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import os |
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import time |
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import logging |
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import requests |
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import json |
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import uuid |
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import concurrent.futures |
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import threading |
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import base64 |
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import io |
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from itertools import chain |
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from PIL import Image |
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from datetime import datetime |
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from apscheduler.schedulers.background import BackgroundScheduler |
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from flask import Flask, request, jsonify, Response, stream_with_context |
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from werkzeug.middleware.proxy_fix import ProxyFix |
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|
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os.environ['TZ'] = 'Asia/Shanghai' |
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time.tzset() |
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|
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logging.basicConfig(level=logging.INFO, |
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format='%(asctime)s - %(levelname)s - %(message)s') |
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|
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API_ENDPOINT = "https://api-st.siliconflow.cn/v1/user/info" |
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TEST_MODEL_ENDPOINT = "https://api-st.siliconflow.cn/v1/chat/completions" |
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MODELS_ENDPOINT = "https://api-st.siliconflow.cn/v1/models" |
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EMBEDDINGS_ENDPOINT = "https://api-st.siliconflow.cn/v1/embeddings" |
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IMAGE_ENDPOINT = "https://api-st.siliconflow.cn/v1/images/generations" |
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|
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app = Flask(__name__) |
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app.wsgi_app = ProxyFix(app.wsgi_app, x_for=1) |
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|
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text_models = [] |
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free_text_models = [] |
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embedding_models = [] |
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free_embedding_models = [] |
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image_models = [] |
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free_image_models = [] |
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|
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invalid_keys_global = [] |
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free_keys_global = [] |
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unverified_keys_global = [] |
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valid_keys_global = [] |
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|
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executor = concurrent.futures.ThreadPoolExecutor(max_workers=10000) |
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model_key_indices = {} |
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|
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request_timestamps = [] |
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token_counts = [] |
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request_timestamps_day = [] |
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token_counts_day = [] |
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data_lock = threading.Lock() |
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|
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def get_credit_summary(api_key): |
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headers = { |
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"Authorization": f"Bearer {api_key}", |
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"Content-Type": "application/json" |
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} |
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max_retries = 3 |
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|
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for attempt in range(max_retries): |
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try: |
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response = requests.get(API_ENDPOINT, headers=headers, timeout=2) |
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response.raise_for_status() |
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data = response.json().get("data", {}) |
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total_balance = data.get("totalBalance", 0) |
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logging.info(f"获取额度，API Key：{api_key}，当前额度: {total_balance}") |
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return {"total_balance": float(total_balance)} |
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except requests.exceptions.Timeout as e: |
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logging.error(f"获取额度信息失败，API Key：{api_key}，尝试次数：{attempt+1}/{max_retries}，错误信息：{e} (Timeout)") |
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if attempt >= max_retries - 1: |
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logging.error(f"获取额度信息失败，API Key：{api_key}，所有重试次数均已失败 (Timeout)") |
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except requests.exceptions.RequestException as e: |
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logging.error(f"获取额度信息失败，API Key：{api_key}，错误信息：{e}") |
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return None |
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|
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FREE_MODEL_TEST_KEY = ( |
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"sk-bmjbjzleaqfgtqfzmcnsbagxrlohriadnxqrzfocbizaxukw" |
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) |
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|
