter-spacing:.3px;
-}
-.app-badge.fast{background:rgba(255,60,173,.10);color:#ff9ad5;border:1px solid rgba(255,60,173,.22)}
-
-.model-tabs-bar{
-    background:#18181b;border-bottom:1px solid #27272a;padding:10px 16px;
-    display:flex;gap:8px;align-items:center;flex-wrap:wrap;
-}
-.model-tab{
-    display:inline-flex;align-items:center;justify-content:center;gap:6px;
-    min-width:32px;height:34px;background:transparent;border:1px solid #27272a;
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-}
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-    width:36px;height:36px;border:3px solid #27272a;border-top-color:#FF1493;border-radius:50%;animation:spin .8s linear infinite;
-}
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-}
-@keyframes shimmer{0%{background-position:200% 0}100%{background-position:-200% 0}}
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-}
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-}
-.slider-row input[type="range"]::-webkit-slider-thumb{
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-}
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-}
-.slider-row .slider-val{
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-}
-
-.app-statusbar{
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-}
-.app-statusbar .sb-section{
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-}
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-}
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-.exp-note{padding:10px 20px;font-size:12px;color:#52525b;border-top:1px solid #27272a;text-align:center}
-.exp-note a{color:#ff8fcf;text-decoration:none}
-.exp-note a:hover{text-decoration:underline}
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-::-webkit-scrollbar{width:8px;height:8px}
-::-webkit-scrollbar-track{background:#09090b}
-::-webkit-scrollbar-thumb{background:#27272a;border-radius:4px}
-::-webkit-scrollbar-thumb:hover{background:#3f3f46}
-
-@media(max-width:980px){
-    .app-main-row{flex-direction:column}
-    .app-main-right{width:100%}
-    .app-main-left{border-right:none;border-bottom:1px solid #27272a}
-}
-"""
-
-gallery_js = r"""
-() => {
-function init() {
-    if (window.__glmOutpostInitDone) return;
-
-    const dropZone = document.getElementById('media-drop-zone');
-    const uploadPrompt = document.getElementById('upload-prompt');
-    const uploadClick = document.getElementById('upload-click-area');
-    const fileInput = document.getElementById('custom-file-input');
-    const previewWrap = document.getElementById('single-preview-wrap');
-    const previewImg = document.getElementById('single-preview-img');
-    const btnUpload = document.getElementById('preview-upload-btn');
-    const btnClear = document.getElementById('preview-clear-btn');
-    const runBtnEl = document.getElementById('custom-run-btn');
-    const outputArea = document.getElementById('custom-output-textarea');
-    const mediaStatus = document.getElementById('sb-media-status');
-
-    if (!dropZone || !fileInput || !previewWrap || !previewImg) {
-        setTimeout(init, 250);
-        return;
-    }
-
-    window.__glmOutpostInitDone = true;
-    let mediaState = null;
-    let toastTimer = null;
-    let examplePoller = null;
-    let lastSeenExamplePayload = null;
-
-    function showToast(message, type) {
-        let toast = document.getElementById('app-toast');
-        if (!toast) {
-            toast = document.createElement('div');
-            toast.id = 'app-toast';
-            toast.className = 'toast-notification';
-            toast.innerHTML = '<span class="toast-icon"></span><span class="toast-text"></span>';
-            document.body.appendChild(toast);
-        }
-        const icon = toast.querySelector('.toast-icon');
-        const text = toast.querySelector('.toast-text');
-        toast.className = 'toast-notification ' + (type || 'error');
-        if (type === 'warning') icon.textContent = '\u26A0';
-        else if (type === 'info') icon.textContent = '\u2139';
-        else icon.textContent = '\u2717';
-        text.textContent = message;
-        if (toastTimer) clearTimeout(toastTimer);
-        void toast.offsetWidth;
-        toast.classList.add('visible');
-        toastTimer = setTimeout(() => toast.classList.remove('visible'), 3500);
-    }
-
-    function showLoader() {
-        const l = document.getElementById('output-loader');
-        if (l) l.classList.add('active');
-        const sb = document.getElementById('sb-run-state');
-        if (sb) sb.textContent = 'Processing...';
-    }
-    function hideLoader() {
-        const l = document.getElementById('output-loader');
-        if (l) l.classList.remove('active');
-        const sb = document.getElementById('sb-run-state');
-        if (sb) sb.textContent = 'Done';
-    }
-    function setRunErrorState() {
-        const l = document.getElementById('output-loader');
-        if (l) l.classList.remove('active');
-        const sb = document.getElementById('sb-run-state');
-        if (sb) sb.textContent = 'Error';
-    }
-
-    window.__hideLoader = hideLoader;
-    window.__setRunErrorState = setRunErrorState;
-    window.__showToast = showToast;
-
-    function flashOutputError() {
-        if (!outputArea) return;
-        outputArea.classList.add('error-flash');
-        setTimeout(() => outputArea.classList.remove('error-flash'), 800);
-    }
-
-    function getValueFromContainer(containerId) {
-        const container = document.getElementById(containerId);
-        if (!container) return '';
-        const el = container.querySelector('textarea, input');
-        return el ? (el.value || '') : '';
-    }
-
-    function setGradioValue(containerId, value) {
-        const container = document.getElementById(containerId);
-        if (!container) return false;
-        const el = container.querySelector('textarea, input');
-        if (!el) return false;
-        const proto = el.tagName === 'TEXTAREA' ? HTMLTextAreaElement.prototype : HTMLInputElement.prototype;
-        const ns = Object.getOwnPropertyDescriptor(proto, 'value');
-        if (ns && ns.set) {
-            ns.set.call(el, value);
-            el.dispatchEvent(new Event('input', {bubbles:true, composed:true}));
-            el.dispatchEvent(new Event('change', {bubbles:true, composed:true}));
-            return true;
-        }
-        return false;
-    }
-
-    function syncImageToGradio() {
-        setGradioValue('hidden-image-b64', mediaState ? mediaState.b64 : '');
-        if (mediaStatus) mediaStatus.textContent = mediaState ? '1 image uploaded' : 'No image uploaded';
-    }
-
-    function syncTaskToGradio(name) {
-        setGradioValue('hidden-task-name', name);
-    }
-
-    function renderPreview() {
-        if (!mediaState) {
-            previewImg.src = '';
-            previewImg.style.display = 'none';
-            previewWrap.style.display = 'none';
-            if (uploadPrompt) uploadPrompt.style.display = 'flex';
