init commit

app.py

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+import os
+import torch
+import numpy as np
+from PIL import Image
+from transformers import AutoProcessor
+from qwen_vl_utils import process_vision_info  # 请确保该模块在你的环境可用
+from transformers import HunYuanVLForConditionalGeneration
+import gradio as gr
+from argparse import ArgumentParser
+import copy
+import requests
+from io import BytesIO
+import tempfile
+import hashlib
+import gc
+
+
+
+
+def _get_args():
+    parser = ArgumentParser()
+
+    parser.add_argument('-c',
+                        '--checkpoint-path',
+                        type=str,
+                        default='tencent/HunyuanOCR',
+                        help='Checkpoint name or path, default to %(default)r')
+    parser.add_argument('--cpu-only', action='store_true', help='Run demo with CPU only')
+
+    parser.add_argument('--flash-attn2',
+                        action='store_true',
+                        default=False,
+                        help='Enable flash_attention_2 when loading the model.')
+    parser.add_argument('--share',
+                        action='store_true',
+                        default=False,
+                        help='Create a publicly shareable link for the interface.')
+    parser.add_argument('--inbrowser',
+                        action='store_true',
+                        default=False,
+                        help='Automatically launch the interface in a new tab on the default browser.')
+    # parser.add_argument('--server-port', type=int, default=8080, help='Demo server port.')
+    # parser.add_argument('--server-name', type=str, default='29.210.129.176', help='Demo server name.')
+
+    args = parser.parse_args()
+    return args
+
+
+def _load_model_processor(args):
+    model = HunYuanVLForConditionalGeneration.from_pretrained(
+        args.checkpoint_path,
+        attn_implementation="eager",  # "flash_attention_2", #也可以是 flash_attention_2 或 sdpa，根据你的环境支持情况选择
+        torch_dtype=torch.bfloat16,
+        device_map="auto",
+        token=os.environ.get('HF_TOKEN')
+    )
+    processor = AutoProcessor.from_pretrained(args.checkpoint_path, use_fast=False, trust_remote_code=True)
+    return model, processor
+
+
+def _parse_text(text):
+    """解析文本，处理特殊格式"""
+    # if text is None:
+    #     return text
+    text = text.replace("<trans>", "").replace("</trans>", "")
+    return text
+
+
+def _remove_image_special(text):
+    """移除图像特殊标记"""
+    # if text is None:
+    #     return text
+    # # 移除可能的图像特殊标记
+    # import re
+    # text = re.sub(r'<image>|</image>|<img>|</img>', '', text)
+    # return text
+    return text
+
+
+def _gc():
+    """垃圾回收"""
+    gc.collect()
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+
+
+def _launch_demo(args, model, processor):
+    def call_local_model(model, processor, messages):
+        print(messages)
+        messages = [messages]
+        # 使用 processor 构造输入格式
+        texts = [
+            processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True)
+            for msg in messages
+        ]
+        image_inputs, video_inputs = process_vision_info(messages)
+        inputs = processor(
+            text=texts,
+            images=image_inputs,
+            videos=video_inputs,
+            padding=True,
+            return_tensors="pt",
+        )
+        inputs = inputs.to(model.device)
+        
+
+        # gen_kwargs = {'max_new_tokens': 32768, 'streamer': streamer, **inputs}
+        # thread = Thread(target=model.generate, kwargs=gen_kwargs)
+        # thread.start()
+
+        # generated_text = ''
+        # for new_text in streamer:
+        #     generated_text += new_text
+        #     yield generated_text
+
+        # 模型推理
+        with torch.no_grad():
+            generated_ids = model.generate(
+                **inputs,
+                max_new_tokens=1024*8,
+                repetition_penalty=1.03,
+                do_sample=False
+            )
+        
+        # 解码输出
+        if "input_ids" in inputs:
+            input_ids = inputs.input_ids
+        else:
+            input_ids = inputs.inputs  # fallback
+
+        generated_ids_trimmed = [
+            out_ids[len(in_ids):] for in_ids, out_ids in zip(input_ids, generated_ids)
+        ]
+
+        output_texts = processor.batch_decode(
+            generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+        )
+
+        return output_texts
+    
+
+    def create_predict_fn():
+
+        def predict(_chatbot, task_history):
+            nonlocal model, processor
+            chat_query = _chatbot[-1][0]
+            query = task_history[-1][0]
+            if len(chat_query) == 0:
+                _chatbot.pop()
+                task_history.pop()
+                return _chatbot
+            print('User: ', query)
+            history_cp = copy.deepcopy(task_history)
+            full_response = ''
+            messages = []
+            content = []
+            for q, a in history_cp:
+                if isinstance(q, (tuple, list)):
+                    # 判断是URL还是本地路径
+                    img_path = q[0]
+                    if img_path.startswith(('http://', 'https://')):
+                        content.append({'type': 'image', 'image': img_path})
+                    else:
+                        content.append({'type': 'image', 'image': f'{os.path.abspath(img_path)}'})
+                else:
+                    content.append({'type': 'text', 'text': q})
+                    messages.append({'role': 'user', 'content': content})
+                    messages.append({'role': 'assistant', 'content': [{'type': 'text', 'text': a}]})
+                    content = []
+            messages.pop()
+            
+            # 调用模型获取响应
+            response_list = call_local_model(model, processor, messages)
+            response = response_list[0] if response_list else ""
+            
+            _chatbot[-1] = (_parse_text(chat_query), _remove_image_special(_parse_text(response)))
+            full_response = _parse_text(response)
+
+            task_history[-1] = (query, full_response)
+            print('HunyuanOCR: ' + _parse_text(full_response))
+            yield _chatbot
+
+        return predict
+    
+    def create_regenerate_fn():
+
+        def regenerate(_chatbot, task_history):
+            nonlocal model, processor
+            if not task_history:
+                return _chatbot
+            item = task_history[-1]
+            if item[1] is None:
+                return _chatbot
+            task_history[-1] = (item[0], None)
+            chatbot_item = _chatbot.pop(-1)
+            if chatbot_item[0] is None:
+                _chatbot[-1] = (_chatbot[-1][0], None)
+            else:
+                _chatbot.append((chatbot_item[0], None))
+            # 使用外层的predict函数
+            _chatbot_gen = predict(_chatbot, task_history)
+            for _chatbot in _chatbot_gen:
+                yield _chatbot
+
+        return regenerate
+
+    predict = create_predict_fn()
+    regenerate = create_regenerate_fn()
+
+    def add_text(history, task_history, text):
+        task_text = text
+        history = history if history is not None else []
+        task_history = task_history if task_history is not None else []
+        history = history + [(_parse_text(text), None)]
+        task_history = task_history + [(task_text, None)]
+        return history, task_history, ''
+
+    def add_file(history, task_history, file):
+        history = history if history is not None else []
+        task_history = task_history if task_history is not None else []
+        history = history + [((file.name,), None)]
+        task_history = task_history + [((file.name,), None)]
+        return history, task_history
+    
+    def download_url_image(url):
+        """下载 URL 图片到本地临时文件"""
+        try:
+            # 使用 URL 的哈希值作为文件名，避免重复下载
+            url_hash = hashlib.md5(url.encode()).hexdigest()
+            temp_dir = tempfile.gettempdir()
+            temp_path = os.path.join(temp_dir, f"hyocr_demo_{url_hash}.jpg")
