asdf

.gitattributes

@@ -38,11 +38,3 @@ images/2.jpg filter=lfs diff=lfs merge=lfs -text
 images/3.png filter=lfs diff=lfs merge=lfs -text
 videos/1.mp4 filter=lfs diff=lfs merge=lfs -text
 videos/2.mp4 filter=lfs diff=lfs merge=lfs -text
-images/0.png filter=lfs diff=lfs merge=lfs -text
-images/8.png filter=lfs diff=lfs merge=lfs -text
-examples/1.jpg filter=lfs diff=lfs merge=lfs -text
-examples/2.jpg filter=lfs diff=lfs merge=lfs -text
-examples/3.jpg filter=lfs diff=lfs merge=lfs -text
-examples/4.jpg filter=lfs diff=lfs merge=lfs -text
-examples/5.jpg filter=lfs diff=lfs merge=lfs -text
-examples/6.jpg filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,14 +1,14 @@
 ---
-title: Multimodal OCR2
+title: OCR2
 emoji: 💻
-colorFrom: gray
-colorTo: blue
+colorFrom: blue
+colorTo: gray
 sdk: gradio
-sdk_version: 6.9.0
+sdk_version: 5.34.2
 app_file: app.py
 pinned: true
 license: apache-2.0
-short_description: FireRed / Nanonets / Monkey / Thyme / Typhoon / SmolDocling
+short_description: nanonets ocr / typhoon ocr / smoldocling / monkey ocr
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,54 +1,50 @@
 import os
-import gc
-import re
-import ast
-import json
-import base64
 import random
-from io import BytesIO
+import uuid
+import json
+import time
+import asyncio
 from threading import Thread
 
 import gradio as gr
 import spaces
 import torch
+import numpy as np
 from PIL import Image, ImageOps
+import cv2
 
 from transformers import (
+    Qwen2VLForConditionalGeneration,
     Qwen2_5_VLForConditionalGeneration,
-    Qwen3VLForConditionalGeneration,
     AutoModelForVision2Seq,
     AutoProcessor,
     TextIteratorStreamer,
 )
+from transformers.image_utils import load_image
 
 from docling_core.types.doc import DoclingDocument, DocTagsDocument
 
+import re
+import ast
+import html
 
-MAX_MAX_NEW_TOKENS = 4096
-DEFAULT_MAX_NEW_TOKENS = 2048
+# Constants for text generation
+MAX_MAX_NEW_TOKENS = 2048
+DEFAULT_MAX_NEW_TOKENS = 1024
 MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-print("Using device:", device)
-
-MODEL_ID_F = "FireRedTeam/FireRed-OCR"
-processor_f = AutoProcessor.from_pretrained(MODEL_ID_F, trust_remote_code=True)
-model_f = Qwen3VLForConditionalGeneration.from_pretrained(
-    MODEL_ID_F,
-    attn_implementation="kernels-community/flash-attn2",
-    trust_remote_code=True,
-    torch_dtype=torch.float16
-).to(device).eval()
 
+# Load Nanonets-OCR-s
 MODEL_ID_M = "nanonets/Nanonets-OCR-s"
 processor_m = AutoProcessor.from_pretrained(MODEL_ID_M, trust_remote_code=True)
 model_m = Qwen2_5_VLForConditionalGeneration.from_pretrained(
     MODEL_ID_M,
-    attn_implementation="kernels-community/flash-attn2",
     trust_remote_code=True,
     torch_dtype=torch.float16
 ).to(device).eval()
 
+# Load MonkeyOCR
 MODEL_ID_G = "echo840/MonkeyOCR"
 SUBFOLDER = "Recognition"
 processor_g = AutoProcessor.from_pretrained(
@@ -58,21 +54,22 @@ processor_g = AutoProcessor.from_pretrained(
 )
 model_g = Qwen2_5_VLForConditionalGeneration.from_pretrained(
     MODEL_ID_G,
-    attn_implementation="kernels-community/flash-attn2",
     trust_remote_code=True,
     subfolder=SUBFOLDER,
     torch_dtype=torch.float16
 ).to(device).eval()
 
+# Load typhoon-ocr-7b
 MODEL_ID_L = "scb10x/typhoon-ocr-7b"
 processor_l = AutoProcessor.from_pretrained(MODEL_ID_L, trust_remote_code=True)
 model_l = Qwen2_5_VLForConditionalGeneration.from_pretrained(
     MODEL_ID_L,
-    attn_implementation="kernels-community/flash-attn2",
     trust_remote_code=True,
     torch_dtype=torch.float16
 ).to(device).eval()
 
+#--------------------------------------------------#
+# Load SmolDocling-256M-preview
 MODEL_ID_X = "ds4sd/SmolDocling-256M-preview"
 processor_x = AutoProcessor.from_pretrained(MODEL_ID_X, trust_remote_code=True)
 model_x = AutoModelForVision2Seq.from_pretrained(
@@ -80,125 +77,23 @@ model_x = AutoModelForVision2Seq.from_pretrained(
     trust_remote_code=True,
     torch_dtype=torch.float16
 ).to(device).eval()
+#--------------------------------------------------#
 
