app.py

from flask import Flask, request, Response
import requests
import json
import time
from openai import OpenAI

app = Flask(__name__)

def get_token():
    url = "https://fluxaiweb.com/flux/getToken"
    response = requests.get(url)
    if response.status_code == 200:
        response_json = response.json()
        return response_json.get("data", {}).get("token")
    return None

def req_flux(token, prompt_value, aspect_ratio="1:1", output_format="webp", num_outputs=1, output_quality=90):
    url = "https://fluxaiweb.com/flux/generateImage"
    payload = {
        "prompt": prompt_value,
        "aspectRatio": aspect_ratio,
        "outputFormat": output_format,
        "numOutputs": num_outputs,
        "outputQuality": output_quality
    }
    headers = {
        'Content-Type': 'application/json',
        'token': token
    }
    try:
        response = requests.post(url, headers=headers, json=payload)
        response.raise_for_status()
        data = response.json()
        return data.get("data", {}).get("image")
    except requests.exceptions.RequestException as e:
        print(f"Error making request: {e}")
        return None

def generate_optimized_prompt(api_key, api_base, system_prompt, user_input):
    client = OpenAI(api_key=api_key, base_url=api_base)

    completion = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": user_input}
        ]
    )
    return completion.choices[0].message.content

def generate_fake_stream(image_url, optimized_prompt):
    chunks = [
        "I'm generating an optimized prompt based on your input...",
        f"Optimized prompt: {optimized_prompt}",
        "Now generating an image based on the optimized prompt...",
        "The image is being processed...",
        "Almost there...",
        f"Image generated successfully! Here it is:\n\n![Generated Image]({image_url})"
    ]
    
    for i, chunk in enumerate(chunks):
        yield f"data: {json.dumps({'id': f'chatcmpl-{int(time.time())}', 'object': 'chat.completion.chunk', 'created': int(time.time()), 'model': 'flux-ai-image-generator', 'choices': [{'index': 0, 'delta': {'role': 'assistant' if i == 0 else None, 'content': chunk}, 'finish_reason': None if i < len(chunks) - 1 else 'stop'}]})}\n\n"
        time.sleep(1)  # Simulate processing time
    
    yield "data: [DONE]\n\n"

@app.route('/hf/v1/chat/completions', methods=['POST'])
def chat_completions():
    data = request.json
    messages = data.get('messages', [])
    stream = data.get('stream', False)
    
    # Extract the prompt from the last user message
    user_input = next((msg['content'] for msg in reversed(messages) if msg['role'] == 'user'), None)
    
    if not user_input:
        return Response(json.dumps({'error': 'No valid user input provided'}), status=400, mimetype='application/json')
    
    # Generate optimized prompt using GPT-4-mini
    '''
    secret_variable：
    api_key = ""  # Replace with your actual API key
    api_base = ""  # Replace with your actual API base URL
    '''
    system_prompt = """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"、"ultra-detailed"、"high resolution"、"photorealistic" 这样的标签可以显著提高图像的细节和整体质量。像"illustration"、"lensflare"、"cinematic lighting" 这样的风格词定义图像的风格和光影效果。像"best lighting"、"volumetric lighting"、"depth of field" 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，同时加入细致的纹理和高光处理。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"lush greenery"、"golden sunlight"、"crystal clear river" 这样的环境词可以丰富场景，并增强其视觉吸引力。考虑添加天气效果，如"soft morning mist"、"sunset glow" 来进一步增强场景的氛围。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：我会发送给您一个图像场景。需要你生成详细的图像描述。图像描述必须是英文，输出为Positive Prompt。确保提示词仅用于描述图像内容，不包含会显示在图像中的文本。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, ultra-detailed, high resolution, photorealistic, illustration style, best lighting, volumetric lighting, depth of field, sharp focus, detailed character, richly textured environment."""
    optimized_prompt = generate_optimized_prompt(api_key, api_base, system_prompt, user_input)
    
    # Generate image using the optimized prompt
    token = get_token()
    if not token:
        return Response(json.dumps({'error': 'Failed to get token'}), status=500, mimetype='application/json')
    
    image_url = req_flux(token, optimized_prompt)
    if not image_url:
        return Response(json.dumps({'error': 'Failed to generate image'}), status=500, mimetype='application/json')
    
    if stream:
        return Response(generate_fake_stream(image_url, optimized_prompt), mimetype='text/event-stream')
    else:
        response = {
            "id": f"chatcmpl-{int(time.time())}",
            "object": "chat.completion",
            "created": int(time.time()),
            "model": "flux-ai-image-generator",
            "choices": [
                {
                    "index": 0,
                    "message": {
                        "role": "assistant",
                        "content": f"I've generated an optimized prompt based on your input: '{optimized_prompt}'\n\nUsing this prompt, I've created an image. Here it is:\n\n![Generated Image]({image_url})"
                    },
                    "finish_reason": "stop"
                }
            ],
            "usage": {
                "prompt_tokens": len(user_input.split()),
                "completion_tokens": len(optimized_prompt.split()) + 20,  # Approximate
                "total_tokens": len(user_input.split()) + len(optimized_prompt.split()) + 20
            }
        }
        return Response(json.dumps(response), mimetype='application/json')

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=7860)