Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import Qwen3VLForConditionalGeneration, AutoProcessor
+from qwen_vl_utils import process_vision_info
+from PIL import Image
+import random
+import os
+import warnings
+from typing import List, Tuple, Optional
+
+# Suppress specific warnings
+warnings.filterwarnings('ignore', message='.*meta device.*')
+
+# Multi-language support
+TRANSLATIONS = {
+    "en": {
+        "title": "Qwen VL Image Description Generator",
+        "header": "🖼️ Image Description Generator based on Qwen Vision Language Models",
+        "subtitle": "Upload an image and enter a prompt to generate a description using Qwen VL models.",
+        "language": "Language",
+        "language_info": "Select language",
+        "model_selection": "Model Selection",
+        "model_info": "Select a model for generating descriptions",
+        "advanced_params": "⚙️ Advanced Parameters",
+        "max_tokens": "Max New Tokens",
+        "max_tokens_info": "Maximum number of tokens to generate",
+        "temperature": "Temperature",
+        "temperature_info": "Controls randomness of generation",
+        "top_p": "Top-p (nucleus sampling)",
+        "top_p_info": "Probability threshold for token sampling",
+        "top_k": "Top-k",
+        "top_k_info": "Number of most probable tokens to consider",
+        "seed": "Seed",
+        "seed_info": "Seed for reproducibility (-1 for random)",
+        "random_seed_btn": "🎲 Random Seed",
+        "single_processing": "📄 Single Processing",
+        "batch_processing": "📚 Batch Processing",
+        "upload_image": "Upload Image",
+        "prompt": "Prompt",
+        "prompt_placeholder": "For example: Create a product description for online store",
+        "generate_btn": "🚀 Generate Description",
+        "result": "Result",
+        "upload_images": "Upload Images",
+        "prompts_multiline": "Prompts (one per line)",
+        "prompts_placeholder": "Create a product description for online store\nCreate SEO Description for product\n...",
+        "prompts_info": "Specify one prompt for all images or one prompt per image",
+        "process_batch_btn": "🚀 Process Batch",
+        "results": "Results",
+        "examples_title": "💡 Example Prompts:",
+        "example_1": "Create a product description for online store",
+        "example_2": "Create an SEO description for a product with a maximum of 160 characters.",
+        "example_3": "Create an attractive product description for marketplace",
+        "example_4": "Describe image in detail for product catalog",
+        "error_no_image": "Please upload an image",
+        "error_no_prompt": "Please enter a prompt",
+        "error_no_images": "Please upload images",
+        "error_no_prompts": "Please enter prompts (one per line)",
+        "error_prompt_mismatch": "Number of prompts ({}) does not match number of images ({}). Specify either one prompt for all images or one prompt per image.",
+        "error_generation": "Error generating description: {}",
+        "loading_model": "Loading model: {}",
+        "model_loaded": "Model {} successfully loaded on {}",
+        "image_label": "=== Image {}: {} ===",
+        "prompt_label": "Prompt: {}",
+        "result_label": "Result: {}",
+        "model_size_warning": "⚠️ Note: Large models (8B+) may use CPU offloading if GPU memory is insufficient, which can slow down generation."
+    },
+    "ru": {
+        "title": "Генератор описаний изображений Qwen VL",
+        "header": "🖼️ Генератор описаний изображений на основе Qwen Vision Language Models",
+        "subtitle": "Загрузите изображение и введите промт для генерации описания с помощью моделей Qwen VL.",
+        "language": "Язык",
+        "language_info": "Выберите язык",
+        "model_selection": "Выбор модели",
+        "model_info": "Выберите модель для генерации описаний",
+        "advanced_params": "⚙️ Расширенные параметры",
+        "max_tokens": "Макс. количество новых токенов",
+        "max_tokens_info": "Максимальное количество токенов для генерации",
+        "temperature": "Температура",
+        "temperature_info": "Контролирует случайность генерации",
+        "top_p": "Top-p (nucleus sampling)",
+        "top_p_info": "Вероятностный порог для выборки токенов",
+        "top_k": "Top-k",
+        "top_k_info": "Количество наиболее вероятных токенов для рассмотрения",
+        "seed": "Seed",
+        "seed_info": "Seed для воспроизводимости (-1 для случайного)",
+        "random_seed_btn": "🎲 Случайный seed",
+        "single_processing": "📄 Одиночная обработка",
+        "batch_processing": "📚 Пакетная обработка",
+        "upload_image": "Загрузите изображение",
+        "prompt": "Промт",
+        "prompt_placeholder": "Например: Создать описание товара для онлайн магазина",
+        "generate_btn": "🚀 Генерировать описание",
+        "result": "Результат",
+        "upload_images": "Загрузите изображения",
+        "prompts_multiline": "Промты (по одному на строку)",
+        "prompts_placeholder": "Создать описание товара для онлайн магазина\nСоздать SEO Description для товара\n...",
+        "prompts_info": "Укажите один промт для всех изображений или по одному промту на каждое изображение",
+        "process_batch_btn": "🚀 Обработать пакет",
+        "results": "Результаты",
+        "examples_title": "💡 Примеры промтов:",
+        "example_1": "Создать описание товара ''  на русском языке",
+        "example_2": "Создать SEO Description для товара максимум 160 символов на русском языке",
+        "example_3": "Создать привлекательное описание продукта для маркетплейса на русском языке",
+        "example_4": "Детально описать изображение для каталога товаров на русском языке",
+        "error_no_image": "Пожалуйста, загрузите изображение",
+        "error_no_prompt": "Пожалуйста, введите промт",
+        "error_no_images": "Пожалуйста, загрузите изображения",
+        "error_no_prompts": "Пожалуйста, введите промты (по одному на строку)",
+        "error_prompt_mismatch": "Количество промтов ({}) не совпадает с количеством изображений ({}). Укажите либо один промт для всех изображений, либо по одному промту на каждое изображение.",
+        "error_generation": "Ошибка при генерации описания: {}",
+        "loading_model": "Загрузка модели: {}",
+        "model_loaded": "Модель {} успешно загружена на {}",
+        "image_label": "=== Изображение {}: {} ===",
+        "prompt_label": "Промт: {}",
+        "result_label": "Результат: {}",
+        "model_size_warning": "⚠️ Примечание: Большие модели (8B+) могут использовать выгрузку на CPU при недостатке памяти GPU, что может замедлить генерацию."
+    },
+    "zh": {
+        "title": "Qwen VL 图像描述生成器",
+        "header": "🖼️ 基于 Qwen Vision Language Models 的图像描述生成器",
+        "subtitle": "上传图像并输入提示词，使用 Qwen VL 模型生成描述。",
+        "language": "语言",
+        "language_info": "选择语言",
+        "model_selection": "模型选择",
+        "model_info": "选择用于生成描述的模型",
+        "advanced_params": "⚙️ 高级参数",
+        "max_tokens": "最大新令牌数",
+        "max_tokens_info": "生成的最大令牌数",
+        "temperature": "温度",
+        "temperature_info": "控制生成的随机性",
+        "top_p": "Top-p（核采样）",
+        "top_p_info": "令牌采样的概率阈值",
+        "top_k": "Top-k",
+        "top_k_info": "考虑的最可能令牌数",
+        "seed": "随机种子",
+        "seed_info": "用于可重现性的种子（-1 表示随机）",
+        "random_seed_btn": "🎲 随机种子",
+        "single_processing": "📄 单张处理",
+        "batch_processing": "📚 批量处理",
+        "upload_image": "上传图像",
+        "prompt": "提示词",
+        "prompt_placeholder": "例如：为在线商店创建产品描述",
+        "generate_btn": "🚀 生成描述",
+        "result": "结果",
+        "upload_images": "上传图像",
+        "prompts_multiline": "提示词（每行一个）",
+        "prompts_placeholder": "为在线商店创建产品描述\n为产品创建SEO描述\n...",
+        "prompts_info": "为所有图像指定一个提示词，或为每个图像指定一个提示词",
+        "process_batch_btn": "🚀 处理批次",
+        "results": "结果",
+        "examples_title": "💡 示例提示词：",
+        "example_1": "为在线商店创建产品描述",
+        "example_2": "为产品创建SEO描述最多 160 个字符",
+        "example_3": "为市场创建有吸引力的产品描述",
+        "example_4": "详细描述产品目录的图像",
+        "error_no_image": "请上传图像",
+        "error_no_prompt": "请输入提示词",
+        "error_no_images": "请上传图像",
+        "error_no_prompts": "请输入提示词（每行一个）",
+        "error_prompt_mismatch": "提示词数量（{}）与图像数量（{}）不匹配。请为所有图像指定一个提示词，或为每个图像指定一个提示词。",
+        "error_generation": "生成描述时出错：{}",
+        "loading_model": "正在加载模型：{}",
+        "model_loaded": "模型 {} 已成功加载到 {}",
+        "image_label": "=== 图像 {}: {} ===",
+        "prompt_label": "提示词：{}",
+        "result_label": "结果：{}",
+        "model_size_warning": "⚠️ 注意：如果 GPU 内存不足，大型模型（8B+）可能会使用 CPU 卸载，这可能会减慢生成速度。"
+    }
+}
+
+# Default language
+current_language = "en"
+
+def get_text(key: str) -> str:
+    """Get translated text for the current language"""
+    return TRANSLATIONS[current_language].get(key, key)
+
+class ImageDescriptionGenerator:
+    def __init__(self):
+        self.model = None
+        self.processor = None
+        self.current_model_name = None
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        
+    def load_model(self, model_name: str):
+        """Загрузка модели только если она еще не загружена или изменилась"""
+        if self.current_model_name == model_name and self.model is not None:
+            return
+        
+        print(get_text("loading_model").format(model_name))
+        
+        # Предупреждение о больших моделях
+        if "8B" in model_name or "4B" in model_name:
+            print(get_text("model_size_warning"))
+        
+        # Освобождаем память от предыдущей модели
+        if self.model is not None:
+            del self.model
+            del self.processor
+            torch.cuda.empty_cache() if torch.cuda.is_available() else None
+        
+        # Загружаем новую модель с подавлением предупреждений
+        with warnings.catch_warnings():
+            warnings.filterwarnings('ignore')
+            self.model = Qwen3VLForConditionalGeneration.from_pretrained(
+                model_name,
+                dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
+                device_map="auto"
+            )
+            self.processor = AutoProcessor.from_pretrained(model_name)
+        
+        self.current_model_name = model_name
+        print(get_text("model_loaded").format(model_name, self.device))
+    
+    def generate_description(
+        self,
+        image_path: str,
+        prompt: str,
+        model_name: str,
+        max_new_tokens: int = 1024,
+        temperature: float = 0.6,
+        top_p: float = 0.9,
+        top_k: int = 50,
+        seed: int = -1
+    ) -> str:
+        """Генерация описания для одного изображения"""
+        try:
+            # Загружаем модель если необходимо
+            self.load_model(model_name)
+            
+            # Устанавливаем seed если указан
+            if seed != -1:
+                torch.manual_seed(seed)
+                if torch.cuda.is_available():
+                    torch.cuda.manual_seed(seed)
+            
+            # Подготавливаем сообщения для модели
+            messages = [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "image",
+                            "image": image_path,
+                        },
+                        {"type": "text", "text": prompt},
+                    ],
+                }
+            ]
+            
+            # Подготавливаем текст для модели
+            text = self.processor.apply_chat_template(
+                messages, tokenize=False, add_generation_prompt=True
+            )
+            
+            # Обрабатываем изображение и текст
+            image_inputs, video_inputs = process_vision_info(messages)
+            inputs = self.processor(
+                text=[text],
+                images=image_inputs,
+                videos=video_inputs,
+                padding=True,
+                return_tensors="pt",
+            )
+            inputs = inputs.to(self.device)
+            
+            # Генерируем ответ
+            with torch.no_grad():
+                generated_ids = self.model.generate(
+                    **inputs,
+                    max_new_tokens=max_new_tokens,
+                    temperature=temperature,
+                    top_p=top_p,
+                    top_k=top_k,
+                    do_sample=True if temperature > 0 else False
+                )
+            
+            # Декодируем результат
+            generated_ids_trimmed = [
+                out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+            ]
+            output_text = self.processor.batch_decode(
+                generated_ids_trimmed,
+                skip_special_tokens=True,
+                clean_up_tokenization_spaces=False
+            )
+            
+            return output_text[0]
+            
+        except Exception as e:
+            return get_text("error_generation").format(str(e))
+
+# Создаем глобальный экземпляр генератора
+generator = ImageDescriptionGenerator()
+
+def process_single_image(
+    image,
+    prompt: str,
+    model_name: str,
+    max_new_tokens: int,
+    temperature: float,
+    top_p: float,
+    top_k: int,
+    seed: int
+) -> str:
+    """Обработка одного изображения"""
+    if image is None:
+        return get_text("error_no_image")
+    
+    if not prompt.strip():
+        return get_text("error_no_prompt")
+    
+    # Сохраняем временное изображение если это numpy array
+    if hasattr(image, 'shape'):
+        temp_path = "temp_image.jpg"
+        Image.fromarray(image).save(temp_path)
+        image_path = temp_path
+    else:
+        image_path = image
+    
+    result = generator.generate_description(
+        image_path=image_path,
+        prompt=prompt,
+        model_name=model_name,
+        max_new_tokens=max_new_tokens,
+        temperature=temperature,
+        top_p=top_p,
+        top_k=top_k,
+        seed=seed
+    )
+    
+    # Удаляем временный файл
+    if hasattr(image, 'shape') and os.path.exists(temp_path):
+        os.remove(temp_path)
+    
+    return result
+
+def process_batch_images(
+    files: List,
+    prompts_text: str,
+    model_name: str,
+    max_new_tokens: int,
+    temperature: float,
+    top_p: float,
+    top_k: int,
+    seed: int
+) -> str:
+    """Обработка пакета изображений"""
+    if not files:
+        return get_text("error_no_images")
+    
+    if not prompts_text.strip():
+        return get_text("error_no_prompts")
+    
+    # Разбиваем промты по строкам
+    prompts = [p.strip() for p in prompts_text.split('\n') if p.strip()]
+    
+    if len(prompts) == 1:
+        # Если один промт, используем его для всех изображений
+        prompts = prompts * len(files)
+    elif len(prompts) != len(files):
+        return get_text("error_prompt_mismatch").format(len(prompts), len(files))
+    
+    results = []
+    for idx, (file, prompt) in enumerate(zip(files, prompts), 1):
+        image_path = file.name if hasattr(file, 'name') else file
+        result = generator.generate_description(
+            image_path=image_path,
+            prompt=prompt,
+            model_name=model_name,
+            max_new_tokens=max_new_tokens,
+            temperature=temperature,
+            top_p=top_p,
+            top_k=top_k,
+            seed=seed if seed == -1 else seed + idx - 1  # Разный seed для каждого изображения
+        )
+        results.append(get_text("image_label").format(idx, os.path.basename(image_path)) + "\n")
+        results.append(get_text("prompt_label").format(prompt) + "\n")
+        results.append(get_text("result_label").format(result) + "\n\n")
+    
+    return "".join(results)
+
+def random_seed() -> int:
+    """Генерация случайного seed"""
+    return random.randint(0, 2**32 - 1)
+
+def create_interface():
+    """Create Gradio interface with current language"""
+    with gr.Blocks(title=get_text("title"), theme=gr.themes.Soft()) as demo:
+        # Header that will be updated
+        header_md = gr.Markdown(f"""
+        # {get_text("header")}
+        
+        {get_text("subtitle")}
+        """)
+        
+        # Общие настройки - модель и язык в одном ряду
+        with gr.Row():
+            model_dropdown = gr.Dropdown(
+                choices=[
+                    "Qwen/Qwen3-VL-2B-Instruct",
+                    "Qwen/Qwen3-VL-4B-Instruct",
+                    "Qwen/Qwen3-VL-8B-Instruct",
+                    "Qwen/Qwen3-VL-32B-Instruct",
+                    "Qwen/Qwen3-VL-2B-Thinking",
+                    "Qwen/Qwen3-VL-4B-Thinking",
+                    "Qwen/Qwen3-VL-8B-Thinking",
+                    "Qwen/Qwen3-VL-32B-Thinking",
+                ],
+                value="Qwen/Qwen3-VL-2B-Instruct",
+                label=get_text("model_selection"),
+                info=get_text("model_info"),
+                scale=3
+            )
+            language_dropdown = gr.Dropdown(
+                choices=[("English", "en"), ("Русский", "ru"), ("中文", "zh")],
+                value=current_language,
+                label=get_text("language"),
+                info=get_text("language_info"),
+                scale=1
+            )
+        
+        # Расширенные параметры
+        advanced_accordion = gr.Accordion(get_text("advanced_params"), open=False)