-MODEL_ID_N = "Kwai-Keye/Thyme-RL"
-processor_n = AutoProcessor.from_pretrained(MODEL_ID_N, trust_remote_code=True)
-model_n = Qwen2_5_VLForConditionalGeneration.from_pretrained(
-    MODEL_ID_N,
-    attn_implementation="kernels-community/flash-attn2",
-    trust_remote_code=True,
-    torch_dtype=torch.float16
-).to(device).eval()
-
-MODEL_MAP = {
-    "FireRed-OCR": (processor_f, model_f),
-    "Nanonets-OCR-s": (processor_m, model_m),
-    "MonkeyOCR-Recognition": (processor_g, model_g),
-    "Thyme-RL": (processor_n, model_n),
-    "Typhoon-OCR-7B": (processor_l, model_l),
-    "SmolDocling-256M-preview": (processor_x, model_x),
-}
-
-MODEL_CHOICES = list(MODEL_MAP.keys())
-
-image_examples = [
-    {"query": "Run OCR on the image and ensure high accuracy.", "image": "examples/4.jpg", "model": "Nanonets-OCR-s"},
-    {"query": "Conduct OCR on the image with exact text recognition.", "image": "examples/2.jpg", "model": "Typhoon-OCR-7B"},
-    {"query": "Generate Markdown", "image": "examples/6.jpg", "model": "SmolDocling-256M-preview"},
-    {"query": "Perform OCR on the image precisely.", "image": "examples/5.jpg", "model": "FireRed-OCR"},
-    {"query": "Perform precise OCR extraction on the image.", "image": "examples/1.jpg", "model": "Thyme-RL"},
-    {"query": "Convert this page to docling", "image": "examples/3.jpg", "model": "SmolDocling-256M-preview"},
-]
-
-
-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["image"])
-        prompt_short = ex["query"][:72] + ("..." if len(ex["query"]) > 72 else "")
-        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>
-            <div class="example-meta-row">
-                <span class="example-badge">{ex["model"]}</span>
-            </div>
-            <div class="example-prompt-text">{prompt_short}</div>
-        </div>
-        """
-    return cards
-
-
-EXAMPLE_CARDS_HTML = build_example_cards_html()
-
-
-def load_example_data(idx_str):
-    try:
-        idx = int(float(idx_str))
-    except Exception:
-        return json.dumps({"status": "error", "message": "Invalid example index"})
-    if idx < 0 or idx >= len(image_examples):
-        return json.dumps({"status": "error", "message": "Example index out of range"})
-    ex = image_examples[idx]
-    img_b64 = file_to_data_url(ex["image"])
-    if not img_b64:
-        return json.dumps({"status": "error", "message": "Could not load example image"})
-    return json.dumps({
-        "status": "ok",
-        "query": ex["query"],
-        "image": img_b64,
-        "model": ex["model"],
-        "name": os.path.basename(ex["image"]),
-    })
-
-
+# Preprocessing functions for SmolDocling-256M
 def add_random_padding(image, min_percent=0.1, max_percent=0.10):
+    """Add random padding to an image based on its size."""
     image = image.convert("RGB")
     width, height = image.size
     pad_w_percent = random.uniform(min_percent, max_percent)
     pad_h_percent = random.uniform(min_percent, max_percent)
     pad_w = int(width * pad_w_percent)
     pad_h = int(height * pad_h_percent)
-    corner_pixel = image.getpixel((0, 0))
+    corner_pixel = image.getpixel((0, 0))  # Top-left corner
     padded_image = ImageOps.expand(image, border=(pad_w, pad_h, pad_w, pad_h), fill=corner_pixel)
     return padded_image
 
-
 def normalize_values(text, target_max=500):
+    """Normalize numerical values in text to a target maximum."""
     def normalize_list(values):
         max_value = max(values) if values else 1
         return [round((v / max_value) * target_max) for v in values]
@@ -212,1102 +107,278 @@ def normalize_values(text, target_max=500):
     normalized_text = re.sub(pattern, process_match, text)
     return normalized_text
 