+            
+            # 如果文件已存在，直接返回
+            if os.path.exists(temp_path):
+                return temp_path
+            
+            # 下载图片
+            response = requests.get(url, timeout=10)
+            response.raise_for_status()
+            img = Image.open(BytesIO(response.content)).convert('RGB')
+            img.save(temp_path)
+            return temp_path
+        except Exception as e:
+            print(f"下载图片失败: {url}, 错误: {e}")
+            return url  # 失败时返回原 URL
+
+    def reset_user_input():
+        return gr.update(value='')
+
+    def reset_state(_chatbot, task_history):
+        task_history.clear()
+        _chatbot.clear()
+        _gc()
+        return []
+
+    # 示例图片路径配置 - 请替换为实际图片路径
+    EXAMPLE_IMAGES = {
+        "spotting": "https://hunyuan-multimodal-1258344703.cos.ap-guangzhou.myqcloud.com/hunyuan_multimodal/mllm_data/23cc43af9376b948f3febaf4ce854a8a.jpg?q-sign-algorithm=sha1&q-ak=AKIDbLEFMUYZgyERZnygUQLC7xkQ1hTAzulX&q-sign-time=1763452661%3B1794556721&q-key-time=1763452661%3B1794556721&q-header-list=host&q-url-param-list=&q-signature=f39c909f209d2b84e3de648e2842942ad5a47d7a",   # TODO: 替换为场景文字示例图片路径
+        "parsing": "https://hunyuan-multimodal-1258344703.cos.ap-guangzhou.myqcloud.com/hunyuan_multimodal/mllm_data/c4997ebd1be9f7c3e002fabba8b46cb7.jpg?q-sign-algorithm=sha1&q-ak=AKIDbLEFMUYZgyERZnygUQLC7xkQ1hTAzulX&q-sign-time=1763455327%3B1794559387&q-key-time=1763455327%3B1794559387&q-header-list=host&q-url-param-list=&q-signature=6a4c093087ab2c76bca363456b70831d1304bc67",
+        "ie": "https://hunyuan-multimodal-1258344703.cos.ap-guangzhou.myqcloud.com/hunyuan_multimodal/mllm_data/7c67c0f78e4423d51644a325da1f8e85.jpg?q-sign-algorithm=sha1&q-ak=AKIDbLEFMUYZgyERZnygUQLC7xkQ1hTAzulX&q-sign-time=1763455327%3B1794559387&q-key-time=1763455327%3B1794559387&q-header-list=host&q-url-param-list=&q-signature=10aceb21db90dc61e843103f9316f975719ea84d",
+        "vqa": "https://hunyuan-multimodal-1258344703.cos.ap-guangzhou.myqcloud.com/hunyuan_multimodal/mllm_data/fea0865d1c70c53aaa2ab91cd0e787f5.jpg?q-sign-algorithm=sha1&q-ak=AKIDbLEFMUYZgyERZnygUQLC7xkQ1hTAzulX&q-sign-time=1763455328%3B1794559388&q-key-time=1763455328%3B1794559388&q-header-list=host&q-url-param-list=&q-signature=09f62488e9fd33f09795de0faf9b855f95299466",
+        "translation": "https://hunyuan-multimodal-1258344703.cos.ap-guangzhou.myqcloud.com/hunyuan_multimodal/mllm_data/1bdacfd77c09f20ec8bc043933b815d6.jpg?q-sign-algorithm=sha1&q-ak=AKIDbLEFMUYZgyERZnygUQLC7xkQ1hTAzulX&q-sign-time=1763455328%3B1794559388&q-key-time=1763455328%3B1794559388&q-header-list=host&q-url-param-list=&q-signature=c7214858ebd48824565cd21898a32d0464373009",
+        # "spotting": "examples/spotting.jpg",  
+        # "parsing": "examples/parsing.jpg", 
+        # "ie": "examples/ie.jpg",   
+        # "vqa": "examples/vqa.jpg",            
+        # "translation": "examples/translation.jpg"  
+    }
+
+    with gr.Blocks(css="""
+        body {
+            background: #f5f7fa;
+        }
+        .gradio-container {
+            max-width: 100% !important;
+            padding: 0 40px !important;
+        }
+        .header-section {
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            padding: 30px 0;
+            margin: -20px -40px 30px -40px;
+            box-shadow: 0 2px 10px rgba(0,0,0,0.1);
+        }
+        .header-content {
+            max-width: 1600px;
+            margin: 0 auto;
+            padding: 0 40px;
+            display: flex;
+            align-items: center;
+            gap: 20px;
+        }
+        .header-logo {
+            height: 60px;
+        }
+        .header-text h1 {
+            color: white;
+            font-size: 32px;
+            font-weight: bold;
+            margin: 0 0 5px 0;
+        }
+        .header-text p {
+            color: rgba(255,255,255,0.9);
+            margin: 0;
+            font-size: 14px;
+        }
+        .main-container {
+            max-width: 1800px;
+            margin: 0 auto;
+        }