+        with advanced_accordion:
+            with gr.Row():
+                max_tokens_slider = gr.Slider(
+                    minimum=1,
+                    maximum=4096,
+                    value=1024,
+                    step=1,
+                    label=get_text("max_tokens"),
+                    info=get_text("max_tokens_info")
+                )
+                temperature_slider = gr.Slider(
+                    minimum=0.1,
+                    maximum=2.0,
+                    value=0.6,
+                    step=0.1,
+                    label=get_text("temperature"),
+                    info=get_text("temperature_info")
+                )
+            
+            with gr.Row():
+                top_p_slider = gr.Slider(
+                    minimum=0.05,
+                    maximum=1.0,
+                    value=0.9,
+                    step=0.05,
+                    label=get_text("top_p"),
+                    info=get_text("top_p_info")
+                )
+                top_k_slider = gr.Slider(
+                    minimum=1,
+                    maximum=1000,
+                    value=50,
+                    step=1,
+                    label=get_text("top_k"),
+                    info=get_text("top_k_info")
+                )
+            
+            with gr.Row():
+                seed_number = gr.Number(
+                    value=-1,
+                    label=get_text("seed"),
+                    info=get_text("seed_info"),
+                    precision=0
+                )
+                random_seed_btn = gr.Button(get_text("random_seed_btn"), size="sm")
+        
+        # Вкладки для одиночной и пакетной обработки
+        tabs = gr.Tabs()
+        with tabs:
+            # Вкладка одиночной обработки
+            single_tab = gr.TabItem(get_text("single_processing"))
+            with single_tab:
+                with gr.Row():
+                    with gr.Column(scale=1):
+                        single_image = gr.Image(
+                            type="numpy",
+                            label=get_text("upload_image"),
+                            height=350
+                        )
+                        single_prompt = gr.Textbox(
+                            label=get_text("prompt"),
+                            placeholder=get_text("prompt_placeholder"),
+                            lines=3
+                        )
+                        single_submit_btn = gr.Button(get_text("generate_btn"), variant="primary")
+                    
+                    with gr.Column(scale=1):
+                        single_output = gr.Textbox(
+                            label=get_text("result"),
+                            lines=15,
+                            show_copy_button=True
+                        )
+                
+                # Кликабельные примеры промтов
+                gr.Markdown(f"### {get_text('examples_title')}")
+                single_examples = gr.Examples(
+                    examples=[
+                        [get_text("example_1")],
+                        [get_text("example_2")],
+                        [get_text("example_3")],
+                        [get_text("example_4")]
+                    ],
+                    inputs=[single_prompt],
+                    label=""
+                )
+            
+            # Вкладка пакетной обработки
+            batch_tab = gr.TabItem(get_text("batch_processing"))
+            with batch_tab:
+                with gr.Row():
+                    with gr.Column(scale=1):
+                        batch_images = gr.File(
+                            file_count="multiple",
+                            label=get_text("upload_images"),
+                            file_types=["image"]
+                        )
+                        batch_prompts = gr.Textbox(
+                            label=get_text("prompts_multiline"),
+                            placeholder=get_text("prompts_placeholder"),
+                            lines=5,
+                            info=get_text("prompts_info")
+                        )
+                        batch_submit_btn = gr.Button(get_text("process_batch_btn"), variant="primary")
+                    
+                    with gr.Column(scale=1):
+                        batch_output = gr.Textbox(
+                            label=get_text("results"),
+                            lines=20,
+                            show_copy_button=True
+                        )
+        
+        # Примеры использования
+        examples_md = gr.Markdown(f"""