-
-def calc_timeout_image(model_name, text, image, max_new_tokens, temperature, top_p, top_k, repetition_penalty, gpu_timeout):
-    try:
-        return int(gpu_timeout)
-    except Exception:
-        return 60
-
-
-@spaces.GPU(duration=calc_timeout_image)
-def generate_image(model_name, text, image, max_new_tokens=1024, temperature=0.6, top_p=0.9, top_k=50, repetition_penalty=1.2, gpu_timeout=60):
-    buffer = ""
-    try:
-        if not model_name or model_name not in MODEL_MAP:
-            yield "[ERROR] Please select a valid model."
-            return
-        if image is None:
-            yield "[ERROR] Please upload an image."
-            return
-        if not text or not str(text).strip():
-            yield "[ERROR] Please enter your OCR/query instruction."
-            return
-        if len(str(text)) > MAX_INPUT_TOKEN_LENGTH * 8:
-            yield "[ERROR] Query is too long. Please shorten your input."
-            return
-
-        processor, model = MODEL_MAP[model_name]
-        images = [image]
-
-        if model_name == "SmolDocling-256M-preview":
-            if "OTSL" in text or "code" in text:
-                images = [add_random_padding(img) for img in images]
-            if "OCR at text at" in text or "Identify element" in text or "formula" in text:
-                text = normalize_values(text, target_max=500)
-
-        messages = [{
+def downsample_video(video_path):
+    """Downsample a video to evenly spaced frames, returning PIL images with timestamps."""
+    vidcap = cv2.VideoCapture(video_path)
+    total_frames = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))
+    fps = vidcap.get(cv2.CAP_PROP_FPS)
+    frames = []
+    frame_indices = np.linspace(0, total_frames - 1, 10, dtype=int)
+    for i in frame_indices:
+        vidcap.set(cv2.CAP_PROP_POS_FRAMES, i)
+        success, image = vidcap.read()
+        if success:
+            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+            pil_image = Image.fromarray(image)
+            timestamp = round(i / fps, 2)
+            frames.append((pil_image, timestamp))
+    vidcap.release()
+    return frames
+
+@spaces.GPU
+def generate_image(model_name: str, text: str, image: Image.Image,
+                   max_new_tokens: int = 1024,
+                   temperature: float = 0.6,
+                   top_p: float = 0.9,
+                   top_k: int = 50,
+                   repetition_penalty: float = 1.2):
+    """Generate responses for image input using the selected model."""
+    # Model selection
+    if model_name == "Nanonets-OCR-s":
+        processor = processor_m
+        model = model_m
+    elif model_name == "MonkeyOCR-Recognition":
+        processor = processor_g
+        model = model_g
+    elif model_name == "SmolDocling-256M-preview":
+        processor = processor_x
+        model = model_x
+    elif model_name == "Typhoon-OCR-7B":
+        processor = processor_l
+        model = model_l
+    else:
+        yield "Invalid model selected."
+        return
+
+    if image is None:
+        yield "Please upload an image."
+        return
+
+    # Prepare images as a list (single image for image inference)
+    images = [image]
+
+    # SmolDocling-256M specific preprocessing
+    if model_name == "SmolDocling-256M-preview":
+        if "OTSL" in text or "code" in text:
+            images = [add_random_padding(img) for img in images]
+        if "OCR at text at" in text or "Identify element" in text or "formula" in text:
+            text = normalize_values(text, target_max=500)
+
+    # Unified message structure for all models
+    messages = [
+        {
             "role": "user",
-            "content": [{"type": "image"} for _ in images] + [{"type": "text", "text": text}]
-        }]
-
-        prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
-        inputs = processor(text=prompt, images=images, return_tensors="pt").to(device)
-
-        streamer = TextIteratorStreamer(
-            processor.tokenizer if hasattr(processor, "tokenizer") else processor,
-            skip_prompt=True,
-            skip_special_tokens=True
-        )
-
-        generation_error = {"error": None}
-
-        generation_kwargs = {
-            **inputs,
-            "streamer": streamer,
-            "max_new_tokens": int(max_new_tokens),
-            "temperature": float(temperature),
-            "top_p": float(top_p),
-            "top_k": int(top_k),
-            "repetition_penalty": float(repetition_penalty),
+            "content": [{"type": "image"} for _ in images] + [
+                {"type": "text", "text": text}
+            ]
         }
-
-        def _run_generation():
-            try:
-                model.generate(**generation_kwargs)
-            except Exception as e:
-                generation_error["error"] = e
-                try:
-                    streamer.end()
-                except Exception:
-                    pass
-
-        thread = Thread(target=_run_generation, daemon=True)
-        thread.start()
-
-        for new_text in streamer:
-            buffer += new_text.replace("<|im_end|>", "")
-            yield buffer
-
-        thread.join(timeout=1.0)
-
-        if generation_error["error"] is not None:
-            err_msg = f"[ERROR] Inference failed: {str(generation_error['error'])}"
-            if buffer.strip():
-                yield buffer + "\n\n" + err_msg
-            else:
-                yield err_msg
-            return
-
-        if model_name == "SmolDocling-256M-preview":
-            cleaned_output = buffer.replace("<end_of_utterance>", "").strip()
-            if any(tag in cleaned_output for tag in ["<doctag>", "<otsl>", "<code>", "<chart>", "<formula>"]):
-                try:
-                    if "<chart>" in cleaned_output:
-                        cleaned_output = cleaned_output.replace("<chart>", "<otsl>").replace("</chart>", "</otsl>")
-                        cleaned_output = re.sub(r'(<loc_500>)(?!.*<loc_500>)<[^>]+>', r'\1', cleaned_output)
-                    doctags_doc = DocTagsDocument.from_doctags_and_image_pairs([cleaned_output], images)
-                    doc = DoclingDocument.load_from_doctags(doctags_doc, document_name="Document")
-                    markdown_output = doc.export_to_markdown()
-                    yield markdown_output
-                except Exception as e:
-                    yield f"[ERROR] Post-processing failed: {str(e)}"
-                    return
-            else:
-                if cleaned_output.strip():
-                    yield cleaned_output
-                else:
-                    yield "[ERROR] No output was generated."
-        else:
-            if not buffer.strip():
-                yield "[ERROR] No output was generated."
-
-    except Exception as e:
-        yield f"[ERROR] {str(e)}"
-    finally:
-        gc.collect()
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-
-
-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:1400px;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,#FF4500,#FF6A33,#FF8C66);
-    border-radius:10px;display:flex;align-items:center;justify-content:center;
-    box-shadow:0 4px 12px rgba(255,69,0,.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;
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-.app-badge{
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-    background:rgba(255,69,0,.12);color:#FF8A66;border:1px solid rgba(255,69,0,.25);letter-spacing:.3px;
-}
-.app-badge.fast{background:rgba(255,99,71,.10);color:#FF7043;border:1px solid rgba(255,99,71,.22)}
-
-.model-tabs-bar{
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-    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;
-    border-radius:999px;cursor:pointer;font-size:12px;font-weight:600;padding:0 12px;
-    color:#ffffff!important;transition:all .15s ease;
-}
-.model-tab:hover{background:rgba(255,69,0,.12);border-color:rgba(255,69,0,.35)}
-.model-tab.active{background:rgba(255,69,0,.22);border-color:#FF4500;color:#fff!important;box-shadow:0 0 0 2px rgba(255,69,0,.10)}
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-
-.app-main-row{display:flex;gap:0;flex:1;overflow:hidden}
-.app-main-left{flex:1;display:flex;flex-direction:column;min-width:0;border-right:1px solid #27272a}
-.app-main-right{width:470px;display:flex;flex-direction:column;flex-shrink:0;background:#18181b}
-
-#image-drop-zone{
-    position:relative;background:#09090b;height:440px;min-height:440px;max-height:440px;
-    overflow:hidden;
-}
-#image-drop-zone.drag-over{outline:2px solid #FF4500;outline-offset:-2px;background:rgba(255,69,0,.04)}
-.upload-prompt-modern{
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-.upload-sub-text{color:#71717a;font-size:12px}
-
-.single-preview-wrap{
-    width:100%;height:100%;display:none;align-items:center;justify-content:center;padding:16px;
-    overflow:hidden;
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-.single-preview-card{
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-    overflow:hidden;border:1px solid #27272a;background:#111114;
-    display:flex;align-items:center;justify-content:center;position:relative;
-}
-.single-preview-card img{
-    width:100%;height:100%;max-width:100%;max-height:100%;
-    object-fit:contain;display:block;
-}
-.preview-overlay-actions{
-    position:absolute;top:12px;right:12px;display:flex;gap:8px;z-index:5;
-}
-.preview-action-btn{
-    display:inline-flex;align-items:center;justify-content:center;
-    min-width:34px;height:34px;padding:0 12px;background:rgba(0,0,0,.65);
-    border:1px solid rgba(255,255,255,.14);border-radius:10px;cursor:pointer;
-    color:#fff!important;font-size:12px;font-weight:600;transition:all .15s ease;
-}
-.preview-action-btn:hover{background:#FF4500;border-color:#FF4500}
-
-.hint-bar{
-    background:rgba(255,69,0,.06);border-top:1px solid #27272a;border-bottom:1px solid #27272a;
-    padding:10px 20px;font-size:13px;color:#a1a1aa;line-height:1.7;
-}
-.hint-bar b{color:#FF8A66;font-weight:600}
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-}
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-    padding:0 10px 8px;font-size:11px;color:#a1a1aa;line-height:1.4;
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-    transition:all .2s ease;letter-spacing:-.2px;
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-}
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-    box-shadow:0 6px 24px rgba(255,69,0,.45),inset 0 1px 0 rgba(255,255,255,.15);
-}
-.btn-run:active{transform:translateY(0);box-shadow:0 2px 8px rgba(255,69,0,.3)}
-#custom-run-btn,#custom-run-btn *,#run-btn-label,.btn-run,.btn-run *{
-    color:#ffffff!important;-webkit-text-fill-color:#ffffff!important;fill:#ffffff!important;
-}
-body:not(.dark) .btn-run,body:not(.dark) .btn-run *,
-.dark .btn-run,.dark .btn-run *,
-.gradio-container .btn-run,.gradio-container .btn-run *,
-.gradio-container #custom-run-btn,.gradio-container #custom-run-btn *{
-    color:#ffffff!important;-webkit-text-fill-color:#ffffff!important;fill:#ffffff!important;
-}
-
-.output-frame{border-bottom:1px solid #27272a;display:flex;flex-direction:column;position:relative}
-.output-frame .out-title,
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-#output-title-label{
-    color:#ffffff!important;
-    -webkit-text-fill-color:#ffffff!important;
-}
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-    padding:10px 20px;font-size:13px;font-weight:700;
-    text-transform:uppercase;letter-spacing:.8px;border-bottom:1px solid rgba(39,39,42,.6);
-    display:flex;align-items:center;justify-content:space-between;gap:8px;flex-wrap:wrap;
-}
-.out-title-right{display:flex;gap:8px;align-items:center}
-.out-action-btn{
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-    border:1px solid rgba(255,69,0,.2);border-radius:6px;cursor:pointer;padding:3px 10px;
-    font-size:11px;font-weight:500;color:#FF8A66!important;gap:4px;height:24px;transition:all .15s;
-}
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-.out-action-btn svg{width:12px;height:12px;fill:#FF8A66}
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-    overflow:hidden;min-height:320px;position:relative;
-}
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-    width:100%;height:100%;padding:0;overflow:hidden;
-}
-.output-textarea{
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-    border:none;outline:none;padding:16px 18px;font-size:13px;line-height:1.6;
-    font-family:'JetBrains Mono',monospace;overflow:auto;resize:none;white-space:pre-wrap;
-}
-.output-textarea::placeholder{color:#52525b}
-.output-textarea.error-flash{
-    box-shadow:inset 0 0 0 2px rgba(239,68,68,.6);
-}
-.modern-loader{
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-    z-index:15;flex-direction:column;align-items:center;justify-content:center;gap:16px;backdrop-filter:blur(4px);
-}
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-.modern-loader .loader-spinner{
-    width:36px;height:36px;border:3px solid #27272a;border-top-color:#FF4500;
-    border-radius:50%;animation:spin .8s linear infinite;
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-    height:100%;background:linear-gradient(90deg,#FF4500,#FF7A4D,#FF4500);
-    background-size:200% 100%;animation:shimmer 1.5s ease-in-out infinite;border-radius:2px;
-}
-@keyframes shimmer{0%{background-position:200% 0}100%{background-position:-200% 0}}
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-    flex:1;-webkit-appearance:none;appearance:none;height:6px;background:#27272a;
-    border-radius:3px;outline:none;min-width:0;
-}
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-    -webkit-appearance:none;width:16px;height:16px;background:linear-gradient(135deg,#FF4500,#E03E00);
-    border-radius:50%;cursor:pointer;box-shadow:0 2px 6px rgba(255,69,0,.4);transition:transform .15s;
-}
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-.slider-row input[type="range"]::-moz-range-thumb{
-    width:16px;height:16px;background:linear-gradient(135deg,#FF4500,#E03E00);
-    border-radius:50%;cursor:pointer;border:none;box-shadow:0 2px 6px rgba(255,69,0,.4);
-}
-.slider-row .slider-val{
-    min-width:58px;text-align:right;font-family:'JetBrains Mono',monospace;font-size:12px;
-    font-weight:500;padding:3px 8px;background:#09090b;border:1px solid #27272a;
-    border-radius:6px;color:#a1a1aa;flex-shrink:0;
-}
-
-.app-statusbar{
-    background:#18181b;border-top:1px solid #27272a;padding:6px 20px;
-    display:flex;gap:12px;height:34px;align-items:center;font-size:12px;
-}
-.app-statusbar .sb-section{
-    padding:0 12px;flex:1;display:flex;align-items:center;font-family:'JetBrains Mono',monospace;
-    font-size:12px;color:#52525b;overflow:hidden;white-space:nowrap;
-}
-.app-statusbar .sb-section.sb-fixed{
-    flex:0 0 auto;min-width:110px;text-align:center;justify-content:center;
-    padding:3px 12px;background:rgba(255,69,0,.08);border-radius:6px;color:#FF8A66;font-weight:500;
-}
-
-.exp-note{padding:10px 20px;font-size:12px;color:#52525b;border-top:1px solid #27272a;text-align:center}
-.exp-note a{color:#FF8A66;text-decoration:none}
-.exp-note a:hover{text-decoration:underline}
-
-::-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.__ocr2InitDone) return;
-
-    const dropZone = document.getElementById('image-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 promptInput = document.getElementById('custom-query-input');
-    const runBtnEl = document.getElementById('custom-run-btn');
-    const outputArea = document.getElementById('custom-output-textarea');
-    const imgStatus = document.getElementById('sb-image-status');
-    const exampleResultContainer = document.getElementById('example-result-data');
-
-    if (!dropZone || !fileInput || !promptInput || !previewWrap || !previewImg) {
-        setTimeout(init, 250);
-        return;
+    ]
+    prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
+    inputs = processor(text=prompt, images=images, return_tensors="pt").to(device)
+
+    # Generation with streaming
+    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
+    generation_kwargs = {
+        **inputs,
+        "streamer": streamer,
+        "max_new_tokens": max_new_tokens,
+        "temperature": temperature,
+        "top_p": top_p,
+        "top_k": top_k,
+        "repetition_penalty": repetition_penalty,
     }
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
 