-            syncImageToGradio();
-            return;
-        }
-
-        previewWrap.style.display = 'flex';
-        if (uploadPrompt) uploadPrompt.style.display = 'none';
-        previewImg.src = mediaState.preview || mediaState.b64;
-        previewImg.style.display = 'block';
-        syncImageToGradio();
-    }
-
-    function setPreviewFromFileReader(b64, name) {
-        mediaState = {b64, name: name || 'file', mode: 'image'};
-        renderPreview();
-    }
-
-    function clearPreview() {
-        mediaState = null;
-        renderPreview();
-    }
-    window.__clearPreview = clearPreview;
-
-    function processFile(file) {
-        if (!file) return;
-        if (!file.type.startsWith('image/')) {
-            showToast('Only image files are supported', 'error');
-            return;
-        }
-        const reader = new FileReader();
-        reader.onload = (e) => setPreviewFromFileReader(e.target.result, file.name);
-        reader.readAsDataURL(file);
-    }
-
-    if (uploadClick) uploadClick.addEventListener('click', () => fileInput.click());
-    if (btnUpload) btnUpload.addEventListener('click', () => fileInput.click());
-    if (btnClear) btnClear.addEventListener('click', clearPreview);
-
-    fileInput.addEventListener('change', (e) => {
-        const file = e.target.files && e.target.files[0] ? e.target.files[0] : null;
-        if (file) processFile(file);
-        e.target.value = '';
-    });
-
-    dropZone.addEventListener('dragover', (e) => {
-        e.preventDefault();
-        dropZone.classList.add('drag-over');
-    });
-    dropZone.addEventListener('dragleave', (e) => {
-        e.preventDefault();
-        dropZone.classList.remove('drag-over');
-    });
-    dropZone.addEventListener('drop', (e) => {
-        e.preventDefault();
-        dropZone.classList.remove('drag-over');
-        if (e.dataTransfer.files && e.dataTransfer.files.length) processFile(e.dataTransfer.files[0]);
-    });
-
-    function activateTaskTab(name) {
-        document.querySelectorAll('.model-tab[data-task]').forEach(btn => {
-            btn.classList.toggle('active', btn.getAttribute('data-task') === name);
-        });
-        syncTaskToGradio(name);
-    }
-
-    window.__activateTaskTab = activateTaskTab;
-
-    document.querySelectorAll('.model-tab[data-task]').forEach(btn => {
-        btn.addEventListener('click', () => activateTaskTab(btn.getAttribute('data-task')));
-    });
-
-    activateTaskTab('Text');
-
-    function syncSlider(customId, gradioId) {
-        const slider = document.getElementById(customId);
-        const valSpan = document.getElementById(customId + '-val');
-        if (!slider) return;
-        slider.addEventListener('input', () => {
-            if (valSpan) valSpan.textContent = slider.value;
-            const container = document.getElementById(gradioId);
-            if (!container) return;
-            container.querySelectorAll('input[type="range"],input[type="number"]').forEach(el => {
-                const ns = Object.getOwnPropertyDescriptor(HTMLInputElement.prototype, 'value');
-                if (ns && ns.set) {
-                    ns.set.call(el, slider.value);
-                    el.dispatchEvent(new Event('input', {bubbles:true, composed:true}));
-                    el.dispatchEvent(new Event('change', {bubbles:true, composed:true}));
-                }
-            });
-        });
-    }
-
-    syncSlider('custom-max-new-tokens', 'gradio-max-new-tokens');
-    syncSlider('custom-gpu-duration', 'gradio-gpu-duration');
-
-    function validateBeforeRun() {
-        if (!mediaState) {
-            showToast('Please upload an image', 'error');
-            return false;
-        }
-        const currentTask = (document.querySelector('.model-tab.active') || {}).dataset?.task;
-        if (!currentTask) {
-            showToast('Please select a task', 'error');
-            return false;
-        }
-        return true;
-    }
-
-    window.__clickGradioRunBtn = function() {
-        if (!validateBeforeRun()) return;
-        syncImageToGradio();
-        const activeTask = document.querySelector('.model-tab.active');
-        if (activeTask) syncTaskToGradio(activeTask.getAttribute('data-task'));
-        if (outputArea) outputArea.value = '';
-        showLoader();
-        setTimeout(() => {
-            const gradioBtn = document.getElementById('gradio-run-btn');
-            if (!gradioBtn) {
-                setRunErrorState();
-                if (outputArea) outputArea.value = '[ERROR] Run button not found.';
-                showToast('Run button not found', 'error');
-                return;
-            }
-            const btn = gradioBtn.querySelector('button');
-            if (btn) btn.click(); else gradioBtn.click();
-        }, 180);
-    };
-
-    if (runBtnEl) runBtnEl.addEventListener('click', () => window.__clickGradioRunBtn());
-
-    const copyBtn = document.getElementById('copy-output-btn');
-    if (copyBtn) {
-        copyBtn.addEventListener('click', async () => {
-            try {
-                const text = outputArea ? outputArea.value : '';
-                if (!text.trim()) {
-                    showToast('No output to copy', 'warning');
-                    flashOutputError();
-                    return;
-                }
-                await navigator.clipboard.writeText(text);
-                showToast('Output copied to clipboard', 'info');
-            } catch(e) {
-                showToast('Copy failed', 'error');
-            }
-        });
-    }
-
-    const saveBtn = document.getElementById('save-output-btn');
-    if (saveBtn) {
-        saveBtn.addEventListener('click', () => {
-            const text = outputArea ? outputArea.value : '';
-            if (!text.trim()) {
-                showToast('No output to save', 'warning');
-                flashOutputError();
-                return;
-            }
-            const blob = new Blob([text], {type: 'text/plain;charset=utf-8'});
-            const a = document.createElement('a');
-            a.href = URL.createObjectURL(blob);
-            a.download = 'glm_ocr_output.txt';
-            document.body.appendChild(a);
-            a.click();
-            setTimeout(() => {
-                URL.revokeObjectURL(a.href);
-                document.body.removeChild(a);
-            }, 200);
-            showToast('Output saved', 'info');
-        });
-    }
+    if mode == "All PDFs in data":
+        help_text = (
+            "Run OCR on every PDF in the data folder. Page selections such as `1`, `1,3`, `2-5`, "
+            "or `all` are applied to each file."
+        )
+        return (
+            gr.update(visible=False),
+            gr.update(visible=False),
+            gr.update(visible=False),
+            gr.update(visible=True),
+            gr.update(visible=True),
+            gr.update(visible=True),
+            gr.update(value=help_text),
+        )
 
