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README.md

@@ -1,14 +1,59 @@
 ---
-title: DeepseekOCR
-emoji: 📊
-colorFrom: yellow
-colorTo: purple
+title: DeepSeek OCR
+emoji: 🚀
+colorFrom: red
+colorTo: red
 sdk: gradio
-sdk_version: 6.0.2
+sdk_version: 5.49.1
 app_file: app.py
-pinned: false
+pinned: true
+short_description: DeepSeek-OCR demo on PDF and img
 license: mit
-short_description: Deepseek OCR Demo
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## API Usage
+
+### Using Gradio Client
+
+```python
+from gradio_client import Client
+
+client = Client("BinKhoaLe1812/DeepseekOCR")
+
+# Process a PDF or image file
+result = client.predict(
+    file_path="path/to/document.pdf",  # or image file
+    mode="Gundam",  # Options: Gundam, Tiny, Small, Base, Large
+    task="📋 Markdown",  # Options: 📋 Markdown, 📝 Free OCR, 📍 Locate, 🔍 Describe, ✏️ Custom
+    custom_prompt="",  # Required for Custom or Locate tasks
+    page_num=1,  # Page number (all pages are processed for PDFs)
+    api_name="/run"
+)
+
+# Result contains: [text_out, markdown_out, raw_out, img_out, gallery]
+text, markdown, raw, image, crops = result
+print(text)  # Extracted text
+print(markdown)  # Markdown formatted output
+```
+
+### Using HTTP API
+
+```python
+import requests
+
+# Upload file and process
+with open("document.pdf", "rb") as f:
+    files = {"files": f}
+    data = {
+        "mode": "Gundam",
+        "task": "📋 Markdown",
+        "custom_prompt": "",
+        "page_num": 1
+    }
+    response = requests.post(
+        "https://YOUR_USERNAME-YOUR_SPACE_NAME.hf.space/api/predict",
+        files=files,
+        data=data
+    )
+    result = response.json()
+```

app.py

@@ -0,0 +1,339 @@
+import gradio as gr
+from transformers import AutoModel, AutoTokenizer
+import torch
+import spaces
+import os
+import sys
+import tempfile
+import shutil
+from PIL import Image, ImageDraw, ImageFont, ImageOps
+import fitz
+import re
+import warnings
+import numpy as np
+import base64
+from io import StringIO, BytesIO
+
+MODEL_NAME = 'deepseek-ai/DeepSeek-OCR'
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
+model = AutoModel.from_pretrained(MODEL_NAME, _attn_implementation='flash_attention_2', torch_dtype=torch.bfloat16, trust_remote_code=True, use_safetensors=True)
+model = model.eval().cuda()
+
+MODEL_CONFIGS = {
+    "Gundam": {"base_size": 1024, "image_size": 640, "crop_mode": True},
+    "Tiny": {"base_size": 512, "image_size": 512, "crop_mode": False},
+    "Small": {"base_size": 640, "image_size": 640, "crop_mode": False},
+    "Base": {"base_size": 1024, "image_size": 1024, "crop_mode": False},
+    "Large": {"base_size": 1280, "image_size": 1280, "crop_mode": False}
+}
+
+TASK_PROMPTS = {
+    "📋 Markdown": {"prompt": "<image>\n<|grounding|>Convert the document to markdown.", "has_grounding": True},
+    "📝 Free OCR": {"prompt": "<image>\nFree OCR.", "has_grounding": False},
+    "📍 Locate": {"prompt": "<image>\nLocate <|ref|>text<|/ref|> in the image.", "has_grounding": True},
+    "🔍 Describe": {"prompt": "<image>\nDescribe this image in detail.", "has_grounding": False},
+    "✏️ Custom": {"prompt": "", "has_grounding": False}
+}
+
+def extract_grounding_references(text):
+    pattern = r'(<\|ref\|>(.*?)<\|/ref\|><\|det\|>(.*?)<\|/det\|>)'
+    return re.findall(pattern, text, re.DOTALL)
+