+        .chatbot {
+            box-shadow: 0 2px 8px rgba(0, 0, 0, 0.08) !important;
+            border-radius: 12px !important;
+            border: 1px solid #e5e7eb !important;
+            background: white !important;
+        }
+        .input-panel {
+            background: white;
+            padding: 20px;
+            border-radius: 12px;
+            box-shadow: 0 2px 8px rgba(0, 0, 0, 0.08);
+            border: 1px solid #e5e7eb;
+        }
+        .input-box textarea {
+            border: 2px solid #e5e7eb !important;
+            border-radius: 8px !important;
+            font-size: 14px !important;
+        }
+        .input-box textarea:focus {
+            border-color: #667eea !important;
+        }
+        .btn-primary {
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important;
+            border: none !important;
+            color: white !important;
+            font-weight: 500 !important;
+            padding: 10px 24px !important;
+            font-size: 14px !important;
+        }
+        .btn-primary:hover {
+            transform: translateY(-1px) !important;
+            box-shadow: 0 4px 12px rgba(102, 126, 234, 0.4) !important;
+        }
+        .btn-secondary {
+            background: white !important;
+            border: 2px solid #667eea !important;
+            color: #667eea !important;
+            padding: 8px 20px !important;
+            font-size: 14px !important;
+        }
+        .btn-secondary:hover {
+            background: #f0f4ff !important;
+        }
+        .example-grid {
+            display: grid;
+            grid-template-columns: repeat(4, 1fr);
+            gap: 20px;
+            margin-top: 30px;
+        }
+        .example-card {
+            background: white;
+            border-radius: 12px;
+            overflow: hidden;
+            box-shadow: 0 2px 8px rgba(0, 0, 0, 0.08);
+            border: 1px solid #e5e7eb;
+            transition: all 0.3s ease;
+        }
+        .example-card:hover {
+            transform: translateY(-4px);
+            box-shadow: 0 8px 20px rgba(102, 126, 234, 0.15);
+            border-color: #667eea;
+        }
+        .example-image-wrapper {
+            width: 100%;
+            height: 180px;
+            overflow: hidden;
+            background: #f5f7fa;
+        }
+        .example-image-wrapper img {
+            width: 100%;
+            height: 100%;
+            object-fit: cover;
+        }
+        .example-btn {
+            width: 100% !important;
+            white-space: pre-wrap !important;
+            text-align: left !important;
+            padding: 16px !important;
+            background: white !important;
+            border: none !important;
+            border-top: 1px solid #e5e7eb !important;
+            color: #1f2937 !important;
+            font-size: 14px !important;
+            line-height: 1.6 !important;
+            transition: all 0.3s ease !important;
+            font-weight: 500 !important;
+        }
+        .example-btn:hover {
+            background: #f9fafb !important;
+            color: #667eea !important;
+        }
+        .feature-section {
+            background: white;
+            padding: 24px;
+            border-radius: 12px;
+            margin-top: 30px;
+            box-shadow: 0 2px 8px rgba(0, 0, 0, 0.08);
+            border: 1px solid #e5e7eb;
+        }
+        .section-title {
+            font-size: 18px;
+            font-weight: 600;
+            color: #1f2937;
+            margin-bottom: 20px;
+            padding-bottom: 12px;
+            border-bottom: 2px solid #e5e7eb;
+        }
+    """) as demo:
+        # 顶部导航栏
+        gr.HTML("""
+        <div class="header-section">
+            <div class="header-content">
+                <img src="https://hunyuan-multimodal-1258344703.cos.ap-guangzhou.myqcloud.com/hunyuan_multimodal/mllm_data/6ef6928b21b323b2b00115f86a779d8f.png?q-sign-algorithm=sha1&q-ak=AKIDbLEFMUYZgyERZnygUQLC7xkQ1hTAzulX&q-sign-time=1763450355%3B1794554415&q-key-time=1763450355%3B1794554415&q-header-list=host&q-url-param-list=&q-signature=41328696dc34571324aa18c791c1196192e729c6" class="header-logo"/>