-    function applyExamplePayload(raw) {
-        try {
-            const data = JSON.parse(raw);
-            if (data.status !== 'ok') return;
-
-            if (data.task) activateTaskTab(data.task);
-
-            mediaState = {
-                b64: data.media || '',
-                preview: data.media || '',
-                name: data.name || 'example_file',
-                mode: 'image'
-            };
-            renderPreview();
-
-            document.querySelectorAll('.example-card.loading').forEach(c => c.classList.remove('loading'));
-            showToast('Example loaded', 'info');
-        } catch (e) {
-            document.querySelectorAll('.example-card.loading').forEach(c => c.classList.remove('loading'));
-        }
-    }
+    help_text = (
+        "Choose one PDF from the data folder and enter pages like `1`, `1,3`, `2-5`, or `all`."
+    )
+    return (
+        gr.update(visible=False),
+        gr.update(visible=False),
+        gr.update(visible=True),
+        gr.update(visible=True),
+        gr.update(visible=True),
+        gr.update(visible=True),
+        gr.update(value=help_text),
+    )
 
-    function startExamplePolling() {
-        if (examplePoller) clearInterval(examplePoller);
-        let attempts = 0;
-        examplePoller = setInterval(() => {
-            attempts += 1;
-            const current = getValueFromContainer('example-result-data');
-            if (current && current !== lastSeenExamplePayload) {
-                lastSeenExamplePayload = current;
-                clearInterval(examplePoller);
-                examplePoller = null;
-                applyExamplePayload(current);
-                return;
-            }
-            if (attempts >= 100) {
-                clearInterval(examplePoller);
-                examplePoller = null;
-                document.querySelectorAll('.example-card.loading').forEach(c => c.classList.remove('loading'));
-                showToast('Example load timed out', 'error');
-            }
-        }, 120);
-    }
 