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FREE_IMAGE_LIST = [ |
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"stabilityai/stable-diffusion-3-5-large", |
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"black-forest-labs/FLUX.1-schnell", |
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"stabilityai/stable-diffusion-3-medium", |
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"stabilityai/stable-diffusion-xl-base-1.0", |
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"stabilityai/stable-diffusion-2-1" |
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] |
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|
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def test_model_availability(api_key, model_name): |
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headers = { |
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"Authorization": f"Bearer {api_key}", |
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"Content-Type": "application/json" |
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} |
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try: |
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response = requests.post( |
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TEST_MODEL_ENDPOINT, |
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headers=headers, |
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json={ |
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"model": model_name, |
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"messages": [{"role": "user", "content": "hi"}], |
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"max_tokens": 5, |
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"stream": False |
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}, |
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timeout=5 |
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) |
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if response.status_code == 429 or response.status_code == 200: |
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return True |
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else: |
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return False |
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except requests.exceptions.RequestException as e: |
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logging.error( |
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f"测试模型 {model_name} 可用性失败，" |
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f"API Key：{api_key}，错误信息：{e}" |
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) |
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return False |
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|
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def create_base64_markdown_image(image_url): |
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try: |
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response = requests.get(image_url, stream=True) |
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response.raise_for_status() |
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|
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image_data = response.content |
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base64_encoded = base64.b64encode(image_data).decode('utf-8') |
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mime_type = response.headers.get('Content-Type', 'image/png') |
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markdown_image_link = f"" |
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print(markdown_image_link) |
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logging.info(f"Created base64 markdown image link.") |
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return markdown_image_link |
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except requests.exceptions.RequestException as e: |
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logging.error(f"Error downloading image: {e}") |
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return None |
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except Exception as e: |
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logging.error(f"Error during processing: {e}") |
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return None |
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|
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def refresh_models(): |
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global text_models, free_text_models |
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global embedding_models, free_embedding_models |
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global image_models, free_image_models |
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|
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text_models = get_all_models(FREE_MODEL_TEST_KEY, "chat") |
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embedding_models = get_all_models(FREE_MODEL_TEST_KEY, "embedding") |
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image_models = get_all_models(FREE_MODEL_TEST_KEY, "text-to-image") |
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free_text_models = [] |
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free_embedding_models = [] |
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free_image_models = [] |
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|
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ban_models_str = os.environ.get("BAN_MODELS") |
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ban_models = [] |
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if ban_models_str: |
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try: |
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ban_models = json.loads(ban_models_str) |
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if not isinstance(ban_models, list): |
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logging.warning( |
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"环境变量 BAN_MODELS 格式不正确，应为 JSON 数组。" |
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) |
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ban_models = [] |
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except json.JSONDecodeError: |
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logging.warning( |
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"环境变量 BAN_MODELS JSON 解析失败，请检查格式。" |
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) |
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ban_models = [] |
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|