-    window.__ocr2InitDone = true;
-    let imageState = null;
-    let toastTimer = 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);
-    }
-    window.__showToast = showToast;
-
-    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.__showLoader = showLoader;
-    window.__hideLoader = hideLoader;
-    window.__setRunErrorState = setRunErrorState;
-
-    function flashPromptError() {
-        promptInput.classList.add('error-flash');
-        promptInput.focus();
-        setTimeout(() => promptInput.classList.remove('error-flash'), 800);
-    }
-
-    function flashOutputError() {
-        if (!outputArea) return;
-        outputArea.classList.add('error-flash');
-        setTimeout(() => outputArea.classList.remove('error-flash'), 800);
-    }
-
-    function setGradioValue(containerId, value) {
-        const container = document.getElementById(containerId);
-        if (!container) return;
-        container.querySelectorAll('input, textarea').forEach(el => {
-            if (el.type === 'file' || el.type === 'range' || el.type === 'checkbox') return;
-            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}));
-            }
-        });
-    }
-
-    function syncImageToGradio() {
-        setGradioValue('hidden-image-b64', imageState ? imageState.b64 : '');
-        const txt = imageState ? '1 image uploaded' : 'No image uploaded';
-        if (imgStatus) imgStatus.textContent = txt;
-    }
-
-    function syncPromptToGradio() {
-        setGradioValue('prompt-gradio-input', promptInput.value);
-    }
-
-    function syncModelToGradio(name) {
-        setGradioValue('hidden-model-name', name);
-    }
-
-    function setPreview(b64, name) {
-        imageState = {b64, name: name || 'image'};
-        previewImg.src = b64;
-        previewWrap.style.display = 'flex';
-        if (uploadPrompt) uploadPrompt.style.display = 'none';
-        syncImageToGradio();
-    }
-    window.__setPreview = setPreview;
-
-    function clearPreview() {
-        imageState = null;
-        previewImg.src = '';
-        previewWrap.style.display = 'none';
-        if (uploadPrompt) uploadPrompt.style.display = 'flex';
-        syncImageToGradio();
-    }
-    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) => setPreview(e.target.result, file.name);
-        reader.readAsDataURL(file);
-    }
-
-    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 = '';
-    });
-
-    if (uploadClick) uploadClick.addEventListener('click', () => fileInput.click());
-    if (btnUpload) btnUpload.addEventListener('click', () => fileInput.click());
-    if (btnClear) btnClear.addEventListener('click', clearPreview);
-
-    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]);
-    });
-
-    promptInput.addEventListener('input', syncPromptToGradio);
-
-    function activateModelTab(name) {
-        document.querySelectorAll('.model-tab[data-model]').forEach(btn => {
-            btn.classList.toggle('active', btn.getAttribute('data-model') === name);
-        });
-        syncModelToGradio(name);
-    }
-    window.__activateModelTab = activateModelTab;
-
-    document.querySelectorAll('.model-tab[data-model]').forEach(btn => {
-        btn.addEventListener('click', () => {
-            const model = btn.getAttribute('data-model');
-            activateModelTab(model);
-        });
-    });
-
-    activateModelTab('FireRed-OCR');
-
-    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-temperature', 'gradio-temperature');
-    syncSlider('custom-top-p', 'gradio-top-p');
-    syncSlider('custom-top-k', 'gradio-top-k');
-    syncSlider('custom-repetition-penalty', 'gradio-repetition-penalty');
-    syncSlider('custom-gpu-duration', 'gradio-gpu-duration');
-
-    function validateBeforeRun() {
-        const promptVal = promptInput.value.trim();
-        if (!imageState && !promptVal) {
-            showToast('Please upload an image and enter your OCR instruction', 'error');
-            flashPromptError();
-            return false;
-        }
-        if (!imageState) {
-            showToast('Please upload an image', 'error');
-            return false;
-        }
-        if (!promptVal) {
-            showToast('Please enter your OCR/query instruction', 'warning');
-            flashPromptError();
-            return false;
-        }
-        const currentModel = (document.querySelector('.model-tab.active') || {}).dataset?.model;
-        if (!currentModel) {
-            showToast('Please select a model', 'error');
-            return false;
+    # Stream output and collect full response
+    buffer = ""
+    full_output = ""
+    for new_text in streamer:
+        full_output += new_text
+        buffer += new_text.replace("<|im_end|>", "")
+        yield buffer
+
+    # SmolDocling-256M specific postprocessing
+    if model_name == "SmolDocling-256M-preview":
+        cleaned_output = full_output.replace("<end_of_utterance>", "").strip()
+        if any(tag in cleaned_output for tag in ["<doctag>", "<otsl>", "<code>", "<chart>", "<formula>"]):
+            if "<chart>" in cleaned_output:
+                cleaned_output = cleaned_output.replace("<chart>", "<otsl>").replace("</chart>", "</otsl>")
+                cleaned_output = re.sub(r'(<loc_500>)(?!.*<loc_500>)<[^>]+>', r'\1', cleaned_output)
+            doctags_doc = DocTagsDocument.from_doctags_and_image_pairs([cleaned_output], images)
+            doc = DoclingDocument.load_from_doctags(doctags_doc, document_name="Document")
+            markdown_output = doc.export_to_markdown()
+            yield f"**MD Output:**\n\n{markdown_output}"
+        else:
+            yield cleaned_output
+
+@spaces.GPU
+def generate_video(model_name: str, text: str, video_path: str,
+                   max_new_tokens: int = 1024,
+                   temperature: float = 0.6,
+                   top_p: float = 0.9,
+                   top_k: int = 50,
+                   repetition_penalty: float = 1.2):
+    """Generate responses for video input using the selected model."""
+    # Model selection
+    if model_name == "Nanonets-OCR-s":
+        processor = processor_m
+        model = model_m
+    elif model_name == "MonkeyOCR-Recognition":
+        processor = processor_g
+        model = model_g
+    elif model_name == "SmolDocling-256M-preview":
+        processor = processor_x
+        model = model_x
+    elif model_name == "Typhoon-OCR-7B":
+        processor = processor_l
+        model = model_l
+    else:
+        yield "Invalid model selected."
+        return
+
+    if video_path is None:
+        yield "Please upload a video."
+        return
+
+    # Extract frames from video
+    frames = downsample_video(video_path)
+    images = [frame for frame, _ in frames]
+
+    # SmolDocling-256M specific preprocessing
+    if model_name == "SmolDocling-256M-preview":
+        if "OTSL" in text or "code" in text:
+            images = [add_random_padding(img) for img in images]
+        if "OCR at text at" in text or "Identify element" in text or "formula" in text:
+            text = normalize_values(text, target_max=500)
+
+    # Unified message structure for all models
+    messages = [
+        {
+            "role": "user",
+            "content": [{"type": "image"} for _ in images] + [
+                {"type": "text", "text": text}
+            ]
         }
-        return true;
-    }
-
-    window.__clickGradioRunBtn = function() {
-        if (!validateBeforeRun()) return;
-        syncPromptToGradio();
-        syncImageToGradio();
-        const active = document.querySelector('.model-tab.active');
-        if (active) syncModelToGradio(active.getAttribute('data-model'));
-        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 = 'multimodal_ocr2_output.txt';
-            document.body.appendChild(a);
-            a.click();
-            setTimeout(() => {
-                URL.revokeObjectURL(a.href);
-                document.body.removeChild(a);
-            }, 200);
-            showToast('Output saved', 'info');
-        });
-    }
-
-    document.querySelectorAll('.example-card[data-idx]').forEach(card => {
-        card.addEventListener('click', () => {
-            const idx = card.getAttribute('data-idx');
-            document.querySelectorAll('.example-card.loading').forEach(c => c.classList.remove('loading'));
-            card.classList.add('loading');
-            showToast('Loading example...', 'info');
-            setGradioValue('example-result-data', '');
-            setGradioValue('example-idx-input', idx);
-            setTimeout(() => {
-                const btn = document.getElementById('example-load-btn');
-                if (btn) {
-                    const b = btn.querySelector('button');
-                    if (b) b.click(); else btn.click();
-                }
-            }, 150);
-            setTimeout(() => card.classList.remove('loading'), 12000);
-        });
-    });
-
-    function checkExampleResult() {
-        if (!exampleResultContainer) return;
-        const el = exampleResultContainer.querySelector('textarea') || exampleResultContainer.querySelector('input');
-        if (!el || !el.value) return;
-        if (window.__lastExampleVal === el.value) return;
-        try {
-            const data = JSON.parse(el.value);
-            if (data.status === 'ok') {
-                window.__lastExampleVal = el.value;
-                if (data.image) setPreview(data.image, data.name || 'example.jpg');
-                if (data.query) {
-                    promptInput.value = data.query;
-                    syncPromptToGradio();
-                }
-                if (data.model) activateModelTab(data.model);
-                document.querySelectorAll('.example-card.loading').forEach(c => c.classList.remove('loading'));
-                showToast('Example loaded', 'info');
-            } else if (data.status === 'error') {
-                document.querySelectorAll('.example-card.loading').forEach(c => c.classList.remove('loading'));
-                showToast(data.message || 'Failed to load example', 'error');
-            }
-        } catch(e) {}
-    }
-
-    const obsExample = new MutationObserver(checkExampleResult);
-    if (exampleResultContainer) {
-        obsExample.observe(exampleResultContainer, {childList:true, subtree:true, characterData:true, attributes:true});
+    ]
+    prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
+    inputs = processor(text=prompt, images=images, return_tensors="pt").to(device)
+
+    # Generation with streaming
+    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
+    generation_kwargs = {
+        **inputs,
+        "streamer": streamer,
+        "max_new_tokens": max_new_tokens,
+        "temperature": temperature,
+        "top_p": top_p,
+        "top_k": top_k,
+        "repetition_penalty": repetition_penalty,
     }
-    setInterval(checkExampleResult, 500);
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
 