+                <div class="header-text">
+                    <h1>HunyuanOCR</h1>
+                    <p>Powered by Tencent Hunyuan Team</p>
+                </div>
+            </div>
+        </div>
+        """)
+        
+        with gr.Column(elem_classes=["main-container"]):
+            # 对话区域 - 全宽
+            chatbot = gr.Chatbot(
+                label='💬 对话窗口', 
+                height=600,
+                bubble_full_width=False,
+                layout="bubble",
+                show_copy_button=True,
+                avatar_images=(None, "https://hunyuan-multimodal-1258344703.cos.ap-guangzhou.myqcloud.com/hunyuan_multimodal/mllm_data/6ef6928b21b323b2b00115f86a779d8f.png?q-sign-algorithm=sha1&q-ak=AKIDbLEFMUYZgyERZnygUQLC7xkQ1hTAzulX&q-sign-time=1763450355%3B1794554415&q-key-time=1763450355%3B1794554415&q-header-list=host&q-url-param-list=&q-signature=41328696dc34571324aa18c791c1196192e729c6"),
+                elem_classes=["chatbot"]
+            )
+            
+            # 输入控制面板 - 全宽
+            with gr.Group(elem_classes=["input-panel"]):
+                query = gr.Textbox(
+                    lines=2, 
+                    label='💭 输入您的问题', 
+                    placeholder='请先上传图片，然后输入问题。例如：检测并识别图片中的文字，将文本坐标格式化输出。',
+                    elem_classes=["input-box"],
+                    show_label=False
+                )
+                
+                with gr.Row():
+                    addfile_btn = gr.UploadButton('📁 上传图片', file_types=['image'], elem_classes=["btn-secondary"])
+                    submit_btn = gr.Button('🚀 发送消息', variant="primary", elem_classes=["btn-primary"], scale=3)
+                    regen_btn = gr.Button('🔄 重新生成', elem_classes=["btn-secondary"])
+                    empty_bin = gr.Button('🗑️ 清空对话', elem_classes=["btn-secondary"])
+            
+            # 示例区域 - 5列网格布局
+            gr.HTML('<div class="section-title">📚 快速体验示例 - 点击下方卡片快速加载</div>')
+            
+            with gr.Row():
+                # 示例1：spotting
+                with gr.Column(scale=1):
+                    with gr.Group(elem_classes=["example-card"]):
+                        gr.HTML("""
+                        <div class="example-image-wrapper">
+                            <img src="https://hunyuan-multimodal-1258344703.cos.ap-guangzhou.myqcloud.com/hunyuan_multimodal/mllm_data/23cc43af9376b948f3febaf4ce854a8a.jpg?q-sign-algorithm=sha1&q-ak=AKIDbLEFMUYZgyERZnygUQLC7xkQ1hTAzulX&q-sign-time=1763452661%3B1794556721&q-key-time=1763452661%3B1794556721&q-header-list=host&q-url-param-list=&q-signature=f39c909f209d2b84e3de648e2842942ad5a47d7a" alt="文字检测识别"/>
+                        </div>
+                        """)
+                        example_1_btn = gr.Button("🔍 文字检测和识别", elem_classes=["example-btn"])
+                
+                # 示例2：parsing
+                with gr.Column(scale=1):
+                    with gr.Group(elem_classes=["example-card"]):
+                        gr.HTML("""
+                        <div class="example-image-wrapper">
+                            <img src="https://hunyuan-multimodal-1258344703.cos.ap-guangzhou.myqcloud.com/hunyuan_multimodal/mllm_data/c4997ebd1be9f7c3e002fabba8b46cb7.jpg?q-sign-algorithm=sha1&q-ak=AKIDbLEFMUYZgyERZnygUQLC7xkQ1hTAzulX&q-sign-time=1763455327%3B1794559387&q-key-time=1763455327%3B1794559387&q-header-list=host&q-url-param-list=&q-signature=6a4c093087ab2c76bca363456b70831d1304bc67" alt="文档解析"/>
+                        </div>
+                        """)
+                        example_2_btn = gr.Button("📋 文档解析", elem_classes=["example-btn"])
+                
+                # 示例3：ie
+                with gr.Column(scale=1):
+                    with gr.Group(elem_classes=["example-card"]):
+                        gr.HTML("""
+                        <div class="example-image-wrapper">