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text_models = [model for model in text_models if model not in ban_models] |
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embedding_models = [model for model in embedding_models if model not in ban_models] |
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image_models = [model for model in image_models if model not in ban_models] |
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|
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with concurrent.futures.ThreadPoolExecutor( |
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max_workers=10000 |
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) as executor: |
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future_to_model = { |
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executor.submit( |
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test_model_availability, |
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FREE_MODEL_TEST_KEY, |
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model |
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): model for model in text_models |
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} |
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for future in concurrent.futures.as_completed(future_to_model): |
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model = future_to_model[future] |
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try: |
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is_free = future.result() |
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if is_free: |
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free_text_models.append(model) |
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except Exception as exc: |
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logging.error(f"模型 {model} 测试生成异常: {exc}") |
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|
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with concurrent.futures.ThreadPoolExecutor( |
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max_workers=10000 |
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) as executor: |
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future_to_model = { |
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executor.submit( |
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test_embedding_model_availability, |
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FREE_MODEL_TEST_KEY, model |
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): model for model in embedding_models |
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} |
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for future in concurrent.futures.as_completed(future_to_model): |
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model = future_to_model[future] |
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try: |
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is_free = future.result() |
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if is_free: |
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free_embedding_models.append(model) |
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except Exception as exc: |
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logging.error(f"模型 {model} 测试生成异常: {exc}") |
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|
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with concurrent.futures.ThreadPoolExecutor( |
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max_workers=10000 |
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) as executor: |
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future_to_model = { |
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executor.submit( |
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test_image_model_availability, |
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FREE_MODEL_TEST_KEY, model |
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): model for model in image_models |
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} |
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for future in concurrent.futures.as_completed(future_to_model): |
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model = future_to_model[future] |
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try: |
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is_free = future.result() |
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if is_free: |
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free_image_models.append(model) |
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except Exception as exc: |
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logging.error(f"模型 {model} 测试生成异常: {exc}") |
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|
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logging.info(f"所有文本模型列表：{text_models}") |
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logging.info(f"免费文本模型列表：{free_text_models}") |
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logging.info(f"所有向量模型列表：{embedding_models}") |
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logging.info(f"免费向量模型列表：{free_embedding_models}") |
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logging.info(f"所有生图模型列表：{image_models}") |
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logging.info(f"免费生图模型列表：{free_image_models}") |
|
|
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def test_embedding_model_availability(api_key, model_name): |
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headers = { |
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"Authorization": f"Bearer {api_key}", |
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"Content-Type": "application/json" |
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} |
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try: |
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response = requests.post( |
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EMBEDDINGS_ENDPOINT, |
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headers=headers, |
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json={ |
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"model": model_name, |
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"input": ["hi"], |
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}, |
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timeout=10 |
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) |
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if response.status_code == 429 or response.status_code == 200: |
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return True |
