Upload 7 files

.env

@@ -0,0 +1,3 @@
+API_KEYS=["ccc"]
+ALLOWED_TOKENS=["xxx"]
+BASE_URL=https://api.groq.com/openai/v1

.gitattributes

@@ -1,35 +1,35 @@
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-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
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+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
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+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
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Dockerfile

@@ -1,19 +1,19 @@
-FROM python:3.9-slim
-
-WORKDIR /app
-
-# 复制所需文件到容器中
-COPY ./app /app/app
-COPY ./main.py /app
-COPY ./requirements.txt /app
-
-RUN pip install --no-cache-dir -r requirements.txt
-ENV API_KEYS=["your_api_key_1"]
-ENV ALLOWED_TOKENS=["your_token_1"]
-ENV BASE_URL=https://api.groq.com/openai/v1
-
-# Expose port
-EXPOSE 8000
-
-# Run the application
-CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]
+FROM python:3.9-slim
+
+WORKDIR /app
+
+# 复制所需文件到容器中
+COPY ./app /app/app
+COPY ./main.py /app
+COPY ./requirements.txt /app
+
+RUN pip install --no-cache-dir -r requirements.txt
+ENV API_KEYS=["your_api_key_1"]
+ENV ALLOWED_TOKENS=["your_token_1"]
+ENV BASE_URL=https://api.groq.com/openai/v1
+
+# Expose port
+EXPOSE 8000
+
+# Run the application
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

README.md

@@ -1,12 +1,12 @@
----
-title: Gemini Balance
-emoji: 🐨
-colorFrom: red
-colorTo: indigo
-sdk: docker
-pinned: false
-license: mit
-app_port: 8000
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Gemini Balance
+emoji: 🐨
+colorFrom: red
+colorTo: indigo
+sdk: docker
+pinned: false
+license: mit
+app_port: 8000
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app/config.py

@@ -6,7 +6,8 @@ class Settings(BaseSettings):
     API_KEYS: List[str]
     ALLOWED_TOKENS: List[str]
     BASE_URL: str
-    
+    MODEL_SEARCH: List[str] = ["gemini-2.0-flash-exp"]
+
     class Config:
         env_file = ".env"
         env_file_encoding = "utf-8"