+                            <img src="https://hunyuan-multimodal-1258344703.cos.ap-guangzhou.myqcloud.com/hunyuan_multimodal/mllm_data/7c67c0f78e4423d51644a325da1f8e85.jpg?q-sign-algorithm=sha1&q-ak=AKIDbLEFMUYZgyERZnygUQLC7xkQ1hTAzulX&q-sign-time=1763455327%3B1794559387&q-key-time=1763455327%3B1794559387&q-header-list=host&q-url-param-list=&q-signature=10aceb21db90dc61e843103f9316f975719ea84d" alt="信息抽取"/>
+                        </div>
+                        """)
+                        example_3_btn = gr.Button("🎯 信息抽取", elem_classes=["example-btn"])
+                
+                # 示例4：VQA
+                with gr.Column(scale=1):
+                    with gr.Group(elem_classes=["example-card"]):
+                        gr.HTML("""
+                        <div class="example-image-wrapper">
+                            <img src="https://hunyuan-multimodal-1258344703.cos.ap-guangzhou.myqcloud.com/hunyuan_multimodal/mllm_data/fea0865d1c70c53aaa2ab91cd0e787f5.jpg?q-sign-algorithm=sha1&q-ak=AKIDbLEFMUYZgyERZnygUQLC7xkQ1hTAzulX&q-sign-time=1763455328%3B1794559388&q-key-time=1763455328%3B1794559388&q-header-list=host&q-url-param-list=&q-signature=09f62488e9fd33f09795de0faf9b855f95299466" alt="视觉问答"/>
+                        </div>
+                        """)
+                        example_4_btn = gr.Button("💬 视觉问答", elem_classes=["example-btn"])
+                
+                # 示例5：translation
+                with gr.Column(scale=1):
+                    with gr.Group(elem_classes=["example-card"]):
+                        gr.HTML("""
+                        <div class="example-image-wrapper">
+                            <img src="https://hunyuan-multimodal-1258344703.cos.ap-guangzhou.myqcloud.com/hunyuan_multimodal/mllm_data/1bdacfd77c09f20ec8bc043933b815d6.jpg?q-sign-algorithm=sha1&q-ak=AKIDbLEFMUYZgyERZnygUQLC7xkQ1hTAzulX&q-sign-time=1763455328%3B1794559388&q-key-time=1763455328%3B1794559388&q-header-list=host&q-url-param-list=&q-signature=c7214858ebd48824565cd21898a32d0464373009" alt="图片翻译"/>
+                        </div>
+                        """)
+                        example_5_btn = gr.Button("🌐 图片翻译", elem_classes=["example-btn"])
+        
+        task_history = gr.State([])
+        
+        
+        # 示例1：文档识别
+        def load_example_1(history, task_hist):
+            prompt = "检测并识别图片中的文字，将文本坐标格式化输出。"
+            image_url = EXAMPLE_IMAGES["spotting"]
+            # 下载 URL 图片到本地
+            image_path = download_url_image(image_url)
+            # 清空对话历史
+            history = []
+            task_hist = []
+            history = history + [((image_path,), None)]
+            task_hist = task_hist + [((image_path,), None)]
+            return history, task_hist, prompt
+        
+        
+        
+        # 示例2：场景文字
+        def load_example_2(history, task_hist):
+            prompt = "提取文档图片中正文的所有信息用markdown 格式表示，其中页眉、页脚部分忽略，表格用html 格式表达，文档中公式用latex 格式表示，按照阅读顺序组织进行解析。"
+            image_url = EXAMPLE_IMAGES["parsing"]
+            # 下载 URL 图片到本地
+            image_path = download_url_image(image_url)
+            # 清空对话历史
+            history = []
+            task_hist = []
+            history = history + [((image_path,), None)]
+            task_hist = task_hist + [((image_path,), None)]
+            return history, task_hist, prompt
+        
+        
+        
+        # 示例3：表格提取
+        def load_example_3(history, task_hist):
+            prompt = "提取图片中的：['单价', '上车时间','发票号码', '省前缀', '总金额', '发票代码', '下车时间', '里程数'] 的字段内容，并且按照JSON格式返回。"
+            image_url = EXAMPLE_IMAGES["ie"]
+            # 下载 URL 图片到本地
+            image_path = download_url_image(image_url)
+            # 清空对话历史
+            history = []
+            task_hist = []
+            history = history + [((image_path,), None)]
+            task_hist = task_hist + [((image_path,), None)]
+            return history, task_hist, prompt
+        
+        # 示例4：手写体
+        def load_example_4(history, task_hist):
+            prompt = "What is the highest life expectancy at birth of male?"