-    function triggerExampleLoad(idx) {
-        const btnWrap = document.getElementById('example-load-btn');
-        const btn = btnWrap ? (btnWrap.querySelector('button') || btnWrap) : null;
-        if (!btn) return;
+@spaces.GPU(duration=calc_timeout_generic)
+def run_router(input_mode, task, uploaded_pdf, pdf_name, page_selection, max_new_tokens_v, gpu_timeout_v):
+    try:
+        mode = str(input_mode or DEFAULT_INPUT_MODE)
+        if mode == "All PDFs in data":
+            yield from stream_data_folder_pdfs(
+                task=task,
+                page_selection=page_selection,
+                max_new_tokens=max_new_tokens_v,
+            )
+            return
 
-        let attempts = 0;
+        if mode == "Upload PDF":
+            pdf_path = resolve_uploaded_pdf_path(uploaded_pdf)
+        else:
+            pdf_path = resolve_data_pdf_path(pdf_name)
 
-        function writeIdxAndClick() {
-            attempts += 1;
+        yield from stream_pdf_text(
+            pdf_path=pdf_path,
+            task=task,
+            page_selection=page_selection,
+            max_new_tokens=max_new_tokens_v,
+        )
+    except Exception as exc:
+        yield f"[ERROR] {str(exc)}"
 
-            const ok1 = setGradioValue('example-idx-input', String(idx));
-            setGradioValue('example-result-data', '');
-            const currentVal = getValueFromContainer('example-idx-input');
 