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else: |
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return False |
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except requests.exceptions.RequestException as e: |
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logging.error( |
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f"测试向量模型 {model_name} 可用性失败，" |
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f"API Key：{api_key}，错误信息：{e}" |
|
) |
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return False |
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|
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def test_image_model_availability(api_key, model_name): |
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return model_name in FREE_IMAGE_LIST |
|
|
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def load_keys(): |
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keys_str = os.environ.get("KEYS") |
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test_model = os.environ.get( |
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"TEST_MODEL", |
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"Pro/google/gemma-2-9b-it" |
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) |
|
|
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if keys_str: |
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keys = [key.strip() for key in keys_str.split(',')] |
|
unique_keys = list(set(keys)) |
|
keys_str = ','.join(unique_keys) |
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os.environ["KEYS"] = keys_str |
|
|
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logging.info(f"加载的 keys：{unique_keys}") |
|
|
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with concurrent.futures.ThreadPoolExecutor( |
|
max_workers=10000 |
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) as executor: |
|
future_to_key = { |
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executor.submit( |
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process_key, key, test_model |
|
): key for key in unique_keys |
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} |
|
|
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invalid_keys = [] |
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free_keys = [] |
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unverified_keys = [] |
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valid_keys = [] |
|
|
|
for future in concurrent.futures.as_completed( |
|
future_to_key |
|
): |
|
key = future_to_key[future] |
|
try: |
|
key_type = future.result() |
|
if key_type == "invalid": |
|
invalid_keys.append(key) |
|
elif key_type == "free": |
|
free_keys.append(key) |
|
elif key_type == "unverified": |
|
unverified_keys.append(key) |
|
elif key_type == "valid": |
|
valid_keys.append(key) |
|
except Exception as exc: |
|
logging.error(f"处理 KEY {key} 生成异常: {exc}") |
|
|
|
logging.info(f"无效 KEY：{invalid_keys}") |
|
logging.info(f"免费 KEY：{free_keys}") |
|
logging.info(f"未实名 KEY：{unverified_keys}") |
|
logging.info(f"有效 KEY：{valid_keys}") |
|
|
|
global invalid_keys_global, free_keys_global |
|
global unverified_keys_global, valid_keys_global |
|
invalid_keys_global = invalid_keys |
|
free_keys_global = free_keys |
|
unverified_keys_global = unverified_keys |
|
valid_keys_global = valid_keys |
|
|
|
else: |
|
logging.warning("环境变量 KEYS 未设置。") |
|
|
|
def process_key(key, test_model): |
|
credit_summary = get_credit_summary(key) |
|
if credit_summary is None: |
|
return "invalid" |
|
else: |
|
total_balance = credit_summary.get("total_balance", 0) |
|
if total_balance <= 0.03: |
|
return "free" |
|
else: |
|
if test_model_availability(key, test_model): |
|
return "valid" |
|
else: |
|
return "unverified" |
|
|
|
def get_all_models(api_key, sub_type): |
|
headers = { |
|
"Authorization": f"Bearer {api_key}", |
|
"Content-Type": "application/json" |
|
} |
|
try: |
|
response = requests.get( |
|
MODELS_ENDPOINT, |
|
headers=headers, |
|
params={"sub_type": sub_type} |
|
) |
|
response.raise_for_status() |
|
data = response.json() |
|
if ( |
|
isinstance(data, dict) and |
|
'data' in data and |
|
isinstance(data['data'], list) |
|
): |
|
return [ |
|
model.get("id") for model in data["data"] |
|
if isinstance(model, dict) and "id" in model |
|
] |
|
else: |
|
logging.error("获取模型列表失败：响应数据格式不正确") |
|
return [] |
|
except requests.exceptions.RequestException as e: |
|
logging.error( |
|
f"获取模型列表失败，" |
|
f"API Key：{api_key}，错误信息：{e}" |
|
) |
|
return [] |
|
except (KeyError, TypeError) as e: |
|
logging.error( |
|
f"解析模型列表失败，" |
|
f"API Key：{api_key}，错误信息：{e}" |
|
) |
|
return [] |
|
|
|
def determine_request_type(model_name, model_list, free_model_list): |
|
if model_name in free_model_list: |
|
return "free" |
|
elif model_name in model_list: |
|
return "paid" |
|
else: |
|
return "unknown" |
|
|
|
def select_key(request_type, model_name): |
|
if request_type == "free": |
|
available_keys = ( |
|
free_keys_global + |
|
unverified_keys_global + |
|
valid_keys_global |
|
) |
|
elif request_type == "paid": |
|
available_keys = unverified_keys_global + valid_keys_global |
|
else: |
|
available_keys = ( |
|
free_keys_global + |
|
unverified_keys_global + |
|
valid_keys_global |
|
) |
|
|
|
if not available_keys: |
|
return None |
|
|
|
current_index = model_key_indices.get(model_name, 0) |
|
|
|
for _ in range(len(available_keys)): |
|
key = available_keys[current_index % len(available_keys)] |
|
current_index += 1 |
|
|
|
if key_is_valid(key, request_type): |
|
model_key_indices[model_name] = current_index |
|
return key |
|
else: |
|
logging.warning( |
|
f"KEY {key} 无效或达到限制，尝试下一个 KEY" |
|
) |
|
|
|
model_key_indices[model_name] = 0 |
|
return None |
|
|
|
def key_is_valid(key, request_type): |
|
if request_type == "invalid": |
|
return False |
|
|
|
credit_summary = get_credit_summary(key) |
|
if credit_summary is None: |
|
return False |
|
|
|
total_balance = credit_summary.get("total_balance", 0) |
|
|
|
if request_type == "free": |
|
return True |
|
elif request_type == "paid" or request_type == "unverified": |
|
return total_balance > 0 |
|
else: |
|
return False |
|
|
|
def check_authorization(request): |
|
authorization_key = os.environ.get("AUTHORIZATION_KEY") |
|
if not authorization_key: |
|
logging.warning("环境变量 AUTHORIZATION_KEY 未设置，此时无需鉴权即可使用，建议进行设置后再使用。") |
|
return True |
|
|
|
auth_header = request.headers.get('Authorization') |
|
if not auth_header: |
|
logging.warning("请求头中缺少 Authorization 字段。") |
|
return False |
|
|
|
if auth_header != f"Bearer {authorization_key}": |
|
logging.warning(f"无效的 Authorization 密钥：{auth_header}") |
|
return False |
|
|
|
return True |
|
|
|
scheduler = BackgroundScheduler() |