+def draw_bounding_boxes(image, refs, extract_images=False):
+    img_w, img_h = image.size
+    img_draw = image.copy()
+    draw = ImageDraw.Draw(img_draw)
+    overlay = Image.new('RGBA', img_draw.size, (0, 0, 0, 0))
+    draw2 = ImageDraw.Draw(overlay)
+    font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", 30)
+    crops = []
+    
+    color_map = {}
+    np.random.seed(42)
+
+    for ref in refs:
+        label = ref[1]
+        if label not in color_map:
+            color_map[label] = (np.random.randint(50, 255), np.random.randint(50, 255), np.random.randint(50, 255))
+
+        color = color_map[label]
+        coords = eval(ref[2])
+        color_a = color + (60,)
+        
+        for box in coords:
+            x1, y1, x2, y2 = int(box[0]/999*img_w), int(box[1]/999*img_h), int(box[2]/999*img_w), int(box[3]/999*img_h)
+            
+            if extract_images and label == 'image':
+                crops.append(image.crop((x1, y1, x2, y2)))
+            
+            width = 5 if label == 'title' else 3
+            draw.rectangle([x1, y1, x2, y2], outline=color, width=width)
+            draw2.rectangle([x1, y1, x2, y2], fill=color_a)
+            
+            text_bbox = draw.textbbox((0, 0), label, font=font)
+            tw, th = text_bbox[2] - text_bbox[0], text_bbox[3] - text_bbox[1]
+            ty = max(0, y1 - 20)
+            draw.rectangle([x1, ty, x1 + tw + 4, ty + th + 4], fill=color)
+            draw.text((x1 + 2, ty + 2), label, font=font, fill=(255, 255, 255))
+    
+    img_draw.paste(overlay, (0, 0), overlay)
+    return img_draw, crops
+
+def clean_output(text, include_images=False):
+    if not text:
+        return ""
+    pattern = r'(<\|ref\|>(.*?)<\|/ref\|><\|det\|>(.*?)<\|/det\|>)'
+    matches = re.findall(pattern, text, re.DOTALL)
+    img_num = 0
+    
+    for match in matches:
+        if '<|ref|>image<|/ref|>' in match[0]:
+            if include_images:
+                text = text.replace(match[0], f'\n\n**[Figure {img_num + 1}]**\n\n', 1)
+                img_num += 1
+            else:
+                text = text.replace(match[0], '', 1)
+        else:
+            text = re.sub(rf'(?m)^[^\n]*{re.escape(match[0])}[^\n]*\n?', '', text)
+    
+    return text.strip()
+
+def embed_images(markdown, crops):
+    if not crops:
+        return markdown
+    for i, img in enumerate(crops):
+        buf = BytesIO()
+        img.save(buf, format="PNG")
+        b64 = base64.b64encode(buf.getvalue()).decode()
+        markdown = markdown.replace(f'**[Figure {i + 1}]**', f'\n\n![Figure {i + 1}](data:image/png;base64,{b64})\n\n', 1)
+    return markdown
+
+@spaces.GPU(duration=60)
+def process_image(image, mode, task, custom_prompt):
+    if image is None:
+        return " Error Upload image", "", "", None, []
+    if task in ["✏️ Custom", "📍 Locate"] and not custom_prompt.strip():
+        return "Enter prompt", "", "", None, []
+    
+    if image.mode in ('RGBA', 'LA', 'P'):
+        image = image.convert('RGB')
+    image = ImageOps.exif_transpose(image)
+    
+    config = MODEL_CONFIGS[mode]
+    
+    if task == "✏️ Custom":
+        prompt = f"<image>\n{custom_prompt.strip()}"
+        has_grounding = '<|grounding|>' in custom_prompt
+    elif task == "📍 Locate":
+        prompt = f"<image>\nLocate <|ref|>{custom_prompt.strip()}<|/ref|> in the image."