-            if (ok1 && currentVal === String(idx)) {
-                btn.click();
-                startExamplePolling();
-                return;
-            }
+available_pdfs = [path.name for path in list_data_pdfs()]
+default_pdf = available_pdfs[0] if available_pdfs else None
 
-            if (attempts < 30) {
-                setTimeout(writeIdxAndClick, 100);
-            } else {
-                document.querySelectorAll('.example-card.loading').forEach(c => c.classList.remove('loading'));
-                showToast('Failed to initialize example loader', 'error');
-            }
-        }
+with gr.Blocks(title="GLM-OCR") as demo:
+    gr.Markdown("# GLM-OCR")
+    gr.Markdown("Upload a PDF with the button below or run OCR on PDFs stored in `glmocr/data`.")
 
-        writeIdxAndClick();
-    }
+    with gr.Row():
+        input_mode = gr.Radio(
+            choices=INPUT_MODES,
+            value=DEFAULT_INPUT_MODE,
+            label="Input mode",
+        )
+        task = gr.Radio(
+            choices=TASK_CHOICES,
+            value="Text",
+            label="OCR task",
+        )
 
-    document.querySelectorAll('.example-card[data-idx]').forEach(card => {
-        card.addEventListener('click', () => {
-            const idx = card.getAttribute('data-idx');
-            if (idx === null || idx === undefined || idx === '') return;
-            document.querySelectorAll('.example-card.loading').forEach(c => c.classList.remove('loading'));
-            card.classList.add('loading');
-            showToast('Loading example...', 'info');
-            triggerExampleLoad(idx);
-        });
-    });
-
-    const observerTarget = document.getElementById('example-result-data');
-    if (observerTarget) {
-        const obs = new MutationObserver(() => {
-            const current = getValueFromContainer('example-result-data');
-            if (!current || current === lastSeenExamplePayload) return;
-            lastSeenExamplePayload = current;
-            if (examplePoller) {
-                clearInterval(examplePoller);
-                examplePoller = null;
-            }
-            applyExamplePayload(current);
-        });
-        obs.observe(observerTarget, {childList:true, subtree:true, characterData:true, attributes:true});
-    }
+    input_help = gr.Markdown(
+        "Click the upload button to choose a PDF, then enter pages like `1`, `1,3`, `2-5`, or `all`."
+    )
 
-    if (outputArea) outputArea.value = '';
-    const sb = document.getElementById('sb-run-state');
-    if (sb) sb.textContent = 'Ready';
-    if (mediaStatus) mediaStatus.textContent = 'No image uploaded';
-}
-init();
-}
-"""
+    with gr.Column():
+        uploaded_pdf_state = gr.State(None)
+        upload_pdf_btn = gr.UploadButton(
+            "Upload PDF",
+            file_types=[".pdf"],
+            file_count="single",
+            type="filepath",
+            visible=True,
+        )
+        uploaded_pdf_status = gr.Markdown("No PDF uploaded yet.", visible=True)
+        pdf_dropdown = gr.Dropdown(
+            choices=available_pdfs,
+            value=default_pdf,
+            label="PDF from data folder",
+            visible=False,
+        )
+        page_selection = gr.Textbox(
+            value="1",
+            label="Pages",
+            placeholder="Examples: 1, 1,3, 2-5, all",
+            visible=True,
+        )
+        data_folder_note = gr.Markdown(build_data_folder_note(), visible=False)
+        refresh_pdfs_btn = gr.Button("Refresh PDF list", visible=False)
+
+    with gr.Row():
+        max_new_tokens = gr.Slider(
+            minimum=1,
+            maximum=MAX_MAX_NEW_TOKENS,
+            step=1,
+            value=DEFAULT_MAX_NEW_TOKENS,
+            label="Max new tokens",
+        )
+        gpu_duration_state = gr.Number(
+            value=60,
+            precision=0,
+            label="GPU duration (seconds)",
+        )
 