-    if (outputArea) outputArea.value = '';
-    const sb = document.getElementById('sb-run-state');
-    if (sb) sb.textContent = 'Ready';
-    if (imgStatus) imgStatus.textContent = 'No image uploaded';
-}
-init();
-}
-"""
-
-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; }
-
-    let lastText = '';
-
-    function isErrorText(val) {
-        return typeof val === 'string' && val.trim().startsWith('[ERROR]');
-    }
+    # Stream output and collect full response
+    buffer = ""
+    full_output = ""
+    for new_text in streamer:
+        full_output += new_text
+        buffer += new_text.replace("<|im_end|>", "")
+        yield buffer
+
+    # SmolDocling-256M specific postprocessing
+    if model_name == "SmolDocling-256M-preview":
+        cleaned_output = full_output.replace("<end_of_utterance>", "").strip()
+        if any(tag in cleaned_output for tag in ["<doctag>", "<otsl>", "<code>", "<chart>", "<formula>"]):
+            if "<chart>" in cleaned_output:
+                cleaned_output = cleaned_output.replace("<chart>", "<otsl>").replace("</chart>", "</otsl>")
+                cleaned_output = re.sub(r'(<loc_500>)(?!.*<loc_500>)<[^>]+>', r'\1', cleaned_output)
+            doctags_doc = DocTagsDocument.from_doctags_and_image_pairs([cleaned_output], images)
+            doc = DoclingDocument.load_from_doctags(doctags_doc, document_name="Document")
+            markdown_output = doc.export_to_markdown()
+            yield f"**MD Output:**\n\n{markdown_output}"
+        else:
+            yield cleaned_output
 
-    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;
+# Define examples for image and video inference
+image_examples = [
+    ["OCR the image", "images/2.jpg"],
+    ["Convert this page to docling", "images/1.png"],
+    ["Convert this page to docling", "images/3.png"],
+    ["Convert chart to OTSL.", "images/4.png"],
+    ["Convert code to text", "images/5.jpg"],
+    ["Convert this table to OTSL.", "images/6.jpg"],
+    ["Convert formula to late.", "images/7.jpg"],
+]
 
-            if (val.trim()) {
-                if (isErrorText(val)) {
-                    if (window.__setRunErrorState) window.__setRunErrorState();
-                    if (window.__showToast) window.__showToast('OCR failed', 'error');
-                } else {
-                    if (window.__hideLoader) window.__hideLoader();
-                }
-            }
-        }
-    }
+video_examples = [
+    ["Explain the video in detail.", "videos/1.mp4"],
+    ["Explain the video in detail.", "videos/2.mp4"]
+]
 
-    const observer = new MutationObserver(syncOutput);
-    observer.observe(resultContainer, {childList:true, subtree:true, characterData:true, attributes:true});
-    setInterval(syncOutput, 500);
+css = """
+.submit-btn {
+    background-color: #2980b9 !important;
+    color: white !important;
 }
-watchOutputs();
+.submit-btn:hover {
+    background-color: #3498db !important;
 }
 """
 