|
scheduler.add_job(load_keys, 'interval', hours=1) |
|
scheduler.remove_all_jobs() |
|
scheduler.add_job(refresh_models, 'interval', hours=1) |
|
|
|
@app.route('/') |
|
def index(): |
|
current_time = time.time() |
|
one_minute_ago = current_time - 60 |
|
one_day_ago = current_time - 86400 |
|
|
|
with data_lock: |
|
while request_timestamps and request_timestamps[0] < one_minute_ago: |
|
request_timestamps.pop(0) |
|
token_counts.pop(0) |
|
|
|
rpm = len(request_timestamps) |
|
tpm = sum(token_counts) |
|
|
|
with data_lock: |
|
while request_timestamps_day and request_timestamps_day[0] < one_day_ago: |
|
request_timestamps_day.pop(0) |
|
token_counts_day.pop(0) |
|
|
|
rpd = len(request_timestamps_day) |
|
tpd = sum(token_counts_day) |
|
|
|
return jsonify({"rpm": rpm, "tpm": tpm, "rpd": rpd, "tpd": tpd}) |
|
|
|
@app.route('/handsome/v1/models', methods=['GET']) |
|
def list_models(): |
|
if not check_authorization(request): |
|
return jsonify({"error": "Unauthorized"}), 401 |
|
|
|
detailed_models = [] |
|
|
|
for model in chain(text_models, embedding_models, image_models): |
|
detailed_models.append({ |
|
"id": model, |
|
"object": "model", |
|
"created": 1678888888, |
|
"owned_by": "openai", |
|
"permission": [], |
|
"root": model, |
|
"parent": None |
|
}) |
|
|
|
return jsonify({ |
|
"success": True, |
|
"data": detailed_models |
|
}) |
|
|
|
@app.route('/handsome/v1/dashboard/billing/usage', methods=['GET']) |
|
def billing_usage(): |
|
if not check_authorization(request): |
|
return jsonify({"error": "Unauthorized"}), 401 |
|
|
|
daily_usage = [] |
|
|
|
return jsonify({ |
|
"object": "list", |
|
"data": daily_usage, |
|
"total_usage": 0 |
|
}) |
|
|
|
@app.route('/handsome/v1/dashboard/billing/subscription', methods=['GET']) |
|
def billing_subscription(): |
|
if not check_authorization(request): |
|
return jsonify({"error": "Unauthorized"}), 401 |
|
|
|
keys = valid_keys_global + unverified_keys_global |
|
total_balance = 0 |
|
|
|
with concurrent.futures.ThreadPoolExecutor( |
|
max_workers=10000 |
|
) as executor: |
|
futures = [ |
|
executor.submit(get_credit_summary, key) for key in keys |
|
] |
|
|
|
for future in concurrent.futures.as_completed(futures): |
|
try: |
|
credit_summary = future.result() |
|
if credit_summary: |
|
total_balance += credit_summary.get("total_balance", 0) |
|
except Exception as exc: |
|
logging.error(f"获取额度信息生成异常: {exc}") |
|
|
|
return jsonify({ |
|
"object": "billing_subscription", |
|
"access_until": int(datetime(9999, 12, 31).timestamp()), |
|
"soft_limit": 0, |
|
"hard_limit": total_balance, |
|
"system_hard_limit": total_balance, |
|
"soft_limit_usd": 0, |
|
"hard_limit_usd": total_balance, |
|
"system_hard_limit_usd": total_balance |
|
}) |
|
|
|
@app.route('/handsome/v1/embeddings', methods=['POST']) |
|
def handsome_embeddings(): |
|
if not check_authorization(request): |
|
return jsonify({"error": "Unauthorized"}), 401 |
|
|
|
data = request.get_json() |
|
if not data or 'model' not in data: |
|
return jsonify({"error": "Invalid request data"}), 400 |
|
if data['model'] not in embedding_models: |
|
return jsonify({"error": "Invalid model"}), 400 |
|
|
|
model_name = data['model'] |
|
request_type = determine_request_type( |
|
model_name, |
|
embedding_models, |
|
free_embedding_models |
|
) |
|
api_key = select_key(request_type, model_name) |
|
|
|
if not api_key: |
|
return jsonify({"error": ("No available API key for this request type or all keys have reached their limits")}), 429 |
|
|
|
headers = { |
|
"Authorization": f"Bearer {api_key}", |
|
"Content-Type": "application/json" |
|
} |
|
|
|
try: |
|
start_time = time.time() |
|
response = requests.post( |
|
EMBEDDINGS_ENDPOINT, |
|
headers=headers, |
|
json=data, |
|
timeout=120 |
|
) |
|
|
|
if response.status_code == 429: |
|
return jsonify(response.json()), 429 |
|
|
|
response.raise_for_status() |
|
end_time = time.time() |
|
response_json = response.json() |
|
total_time = end_time - start_time |
|
|
|
try: |
|
prompt_tokens = response_json["usage"]["prompt_tokens"] |
|
embedding_data = response_json["data"] |
|
except (KeyError, ValueError, IndexError) as e: |
|
logging.error( |
|
f"解析响应 JSON 失败: {e}, " |
|
f"完整内容: {response_json}" |
|
) |
|
prompt_tokens = 0 |
|
embedding_data = [] |
|
|
|
logging.info( |
|
f"使用的key: {api_key}, " |
|
f"提示token: {prompt_tokens}, " |
|
f"总共用时: {total_time:.4f}秒, " |
|
f"使用的模型: {model_name}" |
|
) |
|
|
|
with data_lock: |
|
request_timestamps.append(time.time()) |
|
token_counts.append(prompt_tokens) |
|
request_timestamps_day.append(time.time()) |
|
token_counts_day.append(prompt_tokens) |
|
|
|
return jsonify({ |
|
"object": "list", |
|
"data": embedding_data, |
|
"model": model_name, |
|
"usage": { |
|
"prompt_tokens": prompt_tokens, |
|
"total_tokens": prompt_tokens |
|
} |
|
}) |
|
|
|
except requests.exceptions.RequestException as e: |
|
return jsonify({"error": str(e)}), 500 |
|
|
|
@app.route('/handsome/v1/images/generations', methods=['POST']) |
|
def handsome_images_generations(): |
|
if not check_authorization(request): |
|
return jsonify({"error": "Unauthorized"}), 401 |
|
|
|
data = request.get_json() |
|
if not data or 'model' not in data: |
|
return jsonify({"error": "Invalid request data"}), 400 |
|
if data['model'] not in image_models: |
|
return jsonify({"error": "Invalid model"}), 400 |
|
|
|
model_name = data.get('model') |
|
|
|
request_type = determine_request_type( |
|
model_name, |
|
image_models, |
|
free_image_models |
|
) |
|
|
|
api_key = select_key(request_type, model_name) |
|
|
|
if not api_key: |
|
return jsonify({"error": ("No available API key for this request type or all keys have reached their limits")}), 429 |
|
|
|
headers = { |
|
"Authorization": f"Bearer {api_key}", |
|
"Content-Type": "application/json" |
|
} |
|
|
|
response_data = {} |
|
|
|
if "stable-diffusion" in model_name or model_name in ["black-forest-labs/FLUX.1-schnell", "Pro/black-forest-labs/FLUX.1-schnell","black-forest-labs/FLUX.1-dev", "black-forest-labs/FLUX.1-pro"]: |
|
siliconflow_data = { |
|
"model": model_name, |
|
"prompt": data.get("prompt"), |
|
|
|
} |
|
|
|
if model_name == "black-forest-labs/FLUX.1-pro": |
|
siliconflow_data["width"] = data.get("width", 1024) |
|
siliconflow_data["height"] = data.get("height", 768) |
|
siliconflow_data["prompt_upsampling"] = data.get("prompt_upsampling", False) |
|
siliconflow_data["image_prompt"] = data.get("image_prompt") |
|
siliconflow_data["steps"] = data.get("steps", 20) |
|
siliconflow_data["guidance"] = data.get("guidance", 3) |
|
siliconflow_data["safety_tolerance"] = data.get("safety_tolerance", 2) |
|
siliconflow_data["interval"] = data.get("interval", 2) |
|