-wire_outputs_js = r"""
-() => {
-function watchOutputs() {
-    const resultContainer = document.getElementById('gradio-result');
-    const outArea = document.getElementById('custom-output-textarea');
-    if (!resultContainer || !outArea) { setTimeout(watchOutputs, 500); return; }
+    run_btn = gr.Button("Run OCR", variant="primary")
+    result = gr.Textbox(label="OCR output", lines=24, max_lines=40)
 
-    let lastText = '';
+    demo.load(
+        fn=refresh_pdf_dropdown,
+        inputs=None,
+        outputs=[pdf_dropdown, data_folder_note],
+        queue=False,
+    )
 
-    function isErrorText(val) {
-        return typeof val === 'string' && val.trim().startsWith('[ERROR]');
-    }
+    input_mode.change(
+        fn=toggle_input_mode,
+        inputs=[input_mode],
+        outputs=[
+            upload_pdf_btn,
+            uploaded_pdf_status,
+            pdf_dropdown,
+            page_selection,
+            data_folder_note,
+            refresh_pdfs_btn,
+            input_help,
+        ],
+        queue=False,
+    )
 
-    function syncOutput() {
-        const el = resultContainer.querySelector('textarea') || resultContainer.querySelector('input');
-        if (!el) return;
-        const val = el.value || '';
-        if (val !== lastText) {
-            lastText = val;
-            outArea.value = val;
-            outArea.scrollTop = outArea.scrollHeight;
-
-            if (val.trim()) {
-                if (isErrorText(val)) {
-                    if (window.__setRunErrorState) window.__setRunErrorState();
-                    if (window.__showToast) window.__showToast('Inference failed', 'error');
-                } else {
-                    if (window.__hideLoader) window.__hideLoader();
-                }
-            }
-        }
-    }
+    upload_pdf_btn.upload(
+        fn=cache_uploaded_pdf,
+        inputs=[upload_pdf_btn],
+        outputs=[uploaded_pdf_state, uploaded_pdf_status],
+        queue=False,
+    )
 
-    const observer = new MutationObserver(syncOutput);
-    observer.observe(resultContainer, {childList:true, subtree:true, characterData:true, attributes:true});
-    setInterval(syncOutput, 500);
-}
-watchOutputs();
-}
-"""
-
-THUNDER_LOGO_SVG = """
-<svg viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg">
-  <path d="M13 2L5 13h5l-1 9 8-11h-5l1-9z" fill="white"/>
-</svg>
-"""
-
-UPLOAD_PREVIEW_SVG = """
-<svg viewBox="0 0 80 80" fill="none" xmlns="http://www.w3.org/2000/svg">
-    <rect x="8" y="14" width="64" height="52" rx="6" fill="none" stroke="#FF1493" stroke-width="2" stroke-dasharray="4 3"/>
-    <polygon points="12,62 30,40 42,50 54,34 68,62" fill="rgba(255,20,147,0.15)" stroke="#FF1493" stroke-width="1.5"/>
-    <circle cx="28" cy="30" r="6" fill="rgba(255,20,147,0.2)" stroke="#FF1493" stroke-width="1.5"/>
-</svg>
-"""
-
-COPY_SVG = """<svg viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path d="M16 1H4C2.9 1 2 1.9 2 3v12h2V3h12V1zm3 4H8C6.9 5 6 5.9 6 7v14c0 1.1.9 2 2 2h11c1.1 0 2-.9 2-2V7c0-1.1-.9-2-2-2zm0 16H8V7h11v14z"/></svg>"""
-SAVE_SVG = """<svg viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path d="M17 3H5a2 2 0 0 0-2 2v14a2 2 0 0 0 2 2h14a2 2 0 0 0 2-2V7l-4-4zM7 5h8v4H7V5zm12 14H5v-6h14v6z"/></svg>"""
-
-TASK_TABS_HTML = "".join([
-    f'<button class="model-tab{" active" if t == "Text" else ""}" data-task="{t}"><span class="model-tab-label">{t}</span></button>'
-    for t in TASK_CHOICES
-])
-
-with gr.Blocks() as demo:
-    hidden_image_b64 = gr.Textbox(value="", elem_id="hidden-image-b64", elem_classes="hidden-input", container=False)
-    hidden_task_name = gr.Textbox(value="Text", elem_id="hidden-task-name", elem_classes="hidden-input", container=False)
-
-    max_new_tokens = gr.Slider(
-        minimum=1,
-        maximum=MAX_MAX_NEW_TOKENS,
-        step=1,
-        value=DEFAULT_MAX_NEW_TOKENS,
-        elem_id="gradio-max-new-tokens",
-        elem_classes="hidden-input",
-        container=False,
+    refresh_pdfs_btn.click(
+        fn=refresh_pdf_dropdown,
+        inputs=None,
+        outputs=[pdf_dropdown, data_folder_note],
+        queue=False,
     )
-    gpu_duration_state = gr.Number(value=60, elem_id="gradio-gpu-duration", elem_classes="hidden-input", container=False)
-
-    result = gr.Textbox(value="", elem_id="gradio-result", elem_classes="hidden-input", container=False)
-
-    example_idx = gr.Textbox(value="", elem_id="example-idx-input", elem_classes="hidden-input", container=False)
-    example_result = gr.Textbox(value="", elem_id="example-result-data", elem_classes="hidden-input", container=False)
-    example_load_btn = gr.Button("Load Example", elem_id="example-load-btn")
-
-    gr.HTML(f"""
-    <div class="app-shell">
-        <div class="app-header">
-            <div class="app-header-left">
-                <div class="app-logo">{THUNDER_LOGO_SVG}</div>
-                <span class="app-title">GLM-OCR</span>
-                <span class="app-badge">vision enabled</span>
-                <span class="app-badge fast">Image Inference</span>
-            </div>
-        </div>
-
-        <div class="model-tabs-bar">
-            {TASK_TABS_HTML}
-        </div>
-
-        <div class="app-main-row">
-            <div class="app-main-left">
-                <div id="media-drop-zone">
-                    <div id="upload-prompt" class="upload-prompt-modern">
-                        <div id="upload-click-area" class="upload-click-area">
-                            {UPLOAD_PREVIEW_SVG}
-                            <span id="upload-main-text" class="upload-main-text">Click or drag an image here</span>
-                            <span id="upload-sub-text" class="upload-sub-text">Upload one image for OCR inference</span>
-                        </div>
-                    </div>
-
-                    <input id="custom-file-input" type="file" accept="image/*" style="display:none;" />
-
-                    <div id="single-preview-wrap" class="single-preview-wrap">
-                        <div class="single-preview-card">
-                            <img id="single-preview-img" src="" alt="Preview" style="display:none;">
-                            <div class="preview-overlay-actions">
-                                <button id="preview-upload-btn" class="preview-action-btn" title="Replace">Upload</button>
-                                <button id="preview-clear-btn" class="preview-action-btn" title="Clear">Clear</button>
-                            </div>
-                        </div>
-                    </div>
-                </div>
-
-                <div class="hint-bar">
-                    <b>Mode:</b> OCR image inference only &nbsp;&middot;&nbsp;
-                    <b>Task:</b> Switch between Text, Formula, and Table &nbsp;&middot;&nbsp;
-                    <kbd>Clear</kbd> removes the current image
-                </div>
-
-                <div class="examples-section">
-                    <div class="examples-title">Quick Examples</div>
-                    <div class="examples-scroll">
-                        {EXAMPLE_CARDS_HTML}
-                    </div>
-                </div>
-            </div>
-
-            <div class="app-main-right">
-                <div class="panel-card">
-                    <div id="instruction-title" class="panel-card-title">OCR Task</div>
-                    <div class="panel-card-body">
-                        <div class="info-markdown">
-                            <p><strong>Use the task tabs above</strong> to run <strong>Text Recognition</strong>, <strong>Formula Recognition</strong>, or <strong>Table Recognition</strong> on the uploaded image.</p>
-                        </div>
-                    </div>
-                </div>
-
-                <div style="padding:12px 20px;">
-                    <button id="custom-run-btn" class="btn-run">
-                        <span id="run-btn-label">Run Inference</span>
-                    </button>
-                </div>
-
-                <div class="output-frame">
-                    <div class="out-title">
-                        <span id="output-title-label">Raw Output Stream</span>
-                        <div class="out-title-right">
-                            <button id="copy-output-btn" class="out-action-btn" title="Copy">{COPY_SVG} Copy</button>
-                            <button id="save-output-btn" class="out-action-btn" title="Save">{SAVE_SVG} Save File</button>
-                        </div>
-                    </div>
-                    <div class="out-body">
-                        <div class="modern-loader" id="output-loader">
-                            <div class="loader-spinner"></div>
-                            <div class="loader-text">Running inference...</div>
-                            <div class="loader-bar-track"><div class="loader-bar-fill"></div></div>
-                        </div>
-                        <div class="output-scroll-wrap">
-                            <textarea id="custom-output-textarea" class="output-textarea" placeholder="Raw output will appear here..." readonly></textarea>
-                        </div>
-                    </div>
-                </div>
-
-                <div class="settings-group">
-                    <div class="settings-group-title">Advanced Settings</div>
-                    <div class="settings-group-body">
-                        <div class="slider-row">
-                            <label>Max new tokens</label>
-                            <input type="range" id="custom-max-new-tokens" min="1" max="{MAX_MAX_NEW_TOKENS}" step="1" value="{DEFAULT_MAX_NEW_TOKENS}">
-                            <span class="slider-val" id="custom-max-new-tokens-val">{DEFAULT_MAX_NEW_TOKENS}</span>
-                        </div>
-                        <div class="slider-row">
-                            <label>GPU Duration (seconds)</label>
-                            <input type="range" id="custom-gpu-duration" min="60" max="300" step="30" value="60">
-                            <span class="slider-val" id="custom-gpu-duration-val">60</span>
-                        </div>
-                    </div>
-                </div>
-            </div>
-        </div>
-
-        <div class="exp-note">
-            Experimental GLM-OCR workspace
-        </div>
-
-        <div class="app-statusbar">
-            <div class="sb-section" id="sb-media-status">No image uploaded</div>
-            <div class="sb-section sb-fixed" id="sb-run-state">Ready</div>
-        </div>
-    </div>
-    """)
-
-    run_btn = gr.Button("Run", elem_id="gradio-run-btn")
-
-    demo.load(fn=noop, inputs=None, outputs=None, js=gallery_js)
-    demo.load(fn=noop, inputs=None, outputs=None, js=wire_outputs_js)
 