+            image_url = EXAMPLE_IMAGES["vqa"]
+            # 下载 URL 图��到本地
+            image_path = download_url_image(image_url)
+            # 清空对话历史
+            history = []
+            task_hist = []
+            history = history + [((image_path,), None)]
+            task_hist = task_hist + [((image_path,), None)]
+            return history, task_hist, prompt
+        
+        # 示例5：翻译
+        def load_example_5(history, task_hist):
+            prompt = "提取图中文字，并将其翻译成英文。"
+            image_url = EXAMPLE_IMAGES["translation"]
+            # 下载 URL 图片到本地
+            image_path = download_url_image(image_url)
+            # 清空对话历史
+            history = []
+            task_hist = []
+            history = history + [((image_path,), None)]
+            task_hist = task_hist + [((image_path,), None)]
+            return history, task_hist, prompt
+        
+        # 绑定事件
+        example_1_btn.click(load_example_1, [chatbot, task_history], [chatbot, task_history, query])
+        example_2_btn.click(load_example_2, [chatbot, task_history], [chatbot, task_history, query])
+        example_3_btn.click(load_example_3, [chatbot, task_history], [chatbot, task_history, query])
+        example_4_btn.click(load_example_4, [chatbot, task_history], [chatbot, task_history, query])
+        example_5_btn.click(load_example_5, [chatbot, task_history], [chatbot, task_history, query])
+        
+        submit_btn.click(add_text, [chatbot, task_history, query],
+                         [chatbot, task_history]).then(predict, [chatbot, task_history], [chatbot], show_progress=True)
+        submit_btn.click(reset_user_input, [], [query])
+        empty_bin.click(reset_state, [chatbot, task_history], [chatbot], show_progress=True)
+        regen_btn.click(regenerate, [chatbot, task_history], [chatbot], show_progress=True)
+        addfile_btn.upload(add_file, [chatbot, task_history, addfile_btn], [chatbot, task_history], show_progress=True)
+
+        # 功能说明区域
+        with gr.Row():
+            with gr.Column(scale=1):
+                gr.HTML("""
+                <div class="feature-section">
+                    <div class="section-title">✨ 核心功能</div>
+                    <ul style="line-height: 2; color: #4b5563; font-size: 14px; margin: 0; padding-left: 20px;">
+                        <li><strong>🎯 高精度文字检测识别</strong> - 支持多场景文字检测与识别</li>
+                        <li><strong>📐 智能文档解析</strong> - 自动识别文档结构，支持多粒度文档解析</li>
+                        <li><strong>📋 信息提取</strong> - 支持30+高频卡证票据识别和结构化输出</li>
+                        <li><strong>✏️ 视觉问答</strong> - 支持以文本为中心的开放式问答</li>
+                        <li><strong>🌍 跨语言翻译</strong> - 支持中英互译及14+语种译为中英文</li>
+                    </ul>
+                </div>
+                """)
+            
+            with gr.Column(scale=1):
+                gr.HTML("""
+                <div class="feature-section">
+                    <div class="section-title">💡 使用建议</div>
+                    <ul style="line-height: 2; color: #4b5563; font-size: 14px; margin: 0; padding-left: 20px;">
+                        <li><strong>图片质量</strong> - 确保图片清晰，光线充足，分辨率适中</li>
+                        <li><strong>拍摄角度</strong> - 避免严重倾斜、遮挡或反光，正面拍摄效果最佳</li>
+                        <li><strong>文件大小</strong> - 建议单张图片不超过 10MB，支持 JPG/PNG 格式</li>
+                        <li><strong>使用场景</strong> - 适用于文字检测识别、文档数字化、票据识别、信息提取、文字图片翻译等</li>
+                        <li><strong>合规使用</strong> - 仅供学习研究，请遵守法律法规，尊重隐私权</li>
+                    </ul>
+                </div>
+                """)
+        
+        # 底部版权信息
+        gr.HTML("""
+        <div style="text-align: center; color: #9ca3af; font-size: 13px; margin-top: 40px; padding: 20px; border-top: 1px solid #e5e7eb;">
+            <p style="margin: 0;">© 2025 Tencent Hunyuan Team. All rights reserved.</p>
+            <p style="margin: 5px 0 0 0;">本系统基于 HunyuanOCR 构建 | 仅供学习研究使用</p>
+        </div>
+        """)
+
+    demo.queue().launch(
+        share=args.share,
+        inbrowser=args.inbrowser,
+        # server_port=args.server_port,
+        # server_name=args.server_name,
+    )
+
+
+def main():
+    args = _get_args()
+    model, processor = _load_model_processor(args)
+    _launch_demo(args, model, processor)
+
+
+if __name__ == '__main__':
+    main()

requirements.txt

@@ -0,0 +1,12 @@
+torch==2.6.0
+git+https://github.com/ManaEstras/transformers.git@v4.57.1.hyvl
+tokenizers
+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
+qwen_vl_utils