app.py

from flask import Flask, request, Response
import os
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, user_input):
    client = OpenAI(api_key=api_key, base_url=api_base)

    prompt = (
        f"As a Stable Diffusion prompt expert, your task is to directly modify the following sentence: '{user_input}'"
        "into a proper Stable Diffusion prompt in English, formatted according to the guidelines provided: "
        "1. Create prompts using common vocabulary, arranged by importance and separated by commas. Avoid using '-' or '.', "
        "but spaces and natural language are acceptable. Avoid repeating words. "
        "2. To emphasize keywords, enclose them in parentheses to increase their weight. For example, '(flowers)' increases "
        "the weight of 'flowers' by 1.1 times, '(((flowers)))' increases it by 1.331 times, and '(flowers:1.5)' increases "
        "the weight of 'flowers' by 1.5 times. Only increase the weight for important tags. "
        "3. The prompt should consist of three parts: prefix (quality tags + style words + effectors), subject "
        "(main focus of the image), and scene (background, environment). The prefix affects image quality; tags like "
        "'masterpiece,' 'best quality,' and '4k' enhance image detail, style words like 'illustration' and 'lensflare' "
        "define the style of the image, and effectors like 'best lighting,' 'lensflare,' and 'depth of field' influence "
        "lighting and depth. The subject is the main focus of the image, such as a character or scene. Providing a detailed "
        "description of the subject ensures a rich and detailed image, and increasing the subject's weight enhances its clarity. "
        "For characters, describe features like the face, hair, body, clothing, and pose. The scene describes the environment; "
        "without a scene, the image's background appears plain, making the subject seem overly large. Some subjects inherently "
        "include scenes (such as buildings or landscapes), and environmental words like 'flower meadow,' 'sunlight,' and 'river' "
        "can enrich the scene. "
        "Return only the revised prompt in English."
    )

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

def generate_fake_stream(image_url, optimized_prompt):
    chunks = [
        "✨ 开始生成...\n正在根据您的输入生成优化提示词...\n",
        f"🎨 优化后的提示词：\n{optimized_prompt}\n",
        "🖼️ 正在根据优化后的提示词生成图像...\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_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(0.5)  # 模拟处理时间
    
    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
    api_key = os.getenv('api_key')
    api_base = os.getenv('api_base')
    
    optimized_prompt = generate_optimized_prompt(api_key, api_base, 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)