siliconflow_data["output_format"] = data.get("output_format", "png") |
|
seed = data.get("seed") |
|
if isinstance(seed, int) and 0 < seed < 9999999999: |
|
siliconflow_data["seed"] = seed |
|
|
|
if siliconflow_data["width"] < 256 or siliconflow_data["width"] > 1440 or siliconflow_data["width"] % 32 != 0: |
|
siliconflow_data["width"] = 1024 |
|
if siliconflow_data["height"] < 256 or siliconflow_data["height"] > 1440 or siliconflow_data["height"] % 32 != 0: |
|
siliconflow_data["height"] = 768 |
|
|
|
if siliconflow_data["steps"] < 1 or siliconflow_data["steps"] > 50: |
|
siliconflow_data["steps"] = 20 |
|
if siliconflow_data["guidance"] < 1.5 or siliconflow_data["guidance"] > 5: |
|
siliconflow_data["guidance"] = 3 |
|
if siliconflow_data["safety_tolerance"] < 0 or siliconflow_data["safety_tolerance"] > 6: |
|
siliconflow_data["safety_tolerance"] = 2 |
|
if siliconflow_data["interval"] < 1 or siliconflow_data["interval"] > 4 : |
|
siliconflow_data["interval"] = 2 |
|
else: |
|
siliconflow_data["image_size"] = data.get("image_size", "1024x1024") |
|
siliconflow_data["prompt_enhancement"] = data.get("prompt_enhancement", False) |
|
seed = data.get("seed") |
|
if isinstance(seed, int) and 0 < seed < 9999999999: |
|
siliconflow_data["seed"] = seed |
|
|
|
if model_name not in ["black-forest-labs/FLUX.1-schnell", "Pro/black-forest-labs/FLUX.1-schnell"]: |
|
siliconflow_data["batch_size"] = data.get("n", 1) |
|
siliconflow_data["num_inference_steps"] = data.get("steps", 20) |
|
siliconflow_data["guidance_scale"] = data.get("guidance_scale", 7.5) |
|
siliconflow_data["negative_prompt"] = data.get("negative_prompt") |
|
if siliconflow_data["batch_size"] < 1: |
|
siliconflow_data["batch_size"] = 1 |
|
if siliconflow_data["batch_size"] > 4: |
|
siliconflow_data["batch_size"] = 4 |
|
|
|
if siliconflow_data["num_inference_steps"] < 1: |
|
siliconflow_data["num_inference_steps"] = 1 |
|
if siliconflow_data["num_inference_steps"] > 50: |
|
siliconflow_data["num_inference_steps"] = 50 |
|
|
|
if siliconflow_data["guidance_scale"] < 0: |
|
siliconflow_data["guidance_scale"] = 0 |
|
if siliconflow_data["guidance_scale"] > 100: |
|
siliconflow_data["guidance_scale"] = 100 |
|
|
|
if "image_size" in siliconflow_data and siliconflow_data["image_size"] not in ["1024x1024", "512x1024", "768x512", "768x1024", "1024x576", "576x1024","960x1280", "720x1440", "720x1280"]: |
|
siliconflow_data["image_size"] = "1024x1024" |
|
|
|
try: |
|
start_time = time.time() |
|
response = requests.post( |
|
IMAGE_ENDPOINT, |
|
headers=headers, |
|
json=siliconflow_data, |
|
timeout=120 |
|
) |
|
|
|
if response.status_code == 429: |
|
return jsonify(response.json()), 429 |
|
|
|
response.raise_for_status() |
|
end_time = time.time() |
|
response_json = response.json() |
|
total_time = end_time - start_time |
|
|
|
try: |
|
images = response_json.get("images", []) |
|
openai_images = [] |
|
for item in images: |
|
if isinstance(item, dict) and "url" in item: |
|
image_url = item["url"] |
|
print(f"image_url: {image_url}") |
|
if data.get("response_format") == "b64_json": |
|
try: |
|
image_data = requests.get(image_url, stream=True).raw |
|
image = Image.open(image_data) |
|
buffered = io.BytesIO() |
|
image.save(buffered, format="PNG") |
|
img_str = base64.b64encode(buffered.getvalue()).decode() |
|
openai_images.append({"b64_json": img_str}) |
|
except Exception as e: |
|
logging.error(f"图片转base64失败: {e}") |
|
openai_images.append({"url": image_url}) |
|
else: |
|
openai_images.append({"url": image_url}) |
|
else: |
|
logging.error(f"无效的图片数据: {item}") |
|
openai_images.append({"url": item}) |
|
|
|
|
|
response_data = { |
|
"created": int(time.time()), |
|
"data": openai_images |
|
} |
|
except (KeyError, ValueError, IndexError) as e: |
|
logging.error( |
|
f"解析响应 JSON 失败: {e}, " |
|
f"完整内容: {response_json}" |
|
) |
|
response_data = { |
|
"created": int(time.time()), |
|
"data": [] |
|
} |
|
|
|
logging.info( |
|
f"使用的key: {api_key}, " |
|
f"总共用时: {total_time:.4f}秒, " |
|
f"使用的模型: {model_name}" |
|
) |
|
|
|
with data_lock: |
|
request_timestamps.append(time.time()) |
|
token_counts.append(0) |
|
request_timestamps_day.append(time.time()) |
|
token_counts_day.append(0) |
|
|
|
return jsonify(response_data) |
|
|
|
except requests.exceptions.RequestException as e: |
|
logging.error(f"请求转发异常: {e}") |
|
return jsonify({"error": str(e)}), 500 |
|
else: |
|
return jsonify({"error": "Unsupported model"}), 400 |
|
|
|
@app.route('/handsome/v1/chat/completions', methods=['POST']) |
|
def handsome_chat_completions(): |
|
if not check_authorization(request): |
|
return jsonify({"error": "Unauthorized"}), 401 |
|
|
|
data = request.get_json() |
|
if not data or 'model' not in data: |
|
return jsonify({"error": "Invalid request data"}), 400 |
|
if data['model'] not in text_models and data['model'] not in image_models: |
|
return jsonify({"error": "Invalid model"}), 400 |
|
|
|
model_name = data['model'] |
|
|
|
request_type = determine_request_type( |
|
model_name, |
|
text_models + image_models, |
|
free_text_models + free_image_models |
|
) |
|
|
|
api_key = select_key(request_type, model_name) |
|
|
|
if not api_key: |
|
return jsonify( |
|
{ |
|
"error": ( |
|
"No available API key for this " |
|
"request type or all keys have " |
|
"reached their limits" |
|
) |
|
} |
|
), 429 |
|
|
|
headers = { |
|
"Authorization": f"Bearer {api_key}", |
|
"Content-Type": "application/json" |
|
} |
|
|
|
if model_name in image_models: |
|
user_content = "" |
|
messages = data.get("messages", []) |
|
for message in messages: |
|
if message["role"] == "user": |
|
if isinstance(message["content"], str): |
|
user_content += message["content"] + " " |
|
elif isinstance(message["content"], list): |
|
for item in message["content"]: |
|
if ( |
|
isinstance(item, dict) and |
|
item.get("type") == "text" |
|
): |
|
user_content += ( |
|
item.get("text", "") + |
|
" " |
|
) |
|
user_content = user_content.strip() |
|
|
|
siliconflow_data = { |
|
"model": model_name, |
|
"prompt": user_content, |
|
} |
|
|
|
if model_name == "black-forest-labs/FLUX.1-pro": |
|
siliconflow_data["width"] = data.get("width", 1024) |
|
siliconflow_data["height"] = data.get("height", 768) |
|
siliconflow_data["prompt_upsampling"] = data.get("prompt_upsampling", False) |
|
siliconflow_data["image_prompt"] = data.get("image_prompt") |
|
siliconflow_data["steps"] = data.get("steps", 20) |
|
siliconflow_data["guidance"] = data.get("guidance", 3) |
|
siliconflow_data["safety_tolerance"] = data.get("safety_tolerance", 2) |
|
siliconflow_data["interval"] = data.get("interval", 2) |
|
siliconflow_data["output_format"] = data.get("output_format", "png") |
|
seed = data.get("seed") |
|
