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| import os | |
| import torch | |
| import numpy as np | |
| from PIL import Image | |
| import spaces | |
| 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.') | |
| args = parser.parse_args() | |
| return args | |
| def _load_model_processor(args): | |
| # ZeroGPU 环境：模型在 CPU 上加载，使用 eager 模式 | |
| # 在 @spaces.GPU 装饰器内会自动移到 GPU | |
| print(f"[INFO] 加载模型（ZeroGPU 环境使用 eager 模式）") | |
| model = HunYuanVLForConditionalGeneration.from_pretrained( | |
| args.checkpoint_path, | |
| attn_implementation="eager", # ZeroGPU 必须用 eager，因为初始在 CPU | |
| torch_dtype=torch.bfloat16, | |
| device_map="auto", # 改回 auto，让 ZeroGPU 自动管理 | |
| token=os.environ.get('HF_TOKEN') | |
| ) | |
| processor = AutoProcessor.from_pretrained(args.checkpoint_path, use_fast=False, trust_remote_code=True) | |
| print(f"[INFO] 模型加载完成") | |
| 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): | |
| # 关键修复：移除 model 和 processor 参数，使用闭包访问 | |
| def call_local_model(messages): | |
| import time | |
| start_time = time.time() | |
| print(f"[DEBUG] ========== 开始推理 ==========") | |
| # 关键：检查并确保模型在 GPU 上 | |
| model_device = next(model.parameters()).device | |
| print(f"[DEBUG] Model device: {model_device}") | |
| if str(model_device) == 'cpu': | |
| print(f"[ERROR] 模型在 CPU 上！尝试移动到 GPU...") | |
| model.cuda() | |
| print(f"[DEBUG] Model device after cuda(): {next(model.parameters()).device}") | |
| 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 | |
| ] | |
| print(f"[DEBUG] 模板构建完成，耗时: {time.time() - start_time:.2f}s") | |
| image_inputs, video_inputs = process_vision_info(messages) | |
| print(f"[DEBUG] 图像处理完成，耗时: {time.time() - start_time:.2f}s") | |
| inputs = processor( | |
| text=texts, | |
| images=image_inputs, | |
| videos=video_inputs, | |
| padding=True, | |
| return_tensors="pt", | |
| ) | |
| # 确保输入在 GPU 上 | |
| inputs = inputs.to('cuda' if torch.cuda.is_available() else 'cpu') | |
| print(f"[DEBUG] 输入准备完成，耗时: {time.time() - start_time:.2f}s") | |
| print(f"[DEBUG] Input IDs shape: {inputs.input_ids.shape}") | |
| print(f"[DEBUG] Input device: {inputs.input_ids.device}") | |
| # 生成 | |
| gen_start = time.time() | |
| with torch.no_grad(): | |
| generated_ids = model.generate( | |
| **inputs, | |
| max_new_tokens=256, | |
| repetition_penalty=1.03, | |
| do_sample=False, | |
| eos_token_id=processor.tokenizer.eos_token_id, | |
| pad_token_id=processor.tokenizer.pad_token_id, | |
| ) | |
| gen_time = time.time() - gen_start | |
| print(f"[DEBUG] ========== 生成完成 ==========") | |
| print(f"[DEBUG] 生成耗时: {gen_time:.2f}s") | |
| print(f"[DEBUG] Output shape: {generated_ids.shape}") | |
| # 解码输出 | |
| if "input_ids" in inputs: | |
| input_ids = inputs.input_ids | |
| else: | |
| input_ids = inputs.inputs | |
| generated_ids_trimmed = [ | |
| out_ids[len(in_ids):] for in_ids, out_ids in zip(input_ids, generated_ids) | |
| ] | |
| actual_tokens = len(generated_ids_trimmed[0]) | |
| print(f"[DEBUG] 实际生成 token 数: {actual_tokens}") | |
| print(f"[DEBUG] 每 token 耗时: {gen_time/actual_tokens if actual_tokens > 0 else 0:.3f}s") | |
| output_texts = processor.batch_decode( | |
| generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False | |
| ) | |
| total_time = time.time() - start_time | |
| print(f"[DEBUG] ========== 全部完成 ==========") | |
| print(f"[DEBUG] 总耗时: {total_time:.2f}s") | |
| print(f"[DEBUG] 输出长度: {len(output_texts[0])} 字符") | |
| print(f"[DEBUG] 输出预览: {output_texts[0][:100]}...") | |
| 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（已修改：不再传递 model 和 processor） | |
| response_list = call_local_model(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}.png") | |
| # 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() | |
| with open(temp_path, 'wb') as f: | |
| f.write(response.content) | |
| 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=1763523817%3B1794627877&q-key-time=1763523817%3B1794627877&q-header-list=host&q-url-param-list=&q-signature=8ebd6a9d3ed7eba73bb783c337349db9c29972e2", # 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=1763523818%3B1794627878&q-key-time=1763523818%3B1794627878&q-header-list=host&q-url-param-list=&q-signature=d2cd12be4c7902821c8c82203e4642624046911a", | |
| "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=1763523818%3B1794627878&q-key-time=1763523818%3B1794627878&q-header-list=host&q-url-param-list=&q-signature=803648f3253706f654faf1423869fd9e00e7056e", | |
| "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=1763523818%3B1794627878&q-key-time=1763523818%3B1794627878&q-header-list=host&q-url-param-list=&q-signature=a92b94e298a11aea130d730d3b16ee761acc3f4c", | |
| "translation": "https://hunyuan-multimodal-1258344703.cos.ap-guangzhou.myqcloud.com/hunyuan_multimodal/mllm_data/d1af99d35e9db9e820ebebb5bc68993a.jpg?q-sign-algorithm=sha1&q-ak=AKIDbLEFMUYZgyERZnygUQLC7xkQ1hTAzulX&q-sign-time=1763967603%3B1795071663&q-key-time=1763967603%3B1795071663&q-header-list=host&q-url-param-list=&q-signature=a57080c0b3d4c76ea74b88c6291f9004241c9d49", | |
| # "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=1763523817%3B1794627877&q-key-time=1763523817%3B1794627877&q-header-list=host&q-url-param-list=&q-signature=8ebd6a9d3ed7eba73bb783c337349db9c29972e2" 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=1763523818%3B1794627878&q-key-time=1763523818%3B1794627878&q-header-list=host&q-url-param-list=&q-signature=d2cd12be4c7902821c8c82203e4642624046911a" 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=1763523818%3B1794627878&q-key-time=1763523818%3B1794627878&q-header-list=host&q-url-param-list=&q-signature=803648f3253706f654faf1423869fd9e00e7056e" 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=1763523818%3B1794627878&q-key-time=1763523818%3B1794627878&q-header-list=host&q-url-param-list=&q-signature=a92b94e298a11aea130d730d3b16ee761acc3f4c" 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/d1af99d35e9db9e820ebebb5bc68993a.jpg?q-sign-algorithm=sha1&q-ak=AKIDbLEFMUYZgyERZnygUQLC7xkQ1hTAzulX&q-sign-time=1763967603%3B1795071663&q-key-time=1763967603%3B1795071663&q-header-list=host&q-url-param-list=&q-signature=a57080c0b3d4c76ea74b88c6291f9004241c9d49" 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5：翻译 | |
| def load_example_5(history, task_hist): | |
| prompt = "将图中文字翻译为中文。" | |
| 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() |