-OCR_LOGO_SVG = """
-<svg viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg">
-  <path d="M4 5.5A2.5 2.5 0 0 1 6.5 3H11v2H6.5a.5.5 0 0 0-.5.5V10H4V5.5Z" fill="white"/>
-  <path d="M20 10h-2V5.5a.5.5 0 0 0-.5-.5H13V3h4.5A2.5 2.5 0 0 1 20 5.5V10Z" fill="white"/>
-  <path d="M4 14h2v4.5a.5.5 0 0 0 .5.5H11v2H6.5A2.5 2.5 0 0 1 4 18.5V14Z" fill="white"/>
-  <path d="M20 14v4.5A2.5 2.5 0 0 1 17.5 21H13v-2h4.5a.5.5 0 0 0 .5-.5V14h2Z" fill="white"/>
-  <path d="M8 8h8v2H8V8Zm0 3h8v2H8v-2Zm0 3h5v2H8v-2Z" 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="#FF4500" stroke-width="2" stroke-dasharray="4 3"/>
-    <polygon points="12,62 30,40 42,50 54,34 68,62" fill="rgba(255,69,0,0.15)" stroke="#FF4500" stroke-width="1.5"/>
-    <circle cx="28" cy="30" r="6" fill="rgba(255,69,0,0.2)" stroke="#FF4500" 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>"""
-
-MODEL_TABS_HTML = "".join([
-    f'<button class="model-tab{" active" if m == "FireRed-OCR" else ""}" data-model="{m}"><span class="model-tab-label">{m}</span></button>'
-    for m in MODEL_CHOICES
-])
-
-with gr.Blocks() as demo:
-    hidden_image_b64 = gr.Textbox(value="", elem_id="hidden-image-b64", elem_classes="hidden-input", container=False)
-    prompt = gr.Textbox(value="", elem_id="prompt-gradio-input", elem_classes="hidden-input", container=False)
-    hidden_model_name = gr.Textbox(value="FireRed-OCR", elem_id="hidden-model-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)
-    temperature = gr.Slider(minimum=0.1, maximum=4.0, step=0.1, value=0.6, elem_id="gradio-temperature", elem_classes="hidden-input", container=False)
-    top_p = gr.Slider(minimum=0.05, maximum=1.0, step=0.05, value=0.9, elem_id="gradio-top-p", elem_classes="hidden-input", container=False)
-    top_k = gr.Slider(minimum=1, maximum=1000, step=1, value=50, elem_id="gradio-top-k", elem_classes="hidden-input", container=False)
-    repetition_penalty = gr.Slider(minimum=1.0, maximum=2.0, step=0.05, value=1.2, elem_id="gradio-repetition-penalty", elem_classes="hidden-input", container=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">{OCR_LOGO_SVG}</div>
-                <span class="app-title">Multimodal OCR 2</span>
-                <span class="app-badge">vision enabled</span>
-                <span class="app-badge fast">OCR Suite</span>
-            </div>
-        </div>
-
-        <div class="model-tabs-bar">
-            {MODEL_TABS_HTML}
-        </div>
-
-        <div class="app-main-row">
-            <div class="app-main-left">
-                <div id="image-drop-zone">
-                    <div id="upload-prompt" class="upload-prompt-modern">
-                        <div id="upload-click-area" class="upload-click-area">
-                            {UPLOAD_PREVIEW_SVG}
-                            <span class="upload-main-text">Click or drag an image here</span>
-                            <span class="upload-sub-text">Upload one document, page, receipt, screenshot, or scene image for OCR and vision tasks</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">
-                            <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>Upload:</b> Click or drag to add an image &nbsp;&middot;&nbsp;
-                    <b>Model:</b> Switch model tabs from the header &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 class="panel-card-title">OCR / Vision Instruction</div>
-                    <div class="panel-card-body">
-                        <label class="modern-label" for="custom-query-input">Query Input</label>
-                        <textarea id="custom-query-input" class="modern-textarea" rows="4" placeholder="e.g., perform OCR on the image precisely, extract all text, generate markdown, read the receipt, summarize the page..."></textarea>
-                    </div>
-                </div>
-
-                <div style="padding:12px 20px;">
-                    <button id="custom-run-btn" class="btn-run">
-                        <span id="run-btn-label">Run OCR</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 OCR...</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>Temperature</label>
-                            <input type="range" id="custom-temperature" min="0.1" max="4.0" step="0.1" value="0.6">
-                            <span class="slider-val" id="custom-temperature-val">0.6</span>
-                        </div>
-                        <div class="slider-row">
-                            <label>Top-p</label>
-                            <input type="range" id="custom-top-p" min="0.05" max="1.0" step="0.05" value="0.9">
-                            <span class="slider-val" id="custom-top-p-val">0.9</span>
-                        </div>
-                        <div class="slider-row">
-                            <label>Top-k</label>
-                            <input type="range" id="custom-top-k" min="1" max="1000" step="1" value="50">
-                            <span class="slider-val" id="custom-top-k-val">50</span>
-                        </div>
-                        <div class="slider-row">
-                            <label>Repetition penalty</label>
-                            <input type="range" id="custom-repetition-penalty" min="1.0" max="2.0" step="0.05" value="1.2">
-                            <span class="slider-val" id="custom-repetition-penalty-val">1.2</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 OCR Suite &middot; Open on <a href="https://github.com/PRITHIVSAKTHIUR/Multimodal-OCR2" target="_blank">GitHub</a>
-        </div>
-
-        <div class="app-statusbar">
-            <div class="sb-section" id="sb-image-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")
-
-    def b64_to_pil(b64_str):
-        if not b64_str:
-            return None
-        try:
-            if b64_str.startswith("data:image"):
-                _, data = b64_str.split(",", 1)
-            else:
-                data = b64_str
-            image_data = base64.b64decode(data)
-            return Image.open(BytesIO(image_data)).convert("RGB")
-        except Exception:
-            return None
-
-    def run_ocr(model_name, text, image_b64, max_new_tokens_v, temperature_v, top_p_v, top_k_v, repetition_penalty_v, gpu_timeout_v):
-        try:
-            image = b64_to_pil(image_b64)
-            yield from generate_image(
-                model_name=model_name,
-                text=text,
-                image=image,
-                max_new_tokens=max_new_tokens_v,
-                temperature=temperature_v,
-                top_p=top_p_v,
-                top_k=top_k_v,
-                repetition_penalty=repetition_penalty_v,
-                gpu_timeout=gpu_timeout_v,
+# Create the Gradio Interface
+with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
+    gr.Markdown("# **[Multimodal OCR2](https://huggingface.co/collections/prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0)**")
+    with gr.Row():
+        with gr.Column():
+            with gr.Tabs():
+                with gr.TabItem("Image Inference"):
+                    image_query = gr.Textbox(label="Query Input", placeholder="Enter your query here...")
+                    image_upload = gr.Image(type="pil", label="Image")
+                    image_submit = gr.Button("Submit", elem_classes="submit-btn")
+                    gr.Examples(
+                        examples=image_examples,
+                        inputs=[image_query, image_upload]
+                    )
+                with gr.TabItem("Video Inference"):
+                    video_query = gr.Textbox(label="Query Input", placeholder="Enter your query here...")
+                    video_upload = gr.Video(label="Video")
+                    video_submit = gr.Button("Submit", elem_classes="submit-btn")
+                    gr.Examples(
+                        examples=video_examples,
+                        inputs=[video_query, video_upload]
+                    )
+            with gr.Accordion("Advanced options", open=False):
+                max_new_tokens = gr.Slider(label="Max new tokens", minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS)
+                temperature = gr.Slider(label="Temperature", minimum=0.1, maximum=4.0, step=0.1, value=0.6)
+                top_p = gr.Slider(label="Top-p (nucleus sampling)", minimum=0.05, maximum=1.0, step=0.05, value=0.9)
+                top_k = gr.Slider(label="Top-k", minimum=1, maximum=1000, step=1, value=50)
+                repetition_penalty = gr.Slider(label="Repetition penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.2)
+        with gr.Column():
+            output = gr.Textbox(label="Output", interactive=False, lines=3, scale=2)
+            model_choice = gr.Radio(
+                choices=["Nanonets-OCR-s", "MonkeyOCR-Recognition", "SmolDocling-256M-preview", "Typhoon-OCR-7B"],
+                label="Select Model",
+                value="Nanonets-OCR-s"
             )
-        except Exception as e:
-            yield f"[ERROR] {str(e)}"
-
-    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_ocr,
-        inputs=[
-            hidden_model_name,
-            prompt,
-            hidden_image_b64,
-            max_new_tokens,
-            temperature,
-            top_p,
-            top_k,
-            repetition_penalty,
-            gpu_duration_state,
-        ],
-        outputs=[result],
-        js=r"""(m, p, img, mnt, t, tp, tk, rp, gd) => {
-            const modelEl = document.querySelector('.model-tab.active');
-            const model = modelEl ? modelEl.getAttribute('data-model') : m;
-            const promptEl = document.getElementById('custom-query-input');
-            const promptVal = promptEl ? promptEl.value : p;
-            const imgContainer = document.getElementById('hidden-image-b64');
-            let imgVal = img;
-            if (imgContainer) {
-                const inner = imgContainer.querySelector('textarea, input');
-                if (inner) imgVal = inner.value;
-            }
-            return [model, promptVal, imgVal, mnt, t, tp, tk, rp, gd];
-        }""",
+            
+            gr.Markdown("**Model Info 💻** | [Report Bug](https://huggingface.co/spaces/prithivMLmods/Multimodal-OCR2/discussions)")
+
+            gr.Markdown("> [SmolDocling-256M](https://huggingface.co/ds4sd/SmolDocling-256M-preview): SmolDocling is a multimodal Image-Text-to-Text model designed for efficient document conversion. It retains Docling's most popular features while ensuring full compatibility with Docling through seamless support for DoclingDocuments.")
+            gr.Markdown("> [Nanonets-OCR-s](https://huggingface.co/nanonets/Nanonets-OCR-s): nanonets-ocr-s is a powerful, state-of-the-art image-to-markdown ocr model that goes far beyond traditional text extraction. it transforms documents into structured markdown with intelligent content recognition and semantic tagging.")
+            gr.Markdown("> [MonkeyOCR-Recognition](https://huggingface.co/echo840/MonkeyOCR): MonkeyOCR adopts a Structure-Recognition-Relation (SRR) triplet paradigm, which simplifies the multi-tool pipeline of modular approaches while avoiding the inefficiency of using large multimodal models for full-page document processing.")
+            gr.Markdown("> [Typhoon-OCR-7B](https://huggingface.co/scb10x/typhoon-ocr-7b): A bilingual document parsing model built specifically for real-world documents in Thai and English inspired by models like olmOCR based on Qwen2.5-VL-Instruction. Extracts and interprets embedded text (e.g., chart labels, captions) in Thai or English.")
+
+    image_submit.click(
+        fn=generate_image,
+        inputs=[model_choice, image_query, image_upload, max_new_tokens, temperature, top_p, top_k, repetition_penalty],
+        outputs=output
     )
-
-    example_load_btn.click(
-        fn=load_example_data,
-        inputs=[example_idx],
-        outputs=[example_result],
-        queue=False,
+    video_submit.click(
+        fn=generate_video,
+        inputs=[model_choice, video_query, video_upload, max_new_tokens, temperature, top_p, top_k, repetition_penalty],
+        outputs=output
     )
 