main.py

@@ -1,163 +1,378 @@
-from fastapi import FastAPI, HTTPException, Header, Request
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import StreamingResponse
-from pydantic import BaseModel
-import openai
-from typing import List, Optional
-import logging
-from itertools import cycle
-import asyncio
-
-import uvicorn
-
-from app import config
-import requests
-from datetime import datetime, timezone
-
-# 配置日志
-logging.basicConfig(
-    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
-)
-logger = logging.getLogger(__name__)
-
-app = FastAPI()
-
-# 允许跨域
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-# API密钥配置
-API_KEYS = config.settings.API_KEYS
-
-# 创建一个循环迭代器
-key_cycle = cycle(API_KEYS)
-key_lock = asyncio.Lock()
-
-
-class ChatRequest(BaseModel):
-    messages: List[dict]
-    model: str = "llama-3.2-90b-text-preview"
-    temperature: Optional[float] = 0.7
-    stream: Optional[bool] = False
-
-
-async def verify_authorization(authorization: str = Header(None)):
-    if not authorization:
-        logger.error("Missing Authorization header")
-        raise HTTPException(status_code=401, detail="Missing Authorization header")
-    if not authorization.startswith("Bearer "):
-        logger.error("Invalid Authorization header format")
-        raise HTTPException(
-            status_code=401, detail="Invalid Authorization header format"
-        )
-    token = authorization.replace("Bearer ", "")
-    if token not in config.settings.ALLOWED_TOKENS:
-        logger.error("Invalid token")
-        raise HTTPException(status_code=401, detail="Invalid token")
-    return token
-
-
-def get_gemini_models(api_key):
-    base_url = "https://generativelanguage.googleapis.com/v1beta"
-    url = f"{base_url}/models?key={api_key}"
-    
-    try:
-        response = requests.get(url)
-        if response.status_code == 200:
-            gemini_models = response.json()
-            return convert_to_openai_format(gemini_models)
-        else:
-            print(f"Error: {response.status_code}")
-            print(response.text)
-            return None
-    
-    except requests.RequestException as e:
-        print(f"Request failed: {e}")
-        return None
-
-def convert_to_openai_format(gemini_models):
-    openai_format = {
-        "object": "list",
-        "data": []
-    }
-    
-    for model in gemini_models.get('models', []):
-        openai_model = {
-            "id": model['name'].split('/')[-1],  # 取最后一部分作为ID
-            "object": "model",
-            "created": int(datetime.now(timezone.utc).timestamp()),  # 使用当前时间戳
-            "owned_by": "google",  # 假设所有Gemini模型都由Google拥有
-            "permission": [],  # Gemini API可能没有直接对应的权限信息
-            "root": model['name'],
-            "parent": None,  # Gemini API可能没有直接对应的父模型信息
-        }
-        openai_format["data"].append(openai_model)
-    
-    return openai_format
-        
-
-@app.get("/v1/models")
-@app.get("/hf/v1/models")
-async def list_models(authorization: str = Header(None)):
-    await verify_authorization(authorization)
-    async with key_lock:
-        api_key = next(key_cycle)
-        logger.info(f"Using API key: {api_key}")
-    try:
-        response = get_gemini_models(api_key)
-        logger.info("Successfully retrieved models list")
-        return response
-    except Exception as e:
-        logger.error(f"Error listing models: {str(e)}")
-        raise HTTPException(status_code=500, detail=str(e))
-
-
-@app.post("/v1/chat/completions")
-@app.post("/hf/v1/chat/completions")
-async def chat_completion(request: ChatRequest, authorization: str = Header(None)):
-    await verify_authorization(authorization)
-    async with key_lock:
-        api_key = next(key_cycle)
-        logger.info(f"Using API key: {api_key}")
-
-    try:
-        logger.info(f"Chat completion request - Model: {request.model}")
-        client = openai.OpenAI(api_key=api_key, base_url=config.settings.BASE_URL)
-        response = client.chat.completions.create(
-            model=request.model,
-            messages=request.messages,
-            temperature=request.temperature,
-            stream=request.stream if hasattr(request, "stream") else False,
-        )
-
-        if hasattr(request, "stream") and request.stream:
-            logger.info("Streaming response enabled")
-
-            async def generate():
-                for chunk in response:
-                    yield f"data: {chunk.model_dump_json()}\n\n"
-
-            return StreamingResponse(content=generate(), media_type="text/event-stream")
-
-        logger.info("Chat completion successful")
-        return response
-
-    except Exception as e:
-        logger.error(f"Error in chat completion: {str(e)}")
-        raise HTTPException(status_code=500, detail=str(e))
-
-
-@app.get("/health")
-@app.get("/")
-async def health_check():
-    logger.info("Health check endpoint called")
-    return {"status": "healthy"}
-
-
-if __name__ == "__main__":
+from fastapi import FastAPI, HTTPException, Header
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel
+import openai
+from typing import List, Optional, Union
+import logging
+from itertools import cycle
+import asyncio
+
+import uvicorn
+
+from app import config
+import requests
+from datetime import datetime, timezone
+import json
+import httpx
+import uuid
+import time
+
+# 配置日志
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
+)
+logger = logging.getLogger(__name__)
+
+app = FastAPI()
+
+# 允许跨域
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# API密钥配置
+API_KEYS = config.settings.API_KEYS
+
+# 创建一个循环迭代器
+key_cycle = cycle(API_KEYS)
+
+# 创建两个独立的锁
+key_cycle_lock = asyncio.Lock()
+failure_count_lock = asyncio.Lock()
+
+# 添加key失败计数记录
+key_failure_counts = {key: 0 for key in API_KEYS}
+MAX_FAILURES = 10  # 最大失败次数阈值
+MAX_RETRIES = 3  # 最大重试次数
+
+
+async def get_next_key():
+    """仅获取下一个key,不检查失败次数"""
+    async with key_cycle_lock:
+        return next(key_cycle)
+
+async def is_key_valid(key):
+    """检查key是否有效"""
+    async with failure_count_lock:
+        return key_failure_counts[key] < MAX_FAILURES
+
+async def reset_failure_counts():
+    """重置所有key的失败计数"""
+    async with failure_count_lock:
+        for key in key_failure_counts:
+            key_failure_counts[key] = 0
+
+async def get_next_working_key():
+    """获取下一个可用的API key"""
+    initial_key = await get_next_key()
+    current_key = initial_key
+    
+    while True:
+        if await is_key_valid(current_key):
+            return current_key
+            
+        current_key = await get_next_key()
+        if current_key == initial_key:  # 已经循环了一圈
+            await reset_failure_counts()
+            return current_key
+
+async def handle_api_failure(api_key):
+    """处理API调用失败"""
+    async with failure_count_lock:
+        key_failure_counts[api_key] += 1
+        if key_failure_counts[api_key] >= MAX_FAILURES:
+            logger.warning(f"API key {api_key} has failed {MAX_FAILURES} times, switching to next key")
+    
+    # 在锁外获取新的key
+    return await get_next_working_key()
+
+
+class ChatRequest(BaseModel):
+    messages: List[dict]
+    model: str = "gemini-1.5-flash-002"
+    temperature: Optional[float] = 0.7
+    stream: Optional[bool] = False
+    tools: Optional[List[dict]] = []
+    tool_choice: Optional[str] = "auto"
+
+
+class EmbeddingRequest(BaseModel):
+    input: Union[str, List[str]]
+    model: str = "text-embedding-004"
+    encoding_format: Optional[str] = "float"
+
+
+async def verify_authorization(authorization: str = Header(None)):
+    if not authorization:
+        logger.error("Missing Authorization header")
+        raise HTTPException(status_code=401, detail="Missing Authorization header")
+    if not authorization.startswith("Bearer "):
+        logger.error("Invalid Authorization header format")
+        raise HTTPException(
+            status_code=401, detail="Invalid Authorization header format"
+        )
+    token = authorization.replace("Bearer ", "")
+    if token not in config.settings.ALLOWED_TOKENS:
+        logger.error("Invalid token")
+        raise HTTPException(status_code=401, detail="Invalid token")
+    return token
+
+
+def get_gemini_models(api_key):
+    base_url = "https://generativelanguage.googleapis.com/v1beta"
+    url = f"{base_url}/models?key={api_key}"
+
+    try:
+        response = requests.get(url)
+        if response.status_code == 200:
+            gemini_models = response.json()
+            return convert_to_openai_models_format(gemini_models)
+        else:
+            print(f"Error: {response.status_code}")
+            print(response.text)
+            return None
+
+    except requests.RequestException as e:
+        print(f"Request failed: {e}")
+        return None
+
+
+def convert_to_openai_models_format(gemini_models):
+    openai_format = {"object": "list", "data": []}
+
+    for model in gemini_models.get("models", []):
+        openai_model = {
+            "id": model["name"].split("/")[-1],  # 取最后一部分作为ID
+            "object": "model",
+            "created": int(datetime.now(timezone.utc).timestamp()),  # 使用当前时间戳
+            "owned_by": "google",  # 假设所有Gemini模型都由Google拥有
+            "permission": [],  # Gemini API可能没有直接对应的权限信息
+            "root": model["name"],
+            "parent": None,  # