app.py

from flask import Flask, request, jsonify, Response
import requests
import json
import time
import random
import logging
import sys
import asyncio

app = Flask(__name__)

# 配置日志
logging.basicConfig(
    level=logging.DEBUG,
    format='%(asctime)s [%(levelname)s] %(message)s',
    handlers=[
        logging.StreamHandler(sys.stdout)
    ]
)
logger = logging.getLogger(__name__)

SYSTEM_ASSISTANT = """作为 Stable Diffusion Prompt 提示词专家，您将从关键词中创建提示，通常来自 Danbooru 等数据库。
提示通常描述图像，使用常见词汇，按重要性排列，并用逗号分隔。避免使用"-"或"."，但可以接受空格和自然语言。避免词汇重复。

为了强调关键词，请将其放在括号中以增加其权重。例如，"(flowers)"将'flowers'的权重增加1.1倍，而"(((flowers)))"将其增加1.331倍。使用"(flowers:1.5)"将'flowers'的权重增加1.5倍。只为重要的标签增加权重。

提示包括三个部分：**前缀** （质量标签+风格词+效果器）+ **主题** （图像的主要焦点）+ **场景** （背景、环境）。

*   前缀影响图像质量。像"masterpiece"、"best quality"、"4k"这样的标签可以提高图像的细节。像"illustration"、"lensflare"这样的风格词定义图像的风格。像"bestlighting"、"lensflare"、"depthoffield"这样的效果器会影响光照和深度。

*   主题是图像的主要焦点，如角色或场景。对主题进行详细描述可以确保图像丰富而详细。增加主题的权重以增强其清晰度。对于角色，描述面部、头发、身体、服装、姿势等特征。

*   场景描述环境。没有场景，图像的背景是平淡的，主题显得过大。某些主题本身包含场景（例如建筑物、风景）。像"花草草地"、"阳光"、"河流"这样的环境词可以丰富场景。你的任务是设计图像生成的提示。请按照以下步骤进行操作：

1.  我会发送给您一个图像场景。需要你生成详细的图像描述
2.  图像描述必须是英文，输出为Positive Prompt。

示例：

我发送：二战时期的护士。
您回复只回复：
A WWII-era nurse in a German uniform, holding a wine bottle and stethoscope, sitting at a table in white attire, with a table in the background, masterpiece, best quality, 4k, illustration style, best lighting, depth of field, detailed character, detailed environment.
"""

def get_random_token(auth_header):
    if not auth_header:
        return None
    if auth_header.startswith('Bearer '):
        auth_header = auth_header[7:]
    tokens = [token.strip() for token in auth_header.split(',') if token.strip()]
    if not tokens:
        return None
    return f"Bearer {random.choice(tokens)}"

def translate_and_enhance_prompt(prompt, auth_token):
    translate_url = 'https://api.siliconflow.cn/v1/chat/completions'
    translate_body = {
        'model': 'Qwen/Qwen2-72B-Instruct',
        'messages': [
            {'role': 'system', 'content': SYSTEM_ASSISTANT},
            {'role': 'user', 'content': prompt}
        ]
    }
    headers = {
        'Content-Type': 'application/json',
        'Authorization': auth_token
    }
    
    logger.info(f"Sending request to {translate_url}")
    logger.info(f"Request headers: {headers}")
    logger.info(f"Request body: {json.dumps(translate_body, ensure_ascii=False)}")
    
    try:
        response = requests.post(translate_url, headers=headers, json=translate_body, timeout=30)
        logger.info(f"Response status code: {response.status_code}")
        logger.info(f"Response content: {response.text}")
        
        response.raise_for_status()
        result = response.json()
        return result['choices'][0]['message']['content']
    except requests.exceptions.RequestException as e:
        logger.error(f"Error in translate_and_enhance_prompt: {str(e)}")
        raise

@app.route('/')
def index():
    return "text-to-image with siliconflow", 200

@app.route('/ai/v1/chat/completions', methods=['POST'])
def handle_request():
    try:
        body = request.json
        model = body.get('model')
        messages = body.get('messages')
        stream = body.get('stream', False)
        if not model or not messages or len(messages) == 0:
            return jsonify({"error": "Bad Request: Missing required fields"}), 400
        
        prompt = messages[-1]['content']
        
        random_token = get_random_token(request.headers.get('Authorization'))
        if not random_token:
            return jsonify({"error": "Unauthorized: Invalid or missing Authorization header"}), 401
        
        try:
            enhanced_prompt = translate_and_enhance_prompt(prompt, random_token)
        except Exception as e:
            logger.error(f"Error in translate_and_enhance_prompt: {str(e)}")
            return jsonify({"error": "Failed to enhance prompt"}), 500
        
        new_url = f'https://api.siliconflow.cn/v1/{model}/text-to-image'
        new_request_body = {
            "prompt": enhanced_prompt,
            "image_size": "1024x1024",
            "batch_size": 1,
            "num_inference_steps": 4,
            "guidance_scale": 1
        }
        