+        ### {get_text("examples_title")}
+        - "{get_text("example_1")}"
+        - "{get_text("example_2")}"
+        - "{get_text("example_3")}"
+        - "{get_text("example_4")}"
+        """)
+        
+        # Обра��отчики событий
+        def change_language(lang):
+            global current_language
+            current_language = lang
+            
+            # Return updated text for all components
+            return [
+                f"""
+        # {get_text("header")}
+        
+        {get_text("subtitle")}
+        """,  # header_md
+                gr.update(label=get_text("model_selection"), info=get_text("model_info")),  # model_dropdown
+                gr.update(label=get_text("language"), info=get_text("language_info")),  # language_dropdown
+                gr.update(label=get_text("advanced_params")),  # advanced_accordion
+                gr.update(label=get_text("max_tokens"), info=get_text("max_tokens_info")),  # max_tokens_slider
+                gr.update(label=get_text("temperature"), info=get_text("temperature_info")),  # temperature_slider
+                gr.update(label=get_text("top_p"), info=get_text("top_p_info")),  # top_p_slider
+                gr.update(label=get_text("top_k"), info=get_text("top_k_info")),  # top_k_slider
+                gr.update(label=get_text("seed"), info=get_text("seed_info")),  # seed_number
+                gr.update(value=get_text("random_seed_btn")),  # random_seed_btn
+                gr.update(label=get_text("single_processing")),  # single_tab
+                gr.update(label=get_text("upload_image")),  # single_image
+                gr.update(label=get_text("prompt"), placeholder=get_text("prompt_placeholder")),  # single_prompt
+                gr.update(value=get_text("generate_btn")),  # single_submit_btn
+                gr.update(label=get_text("result")),  # single_output
+                gr.update(label=get_text("batch_processing")),  # batch_tab
+                gr.update(label=get_text("upload_images")),  # batch_images
+                gr.update(label=get_text("prompts_multiline"), placeholder=get_text("prompts_placeholder"), info=get_text("prompts_info")),  # batch_prompts
+                gr.update(value=get_text("process_batch_btn")),  # batch_submit_btn
+                gr.update(label=get_text("results")),  # batch_output
+                f"""
+        ### {get_text("examples_title")}
+        - "{get_text("example_1")}"
+        - "{get_text("example_2")}"
+        - "{get_text("example_3")}"
+        - "{get_text("example_4")}"
+        """  # examples_md
+            ]
+        
+        language_dropdown.change(
+            fn=change_language,
+            inputs=language_dropdown,
+            outputs=[
+                header_md,
+                model_dropdown,
+                language_dropdown,
+                advanced_accordion,
+                max_tokens_slider,
+                temperature_slider,
+                top_p_slider,
+                top_k_slider,
+                seed_number,
+                random_seed_btn,
+                single_tab,
+                single_image,
+                single_prompt,
+                single_submit_btn,
+                single_output,
+                batch_tab,
+                batch_images,
+                batch_prompts,
+                batch_submit_btn,
+                batch_output,
+                examples_md
+            ]
+        )
+
+        random_seed_btn.click(
+            fn=random_seed,
+            outputs=seed_number
+        )
+        
+        single_submit_btn.click(
+            fn=process_single_image,
+            inputs=[
+                single_image,
+                single_prompt,
+                model_dropdown,
+                max_tokens_slider,
+                temperature_slider,
+                top_p_slider,
+                top_k_slider,
+                seed_number
+            ],
+            outputs=single_output
+        )
+        
+        batch_submit_btn.click(
+            fn=process_batch_images,
+            inputs=[
+                batch_images,
+                batch_prompts,
+                model_dropdown,
+                max_tokens_slider,
+                temperature_slider,
+                top_p_slider,
+                top_k_slider,
+                seed_number
+            ],
+            outputs=batch_output
+        )
+        
+        return demo
+
+# Создаем интерфейс Gradio
+demo = create_interface()
+
+if __name__ == "__main__":
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False,
+        show_error=True
+    )