 if __name__ == "__main__":
-    demo.queue(max_size=50).launch(
-        css=css,
-        mcp_server=True,
-        ssr_mode=False,
-        show_error=True,
-        allowed_paths=["examples"],
-    )
+    demo.queue(max_size=30).launch(share=True, mcp_server=True, ssr_mode=False, show_error=True)

pre-requirements.txt

@@ -1 +0,0 @@
-pip>=26.0.0

requirements.txt

@@ -1,19 +1,15 @@
-git+https://github.com/huggingface/transformers.git@v4.57.6
-git+https://github.com/huggingface/accelerate.git
-git+https://github.com/huggingface/peft.git
-transformers-stream-generator
-huggingface_hub
-qwen-vl-utils
-sentencepiece
-opencv-python
-torch==2.8.0
-docling-core
-torchvision
-matplotlib
-requests
-kernels
-hf_xet
-spaces
-pillow
-gradio
+gradio
+transformers
+transformers-stream-generator
+qwen-vl-utils
+torchvision
+docling-core
+torch
+requests
+huggingface_hub
+albumentations
+spaces
+accelerate
+pillow
+opencv-python
 av

examples/4.jpg → videos/1.mp4

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:6b97b675f13f09b0c03f1e222d9c89be146370e067a38fed7bf971638717bd87
-size 176412
+oid sha256:9127aaafccef6f02fce6812bc9c89e1e4026832cf133492481952cc4b94cb595
+size 791367

videos/2.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bdf85ced4e76f2afd1a66b2c41e93868ccd9f928a02105de5e7db3c8651c692e
+size 1040341