Update app.py

app.py

@@ -1,70 +1,337 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
-    """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-    """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
-
-    messages = [{"role": "system", "content": system_message}]
-
-    messages.extend(history)
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+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
+
+# 模型路径配置
+# 方式1: 使用在线模型（默认）
+MODEL_PATH = 'deepseek-ai/DeepSeek-OCR'
+
+# 方式2: 使用本地下载的模型（推荐）
+# 将模型下载到本地后，修改为本地路径，例如：
+# MODEL_PATH = './models/DeepSeek-OCR'  # 本地模型路径
+# MODEL_PATH = 'E:/hugging_face/models/DeepSeek-OCR'  # 或使用绝对路径
+
+# 如果本地路径不存在，则使用在线模型
+if not os.path.exists(MODEL_PATH):
+    print(f"本地模型路径不存在: {MODEL_PATH}")
+    print("将使用在线模型: deepseek-ai/DeepSeek-OCR")
+    MODEL_PATH = 'deepseek-ai/DeepSeek-OCR'
+else:
+    print(f"使用本地模型: {MODEL_PATH}")
+
+# Auto-detect device (GPU if available, else CPU)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float32
+print(f"使用设备: {device}, 数据类型: {torch_dtype}")
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True)
+model = AutoModel.from_pretrained(
+    MODEL_PATH, 
+    trust_remote_code=True, 
+    use_safetensors=True,
+    torch_dtype=torch_dtype
 )
+model = model.eval().to(device)
+
+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.load_default()
+    crops = []
+
+    for ref in refs:
+        label = ref[1]
+        coords = eval(ref[2])
+        color = (np.random.randint(50, 255), np.random.randint(
+            50, 255), np.random.randint(50, 255))
+        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, remove_labels=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:
+            if remove_labels:
+                text = text.replace(match[0], '', 1)
+            else:
+                text = text.replace(match[0], match[1], 1)
+
+    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, False)
+    markdown = clean_output(result, True, 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=300)
+def process_pdf(path, mode, task, custom_prompt):
+    doc = fitz.open(path)
+    texts, markdowns, raws, all_crops = [], [], [], []
+
+    for i in range(len(doc)):
+        page = doc.load_page(i)
+        pix = page.get_pixmap(matrix=fitz.Matrix(300/72, 300/72), alpha=False)
+        img = Image.open(BytesIO(pix.tobytes("png")))
+
+        text, md, raw, _, crops = process_image(img, mode, task, custom_prompt)
+
+        if text and text != "No text":
+            texts.append(f"### Page {i + 1}\n\n{text}")
+            markdowns.append(f"### Page {i + 1}\n\n{md}")
+            raws.append(f"=== Page {i + 1} ===\n{raw}")
+            all_crops.extend(crops)
+
+    doc.close()
+
+    return ("\n\n---\n\n".join(texts) if texts else "No text in PDF",
+            "\n\n---\n\n".join(markdowns) if markdowns else "No text in PDF",
+            "\n\n".join(raws), None, all_crops)
+
+
+def process_file(path, mode, task, custom_prompt):
+    if not path:
+        return "Error Upload file", "", "", None, []
+
+    if path.lower().endswith('.pdf'):
+        return process_pdf(path, mode, task, custom_prompt)
+    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 load_image(file_path):
+    if not file_path:
+        return None
+    if file_path.lower().endswith('.pdf'):
+        doc = fitz.open(file_path)
+        page = doc.load_page(0)
+        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)
+
+
+with gr.Blocks(theme=gr.themes.Soft(), title="DeepSeek-OCR") as demo:
+    gr.Markdown("""
+    # 🚀 DeepSeek-OCR Demo
+    **Convert documents to markdown, extract raw text, and locate specific content with bounding boxes. Check the info at the bottom of the page for more information.**
+    
+    **Hope this tool was helpful! If so, a quick like ❤️ would mean a lot :)**
+    """)
+
+    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)
+            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():
+                with gr.Tab("📝 Text"):
+                    text_out = gr.Textbox(
+                        lines=20, show_copy_button=True, show_label=False)
+                with gr.Tab("🎨 Markdown"):
+                    md_out = gr.Markdown("")
+                with gr.Tab("🖼️ Boxes"):
+                    img_out = gr.Image(
+                        type="pil", height=500, show_label=False)
+                with gr.Tab("🖼️ Cropped Images"):
+                    gallery = gr.Gallery(
+                        show_label=False, columns=3, height=400)
+                with gr.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 text in image (grounding ✅)
+        - **Describe**: General image description
+        - **Custom**: Your own prompt (add `<|grounding|>` for boxes)
+        """)
+
+    file_in.change(load_image, [file_in], [input_img])
+    task.change(toggle_prompt, [task], [prompt])
 
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
+    def run(image, file_path, mode, task, custom_prompt):
+        if image is not None:
+            return process_image(image, mode, task, custom_prompt)
+        if file_path:
+            return process_file(file_path, mode, task, custom_prompt)
+        return "Error uploading file or image", "", "", None, []
 
+    btn.click(run, [input_img, file_in, mode, task, prompt],
+              [text_out, md_out, raw_out, img_out, gallery])
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.queue(max_size=20).launch()