if isinstance(seed, int) and 0 < seed < 9999999999: |
|
siliconflow_data["seed"] = seed |
|
if siliconflow_data["width"] < 256 or siliconflow_data["width"] > 1440 or siliconflow_data["width"] % 32 != 0: |
|
siliconflow_data["width"] = 1024 |
|
if siliconflow_data["height"] < 256 or siliconflow_data["height"] > 1440 or siliconflow_data["height"] % 32 != 0: |
|
siliconflow_data["height"] = 768 |
|
|
|
if siliconflow_data["steps"] < 1 or siliconflow_data["steps"] > 50: |
|
siliconflow_data["steps"] = 20 |
|
if siliconflow_data["guidance"] < 1.5 or siliconflow_data["guidance"] > 5: |
|
siliconflow_data["guidance"] = 3 |
|
if siliconflow_data["safety_tolerance"] < 0 or siliconflow_data["safety_tolerance"] > 6: |
|
siliconflow_data["safety_tolerance"] = 2 |
|
if siliconflow_data["interval"] < 1 or siliconflow_data["interval"] > 4 : |
|
siliconflow_data["interval"] = 2 |
|
else: |
|
siliconflow_data["image_size"] = "1024x1024" |
|
siliconflow_data["batch_size"] = 1 |
|
siliconflow_data["num_inference_steps"] = 20 |
|
siliconflow_data["guidance_scale"] = 7.5 |
|
siliconflow_data["prompt_enhancement"] = False |
|
|
|
if data.get("size"): |
|
siliconflow_data["image_size"] = data.get("size") |
|
if data.get("n"): |
|
siliconflow_data["batch_size"] = data.get("n") |
|
if data.get("steps"): |
|
siliconflow_data["num_inference_steps"] = data.get("steps") |
|
if data.get("guidance_scale"): |
|
siliconflow_data["guidance_scale"] = data.get("guidance_scale") |
|
if data.get("negative_prompt"): |
|
siliconflow_data["negative_prompt"] = data.get("negative_prompt") |
|
if data.get("seed"): |
|
siliconflow_data["seed"] = data.get("seed") |
|
if data.get("prompt_enhancement"): |
|
siliconflow_data["prompt_enhancement"] = data.get("prompt_enhancement") |
|
|
|
if siliconflow_data["batch_size"] < 1: |
|
siliconflow_data["batch_size"] = 1 |
|
if siliconflow_data["batch_size"] > 4: |
|
siliconflow_data["batch_size"] = 4 |
|
|
|
if siliconflow_data["num_inference_steps"] < 1: |
|
siliconflow_data["num_inference_steps"] = 1 |
|
if siliconflow_data["num_inference_steps"] > 50: |
|
siliconflow_data["num_inference_steps"] = 50 |
|
|
|
if siliconflow_data["guidance_scale"] < 0: |
|
siliconflow_data["guidance_scale"] = 0 |
|
if siliconflow_data["guidance_scale"] > 100: |
|
siliconflow_data["guidance_scale"] = 100 |
|
|
|
if siliconflow_data["image_size"] not in ["1024x1024", "512x1024", "768x512", "768x1024", "1024x576", "576x1024", "960x1280", "720x1440", "720x1280"]: |
|
siliconflow_data["image_size"] = "1024x1024" |
|
|
|
try: |
|
start_time = time.time() |
|
response = requests.post( |
|
IMAGE_ENDPOINT, |
|
headers=headers, |
|
json=siliconflow_data, |
|
timeout=120, |
|
stream=data.get("stream", False) |
|
) |
|
|
|
if response.status_code == 429: |
|
return jsonify(response.json()), 429 |
|
|
|
if data.get("stream", False): |
|
def generate(): |
|
try: |
|
response.raise_for_status() |
|
response_json = response.json() |
|
|
|
images = response_json.get("images", []) |
|
|
|
image_url = "" |
|
if images and isinstance(images[0], dict) and "url" in images[0]: |
|
image_url = images[0]["url"] |
|
logging.info(f"Extracted image URL: {image_url}") |
|
elif images and isinstance(images[0], str): |
|
image_url = images[0] |
|
logging.info(f"Extracted image URL: {image_url}") |
|
|
|
markdown_image_link = create_base64_markdown_image(image_url) |
|
if image_url: |
|
chunk_data = { |
|
"id": f"chatcmpl-{uuid.uuid4()}", |
|
"object": "chat.completion.chunk", |
|
"created": int(time.time()), |
|
"model": model_name, |
|
"choices": [ |
|
{ |
|
"index": 0, |
|
"delta": { |
|
"role": "assistant", |
|
"content": markdown_image_link |
|
}, |
|
"finish_reason": None |
|
} |
|
] |
|
} |
|
yield f"data: {json.dumps(chunk_data)}\n\n".encode('utf-8') |
|
else: |
|
chunk_data = { |
|
"id": f"chatcmpl-{uuid.uuid4()}", |
|
"object": "chat.completion.chunk", |
|
"created": int(time.time()), |
|
"model": model_name, |
|
"choices": [ |
|
{ |
|
"index": 0, |
|
"delta": { |
|
"role": "assistant", |
|
"content": "Failed to generate image" |
|
}, |
|
"finish_reason": None |
|
} |
|
] |
|
} |
|
yield f"data: {json.dumps(chunk_data)}\n\n".encode('utf-8') |
|
|
|
end_chunk_data = { |
|
"id": f"chatcmpl-{uuid.uuid4()}", |
|
"object": "chat.completion.chunk", |
|
"created": int(time.time()), |
|
"model": model_name, |
|
"choices": [ |
|
{ |
|
"index": 0, |
|
"delta": {}, |
|
"finish_reason": "stop" |
|
} |
|
] |
|
} |
|
yield f"data: {json.dumps(end_chunk_data)}\n\n".encode('utf-8') |
|
with data_lock: |
|
request_timestamps.append(time.time()) |
|
token_counts.append(0) |
|
request_timestamps_day.append(time.time()) |
|
token_counts_day.append(0) |
|
except requests.exceptions.RequestException as e: |
|
logging.error(f"请求转发异常: {e}") |
|
error_chunk_data = { |
|
"id": f"chatcmpl-{uuid.uuid4()}", |
|
"object": "chat.completion.chunk", |
|
"created": int(time.time()), |
|
"model": model_name, |
|
"choices": [ |
|
{ |
|
"index": 0, |
|
"delta": { |
|
"role": "assistant", |
|
"content": f"Error: {str(e)}" |
|
}, |
|
"finish_reason": None |
|
} |
|
] |
|
} |
|
yield f"data: {json.dumps(error_chunk_data)}\n\n".encode('utf-8') |
|
end_chunk_data = { |
|
"id": f"chatcmpl-{uuid.uuid4()}", |
|
"object": "chat.completion.chunk", |
|
"created": int(time.time()), |
|
"model": model_name, |
|
"choices": [ |
|
{ |
|
"index": 0, |
|
"delta": {}, |
|
"finish_reason": "stop" |
|
} |
|
] |
|
} |
|
yield f"data: {json.dumps(end_chunk_data)}\n\n".encode('utf-8') |
|
logging.info( |
|
f"使用的key: {api_key}, " |
|
f"使用的模型: {model_name}" |
|
) |
|
yield "data: [DONE]\n\n".encode('utf-8') |
|
return Response(stream_with_context(generate()), content_type='text/event-stream') |
|
|
|
else: |
|
response.raise_for_status() |
|
end_time = time.time() |
|
response_json = response.json() |
|
total_time = end_time - start_time |
|
|
|
try: |
|
images = response_json.get("images", []) |
|
|
|
image_url = "" |
|
if images and isinstance(images[0], dict) and "url" in images[0]: |
|
image_url = images[0]["url"] |
|
logging.info(f"Extracted image URL: {image_url}") |
|
elif images and isinstance(images[0], str): |
|
image_url = images[0] |
|
logging.info(f"Extracted image URL: {image_url}") |
|
|
|
markdown_image_link = f"" |
|
response_data = { |
|
"id": f"chatcmpl-{uuid.uuid4()}", |
|
"object": "chat.completion", |
|
"created": int(time.time()), |
|
"model": model_name, |
|
"choices": [ |
|
{ |
|
"index": 0, |
|
"message": { |
|
"role": "assistant", |
|
"content": markdown_image_link if image_url else "Failed to generate image", |
|
}, |
|
"finish_reason": "stop", |
|
} |
|
], |
|
} |
|
except (KeyError, ValueError, IndexError) as e: |
|
logging.error( |
|
f"解析响应 JSON 失败: {e}, " |