        headers = {
            'accept': 'application/json',
            'content-type': 'application/json',
            'Authorization': random_token
        }
        
        logger.info(f"Sending request to {new_url}")
        logger.info(f"Request headers: {headers}")
        logger.info(f"Request body: {json.dumps(new_request_body, ensure_ascii=False)}")
        
        try:
            response = requests.post(new_url, headers=headers, json=new_request_body, timeout=60)
            logger.info(f"Response status code: {response.status_code}")
            logger.info(f"Response content: {response.text}")
            
            response.raise_for_status()
            response_body = response.json()
            
            if 'images' in response_body and response_body['images'] and 'url' in response_body['images'][0]:
                image_url = response_body['images'][0]['url']
                logger.info(f"Successfully retrieved image URL: {image_url}")
            else:
                logger.error(f"Unexpected response structure: {response_body}")
                return jsonify({"error": "Unexpected response structure from image generation API"}), 500
        except requests.exceptions.RequestException as e:
            logger.error(f"Error in image generation request: {str(e)}")
            return jsonify({"error": "Failed to generate image"}), 500
        except (KeyError, IndexError, ValueError) as e:
            logger.error(f"Error parsing image generation response: {str(e)}")
            return jsonify({"error": "Failed to parse image generation response"}), 500
        
        unique_id = int(time.time() * 1000)
        current_timestamp = unique_id // 1000
        system_fingerprint = "fp_" + ''.join(random.choices('abcdefghijklmnopqrstuvwxyz0123456789', k=9))
        
        image_data = {'data': [{'url': image_url}]}
        
        if stream:
            return stream_response(request, unique_id, image_data, prompt, enhanced_prompt, "1024x1024", current_timestamp, model, system_fingerprint)
        else:
            return non_stream_response(unique_id, image_data, prompt, enhanced_prompt, "1024x1024", current_timestamp, model, system_fingerprint)
    except Exception as e:
        logger.error(f"Unexpected error in handle_request: {str(e)}")
        return jsonify({"error": f"Internal Server Error: {str(e)}"}), 500

def stream_response(request, unique_id, image_data, original_prompt, translated_prompt, size, created, model, system_fingerprint):
    logger.debug("Starting stream response")
    response = Response(stream_with_context(generate_stream(unique_id, image_data, original_prompt, translated_prompt, size, created, model, system_fingerprint)), content_type='text/event-stream')
    logger.debug("Stream response completed")
    return response

def generate_stream(unique_id, image_data, original_prompt, translated_prompt, size, created, model, system_fingerprint):
    chunks = [
        f"原始提示词：\n{original_prompt}\n",
        f"翻译后的提示词：\n{translated_prompt}\n",
        f"图像规格：{size}\n",
        "正在根据提示词生成图像...\n",
        "图像正在处理中...\n",
        "即将完成...\n",
        f"生成成功！\n图像生成完毕，以下是结果：\n\n![生成的图像]({image_data['data'][0]['url']})"
    ]

    for i, chunk in enumerate(chunks):
        json_chunk = json.dumps({
            "id": unique_id,
            "object": "chat.completion.chunk",
            "created": created,
            "model": model,
            "system_fingerprint": system_fingerprint,
            "choices": [{
                "index": 0,
                "delta": {"content": chunk},
                "logprobs": None,
                "finish_reason": None
            }]
        })
        yield f"data: {json_chunk}\n\n"
        time.sleep(0.5)  # 模拟生成时间

    final_chunk = json.dumps({
        "id": unique_id,
        "object": "chat.completion.chunk",
        "created": created,
        "model": model,
        "system_fingerprint": system_fingerprint,
        "choices": [{
            "index": 0,
            "delta": {},
            "logprobs": None,
            "finish_reason": "stop"
        }]
    })
    yield f"data: {final_chunk}\n\n"

def non_stream_response(unique_id, image_data, original_prompt, translated_prompt, size, created, model, system_fingerprint):
    content = (
        f"原始提示词：{original_prompt}\n"
        f"翻译后的提示词：{translated_prompt}\n"
        f"图像规格：{size}\n"
        f"图像生成成功！\n"
        f"以下是结果：\n\n"
        f"![生成的图像]({image_data['data'][0]['url']})"
    )

    response = {
        'id': unique_id,
        'object': "chat.completion",
        'created': created,
        'model': model,
        'system_fingerprint': system_fingerprint,
        'choices': [{
            'index': 0,
            'message': {
                'role': "assistant",
                'content': content
            },
            'finish_reason': "stop"
        }],
        'usage': {
            'prompt_tokens': len(original_prompt),
            'completion_tokens': len(content),
            'total_tokens': len(original_prompt) + len(content)
        }
    }

    return jsonify(response)

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8000)