+        has_grounding = True
+    else:
+        prompt = TASK_PROMPTS[task]["prompt"]
+        has_grounding = TASK_PROMPTS[task]["has_grounding"]
+    
+    tmp = tempfile.NamedTemporaryFile(delete=False, suffix='.jpg')
+    image.save(tmp.name, 'JPEG', quality=95)
+    tmp.close()
+    out_dir = tempfile.mkdtemp()
+    
+    stdout = sys.stdout
+    sys.stdout = StringIO()
+    
+    model.infer(tokenizer=tokenizer, prompt=prompt, image_file=tmp.name, output_path=out_dir,
+                base_size=config["base_size"], image_size=config["image_size"], crop_mode=config["crop_mode"])
+    
+    result = '\n'.join([l for l in sys.stdout.getvalue().split('\n') 
+                        if not any(s in l for s in ['image:', 'other:', 'PATCHES', '====', 'BASE:', '%|', 'torch.Size'])]).strip()
+    sys.stdout = stdout
+    
+    os.unlink(tmp.name)
+    shutil.rmtree(out_dir, ignore_errors=True)
+    
+    if not result:
+        return "No text", "", "", None, []
+    
+    cleaned = clean_output(result, False)
+    markdown = clean_output(result, True)
+    
+    img_out = None
+    crops = []
+    
+    if has_grounding and '<|ref|>' in result:
+        refs = extract_grounding_references(result)
+        if refs:
+            img_out, crops = draw_bounding_boxes(image, refs, True)
+    
+    markdown = embed_images(markdown, crops)
+    
+    return cleaned, markdown, result, img_out, crops
+
+@spaces.GPU(duration=60)
+def process_pdf(path, mode, task, custom_prompt, page_num):
+    doc = fitz.open(path)
+    total_pages = len(doc)
+    
+    # Process all pages
+    all_cleaned = []
+    all_markdown = []
+    all_raw = []
+    all_crops = []
+    img_out = None
+    
+    for page_idx in range(total_pages):
+        page = doc.load_page(page_idx)
+        pix = page.get_pixmap(matrix=fitz.Matrix(300/72, 300/72), alpha=False)
+        img = Image.open(BytesIO(pix.tobytes("png")))
+        
+        cleaned, markdown, result, page_img_out, page_crops = process_image(img, mode, task, custom_prompt)
+        
+        if page_idx == 0:
+            # Use first page's error message if there's an error
+            if cleaned.startswith(" Error") or cleaned.startswith("Enter prompt") or cleaned == "No text":
+                doc.close()
+                return cleaned, "", "", None, []
+        
+        all_cleaned.append(cleaned)
+        all_markdown.append(markdown)
+        all_raw.append(result)
+        all_crops.extend(page_crops)
+        
+        # Use the last page's bounding boxes image, or first if available
+        if page_img_out is not None:
+            img_out = page_img_out
+    
+    doc.close()
+    
+    # Combine results from all pages
+    combined_cleaned = "\n\n--- Page Break ---\n\n".join(all_cleaned)
+    combined_markdown = "\n\n--- Page Break ---\n\n".join(all_markdown)
+    combined_raw = "\n\n--- Page Break ---\n\n".join(all_raw)
+    
+    return combined_cleaned, combined_markdown, combined_raw, img_out, all_crops
+
+def process_file(path, mode, task, custom_prompt, page_num):
+    if not path:
+        return "Error Upload file", "", "", None, []
+    if path.lower().endswith('.pdf'):
+        return process_pdf(path, mode, task, custom_prompt, page_num)
+    else:
+        return process_image(Image.open(path), mode, task, custom_prompt)
+
+def toggle_prompt(task):
+    if task == "✏️ Custom":
+        return gr.update(visible=True, label="Custom Prompt", placeholder="Add <|grounding|> for boxes")
+    elif task == "📍 Locate":
+        return gr.update(visible=True, label="Text to Locate", placeholder="Enter text")
+    return gr.update(visible=False)
+
+def select_boxes(task):
+    if task == "📍 Locate":
+        return gr.update(selected="tab_boxes")
+    return gr.update()
+
+def get_pdf_page_count(file_path):
+    if not file_path or not file_path.lower().endswith('.pdf'):
+        return 1
+    doc = fitz.open(file_path)
+    count = len(doc)
+    doc.close()
+    return count
+
+def load_image(file_path, page_num=1):
+    if not file_path:
+        return None
+    if file_path.lower().endswith('.pdf'):
+        doc = fitz.open(file_path)
+        page_idx = max(0, min(int(page_num) - 1, len(doc) - 1))
+        page = doc.load_page(page_idx)
+        pix = page.get_pixmap(matrix=fitz.Matrix(300/72, 300/72), alpha=False)
+        img = Image.open(BytesIO(pix.tobytes("png")))
+        doc.close()
+        return img
+    else:
+        return Image.open(file_path)
+
+def update_page_selector(file_path):
+    if not file_path:
+        return gr.update(visible=False)
+    if file_path.lower().endswith('.pdf'):
+        page_count = get_pdf_page_count(file_path)
+        return gr.update(visible=True, maximum=page_count, value=1, minimum=1,
+                        label=f"Select Page (1-{page_count})")
+    return gr.update(visible=False)
+
+with gr.Blocks(theme=gr.themes.Soft(), title="DeepSeek-OCR") as demo:
+    gr.Markdown("""
+    # 🚀 DeepSeek-OCR
+    
+    **Document parser with OCR capabilities. Process multi-page PDFs and images to extract text, convert to markdown, or locate specific content with bounding boxes.**
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            file_in = gr.File(label="Upload Image or PDF", file_types=["image", ".pdf"], type="filepath")
+            input_img = gr.Image(label="Input Image", type="pil", height=300)
+            page_selector = gr.Number(label="Select Page", value=1, minimum=1, step=1, visible=False)
+            mode = gr.Dropdown(list(MODEL_CONFIGS.keys()), value="Gundam", label="Mode")
+            task = gr.Dropdown(list(TASK_PROMPTS.keys()), value="📋 Markdown", label="Task")
+            prompt = gr.Textbox(label="Prompt", lines=2, visible=False)
+            btn = gr.Button("Extract", variant="primary", size="lg")
+        
+        with gr.Column(scale=2):
+            with gr.Tabs() as tabs:
+                with gr.Tab("Text", id="tab_text"):
+                    text_out = gr.Textbox(lines=20, show_copy_button=True, show_label=False)
+                with gr.Tab("Markdown Preview", id="tab_markdown"):
+                    md_out = gr.Markdown("")
+                with gr.Tab("Boxes", id="tab_boxes"):
+                    img_out = gr.Image(type="pil", height=500, show_label=False)
+                with gr.Tab("Cropped Images", id="tab_crops"):
+                    gallery = gr.Gallery(show_label=False, columns=3, height=400)
+                with gr.Tab("Raw Text", id="tab_raw"):
+                    raw_out = gr.Textbox(lines=20, show_copy_button=True, show_label=False)
+    
+    gr.Examples(
+        examples=[
+            ["examples/ocr.jpg", "Gundam", "📋 Markdown", ""],
+            ["examples/reachy-mini.jpg", "Gundam", "📍 Locate", "Robot"]
+        ],
+        inputs=[input_img, mode, task, prompt],
+        cache_examples=False
+    )
+    
+    with gr.Accordion("ℹ️ Info", open=False):
+        gr.Markdown("""
+        ### Modes
+        - **Gundam**: 1024 base + 640 tiles with cropping - Best balance
+        - **Tiny**: 512×512, no crop - Fastest
+        - **Small**: 640×640, no crop - Quick
+        - **Base**: 1024×1024, no crop - Standard
+        - **Large**: 1280×1280, no crop - Highest quality
+        
+        ### Tasks
+        - **Markdown**: Convert document to structured markdown (grounding ✅)
+        - **Free OCR**: Simple text extraction
+        - **Locate**: Find specific things in image (grounding ✅)
+        - **Describe**: General image description
+        - **Custom**: Your own prompt (add `<|grounding|>` for boxes)
+        """)
+    
+    file_in.change(load_image, [file_in, page_selector], [input_img])
+    file_in.change(update_page_selector, [file_in], [page_selector])
+    page_selector.change(load_image, [file_in, page_selector], [input_img])
+    task.change(toggle_prompt, [task], [prompt])
+    task.change(select_boxes, [task], [tabs])
+    
+    def run(image, file_path, mode, task, custom_prompt, page_num):
+        if file_path:
+            return process_file(file_path, mode, task, custom_prompt, int(page_num))
+        if image is not None:
+            return process_image(image, mode, task, custom_prompt)
+        return "Error uploading file or image", "", "", None, []
+
+    submit_event = btn.click(run, [input_img, file_in, mode, task, prompt, page_selector],
+                             [text_out, md_out, raw_out, img_out, gallery])
+    submit_event.then(select_boxes, [task], [tabs])
+
+if __name__ == "__main__":
+    demo.queue(max_size=20).launch()

requirements.txt

@@ -0,0 +1,11 @@
+torch==2.6.0
+transformers==4.46.3
+tokenizers==0.20.3
+accelerate
+einops
+addict 
+easydict
+torchvision
+flash-attn @ https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.3/flash_attn-2.7.3+cu12torch2.6cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
+PyMuPDF
+hf_transfer