import requests
from PIL import Image
from transformers import AutoModelForCausalLM, AutoProcessor, MarianMTModel, MarianTokenizer
from diffusers import StableDiffusionPipeline
import torch
import gradio as gr

# 验证 SentencePiece 是否安装
try:
    import sentencepiece
    print("SentencePiece is installed successfully!")
except ImportError:
    print("SentencePiece is NOT installed!")

# 设置设备
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")

# 加载 Florence-2 模型和处理器
print("Loading Florence-2 model...")
model = AutoModelForCausalLM.from_pretrained("MiaoshouAI/Florence-2-base-PromptGen-v1.5", trust_remote_code=True).to(device)
processor = AutoProcessor.from_pretrained("MiaoshouAI/Florence-2-base-PromptGen-v1.5", trust_remote_code=True)
print("Florence-2 model loaded successfully.")

# 加载 Helsinki-NLP 的翻译模型（英文到中文）
print("Loading translation model...")
translation_model_name = "Helsinki-NLP/opus-mt-en-zh"
translation_tokenizer = MarianTokenizer.from_pretrained(translation_model_name)
translation_model = MarianMTModel.from_pretrained(translation_model_name).to(device)
print("Translation model loaded successfully.")

# 加载 Stable Diffusion 模型
print("Loading Stable Diffusion model...")
pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to(device)
print("Stable Diffusion model loaded successfully.")

# 翻译函数
def translate_to_chinese(text):
    try:
        # 确保输入是字符串
        if not isinstance(text, str):
            print(f"Input is not a string: {text} (type: {type(text)})")
            text = str(text)  # 强制转换为字符串
        
        print("Input text for translation:", text)
        tokenized_text = translation_tokenizer(text, return_tensors="pt", max_length=512, truncation=True).to(device)
        translated_tokens = translation_model.generate(**tokenized_text)
        translated_text = translation_tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
        print("Translated text:", translated_text)
        return translated_text
    except Exception as e:
        print("Translation error:", str(e))
        return f"Translation error: {str(e)}"

# 生成描述并翻译
def generate_caption(image):
    try:
        # 如果输入是 URL，下载图片
        if isinstance(image, str) and (image.startswith("http://") or image.startswith("https://")):
            print("Downloading image from URL...")
            try:
                response = requests.get(image, stream=True, timeout=10)
                response.raise_for_status()  # 检查请求是否成功
                image = Image.open(response.raw)
                print("Image downloaded successfully.")
            except requests.exceptions.RequestException as e:
                return f"Failed to download image: {str(e)}", None
        # 如果输入是文件路径，直接打开图片
        else:
            print("Loading image from file...")
            image = Image.open(image)
        
        # 准备输入
        prompt = "<MORE_DETAILED_CAPTION>"
        inputs = processor(text=prompt, images=image, return_tensors="pt").to(device)
        
        # 生成文本
        print("Generating caption...")
        generated_ids = model.generate(
            input_ids=inputs["input_ids"],
            pixel_values=inputs["pixel_values"],
            max_new_tokens=1024,
            do_sample=False,
            num_beams=3
        )
        generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
        print("Generated text:", generated_text)
        print("Type of generated text:", type(generated_text))
        
        # 解析生成的文本
        parsed_answer = processor.post_process_generation(generated_text, task=prompt, image_size=(image.width, image.height))
        print("Parsed answer:", parsed_answer)
        print("Type of parsed answer:", type(parsed_answer))
        
        # 翻译成中文
        print("Translating to Chinese...")
        translated_answer = translate_to_chinese(parsed_answer)
        print("Translation completed.")
        
        return translated_answer, parsed_answer
    except Exception as e:
        print("Error:", str(e))
        return f"Error: {str(e)}", None

# 生成图片
def generate_images_from_prompt(prompt):
    try:
        # 生成 4 张图片
        images = pipe(prompt, num_images_per_prompt=1).images
        return images
    except Exception as e:
        print("Image generation error:", str(e))
        return None

# Gradio 界面
def gradio_interface(image):
    # 生成描述并翻译
    translated_answer, parsed_answer = generate_caption(image)
    if translated_answer.startswith("Error"):
        return translated_answer, None
    
    # 返回翻译后的描述
    return translated_answer, None

# 生成图片的 Gradio 界面
def generate_images_interface(prompt):
    # 生成图片
    images = generate_images_from_prompt(prompt)
    if images is None:
        return None
    
    # 返回 4 张图片
    return images

# 创建 Gradio 应用
with gr.Blocks() as demo:
    gr.Markdown("# Florence-2 Prompt Generation and Image Generation")
    
    with gr.Row():
        with gr.Column():
            # 输入：上传图片或输入图片 URL
            image_input = gr.Image(label="Upload Image or Enter Image URL", type="filepath")
            # 输出：生成的描述（翻译成中文）
            caption_output = gr.Textbox(label="Generated Caption (Translated to Chinese)")
            # 按钮：生成描述
            generate_caption_button = gr.Button("Generate Caption")
        
        with gr.Column():
            # 输入：生成的描述（用于生成图片）
            prompt_input = gr.Textbox(label="Generated Caption (for Image Generation)")
            # 输出：生成的图片
            image_output = gr.Gallery(label="Generated Images")
            # 按钮：生成图片
            generate_images_button = gr.Button("Generate Images")
    
    # 绑定事件
    generate_caption_button.click(gradio_interface, inputs=image_input, outputs=[caption_output, prompt_input])
    generate_images_button.click(generate_images_interface, inputs=prompt_input, outputs=image_output)

# 启动 Gradio 应用
print("Launching Gradio app...")
demo.launch()