     run_btn.click(
         fn=run_router,
         inputs=[
-            hidden_task_name,
-            hidden_image_b64,
+            input_mode,
+            task,
+            uploaded_pdf_state,
+            pdf_dropdown,
+            page_selection,
             max_new_tokens,
             gpu_duration_state,
         ],
         outputs=[result],
-        js=r"""(task, img, mnt, gd) => {
-            const taskEl = document.querySelector('.model-tab.active');
-            const taskVal = taskEl ? taskEl.getAttribute('data-task') : task;
-
-            let imgVal = img;
-            const imgContainer = document.getElementById('hidden-image-b64');
-            if (imgContainer) {
-                const inner = imgContainer.querySelector('textarea, input');
-                if (inner) imgVal = inner.value;
-            }
-
-            return [taskVal, imgVal, mnt, gd];
-        }""",
     )
 
-    example_load_btn.click(
-        fn=load_example_data,
-        inputs=[example_idx],
-        outputs=[example_result],
-        queue=False,
-    )
 
 if __name__ == "__main__":
-    demo.queue(max_size=50).launch(
-        css=css,
-        mcp_server=True,
-        ssr_mode=False,
-        show_error=True,
-        allowed_paths=["examples"],
-    )
+    launch_signature = inspect.signature(demo.launch)
+    launch_kwargs = {
+        "show_error": True,
+    }
+
+    if "ssr_mode" in launch_signature.parameters:
+        launch_kwargs["ssr_mode"] = False
+
+    if "mcp_server" in launch_signature.parameters:
+        launch_kwargs["mcp_server"] = True
+
+    demo.queue(max_size=50).launch(**launch_kwargs)