|
f"完整内容: {response_json}" |
|
) |
|
response_data = { |
|
"id": f"chatcmpl-{uuid.uuid4()}", |
|
"object": "chat.completion", |
|
"created": int(time.time()), |
|
"model": model_name, |
|
"choices": [ |
|
{ |
|
"index": 0, |
|
"message": { |
|
"role": "assistant", |
|
"content": "Failed to process image data", |
|
}, |
|
"finish_reason": "stop", |
|
} |
|
], |
|
} |
|
|
|
logging.info( |
|
f"使用的key: {api_key}, " |
|
f"总共用时: {total_time:.4f}秒, " |
|
f"使用的模型: {model_name}" |
|
) |
|
with data_lock: |
|
request_timestamps.append(time.time()) |
|
token_counts.append(0) |
|
request_timestamps_day.append(time.time()) |
|
token_counts_day.append(0) |
|
return jsonify(response_data) |
|
|
|
except requests.exceptions.RequestException as e: |
|
logging.error(f"请求转发异常: {e}") |
|
return jsonify({"error": str(e)}), 500 |
|
else: |
|
try: |
|
start_time = time.time() |
|
response = requests.post( |
|
TEST_MODEL_ENDPOINT, |
|
headers=headers, |
|
json=data, |
|
stream=data.get("stream", False), |
|
timeout=60 |
|
) |
|
|
|
if response.status_code == 429: |
|
return jsonify(response.json()), 429 |
|
|
|
if data.get("stream", False): |
|
def generate(): |
|
first_chunk_time = None |
|
full_response_content = "" |
|
for chunk in response.iter_content(chunk_size=2048): |
|
if chunk: |
|
if first_chunk_time is None: |
|
first_chunk_time = time.time() |
|
full_response_content += chunk.decode("utf-8") |
|
yield chunk |
|
|
|
end_time = time.time() |
|
first_token_time = ( |
|
first_chunk_time - start_time |
|
if first_chunk_time else 0 |
|
) |
|
total_time = end_time - start_time |
|
|
|
prompt_tokens = 0 |
|
completion_tokens = 0 |
|
response_content = "" |
|
for line in full_response_content.splitlines(): |
|
if line.startswith("data:"): |
|
line = line[5:].strip() |
|
if line == "[DONE]": |
|
continue |
|
try: |
|
response_json = json.loads(line) |
|
|
|
if ( |
|
"usage" in response_json and |
|
"completion_tokens" in response_json["usage"] |
|
): |
|
completion_tokens = response_json[ |
|
"usage" |
|
]["completion_tokens"] |
|
|
|
if ( |
|
"choices" in response_json and |
|
len(response_json["choices"]) > 0 and |
|
"delta" in response_json["choices"][0] and |
|
"content" in response_json[ |
|
"choices" |
|
][0]["delta"] |
|
): |
|
response_content += response_json[ |
|
"choices" |
|
][0]["delta"]["content"] |
|
|
|
if ( |
|
"usage" in response_json and |
|
"prompt_tokens" in response_json["usage"] |
|
): |
|
prompt_tokens = response_json[ |
|
"usage" |
|
]["prompt_tokens"] |
|
|
|
except ( |
|
KeyError, |
|
ValueError, |
|
IndexError |
|
) as e: |
|
logging.error( |
|
f"解析流式响应单行 JSON 失败: {e}, " |
|
f"行内容: {line}" |
|
) |
|
|
|
user_content = "" |
|
messages = data.get("messages", []) |
|
for message in messages: |
|
if message["role"] == "user": |
|
if isinstance(message["content"], str): |
|
user_content += message["content"] + " " |
|
elif isinstance(message["content"], list): |
|
for item in message["content"]: |
|
if ( |
|
isinstance(item, dict) and |
|
item.get("type") == "text" |
|
): |
|
user_content += ( |
|
item.get("text", "") + |
|
" " |
|
) |
|
|
|
user_content = user_content.strip() |
|
|
|
user_content_replaced = user_content.replace( |
|
'\n', '\\n' |
|
).replace('\r', '\\n') |
|
response_content_replaced = response_content.replace( |
|
'\n', '\\n' |
|
).replace('\r', '\\n') |
|
|
|
logging.info( |
|
f"使用的key: {api_key}, " |
|
f"提示token: {prompt_tokens}, " |
|
f"输出token: {completion_tokens}, " |
|
f"首字用时: {first_token_time:.4f}秒, " |
|
f"总共用时: {total_time:.4f}秒, " |
|
f"使用的模型: {model_name}, " |
|
f"用户的内容: {user_content_replaced}, " |
|
f"输出的内容: {response_content_replaced}" |
|
) |
|
|
|
with data_lock: |
|
request_timestamps.append(time.time()) |
|
token_counts.append(prompt_tokens+completion_tokens) |
|
request_timestamps_day.append(time.time()) |
|
token_counts_day.append(prompt_tokens+completion_tokens) |
|
|
|
return Response( |
|
stream_with_context(generate()), |
|
content_type=response.headers['Content-Type'] |
|
) |
|
else: |
|
response.raise_for_status() |
|
end_time = time.time() |
|
response_json = response.json() |
|
total_time = end_time - start_time |
|
|
|
try: |
|
prompt_tokens = response_json["usage"]["prompt_tokens"] |
|
completion_tokens = response_json[ |
|
"usage" |
|
]["completion_tokens"] |
|
response_content = response_json[ |
|
"choices" |
|
][0]["message"]["content"] |
|
except (KeyError, ValueError, IndexError) as e: |
|
logging.error( |
|
f"解析非流式响应 JSON 失败: {e}, " |
|
f"完整内容: {response_json}" |
|
) |
|
prompt_tokens = 0 |
|
completion_tokens = 0 |
|
response_content = "" |
|
|
|
user_content = "" |
|
messages = data.get("messages", []) |
|
for message in messages: |
|
if message["role"] == "user": |
|
if isinstance(message["content"], str): |
|
user_content += message["content"] + " " |
|
elif isinstance(message["content"], list): |
|
for item in message["content"]: |
|
if ( |
|
isinstance(item, dict) and |
|
item.get("type") == "text" |
|
): |
|
user_content += ( |
|
item.get("text", "") + " " |
|
) |
|
|
|
user_content = user_content.strip() |
|
|
|
user_content_replaced = user_content.replace( |
|
'\n', '\\n' |
|
).replace('\r', '\\n') |
|
response_content_replaced = response_content.replace( |
|
'\n', '\\n' |
|
).replace('\r', '\\n') |
|
|
|
logging.info( |
|
f"使用的key: {api_key}, " |
|
f"提示token: {prompt_tokens}, " |
|
f"输出token: {completion_tokens}, " |
|
f"首字用时: 0, " |
|
f"总共用时: {total_time:.4f}秒, " |
|
f"使用的模型: {model_name}, " |
|
f"用户的内容: {user_content_replaced}, " |
|
f"输出的内容: {response_content_replaced}" |
|
) |
|
with data_lock: |
|
request_timestamps.append(time.time()) |
|
if "prompt_tokens" in response_json["usage"] and "completion_tokens" in response_json["usage"]: |
|
token_counts.append(response_json["usage"]["prompt_tokens"] + response_json["usage"]["completion_tokens"]) |
|
else: |
|
token_counts.append(0) |
|
request_timestamps_day.append(time.time()) |
|
if "prompt_tokens" in response_json["usage"] and "completion_tokens" in response_json["usage"]: |
|
token_counts_day.append(response_json["usage"]["prompt_tokens"] + response_json["usage"]["completion_tokens"]) |
|
else: |
|
token_counts_day.append(0) |
|
|
|
return jsonify(response_json) |
|
|
|
except requests.exceptions.RequestException as e: |
|
logging.error(f"请求转发异常: {e}") |
|
return jsonify({"error": str(e)}), 500 |
|
|
|
if __name__ == '__main__': |
|
logging.info(f"环境变量：{os.environ}") |
|
|
|
load_keys() |
|
logging.info("程序启动时首次加载 keys 已执行") |
|
|
|
scheduler.start() |
|
|
|
logging.info("首次加载 keys 已手动触发执行") |
|
|
|
refresh_models() |
|
logging.info("首次刷新模型列表已手动触发执行") |
|
|
|
app.run( |
|
debug=False, |
|
host='0.0.0.0', |
|
port=int(os.environ.get('PORT', 7860)) |
|
) |
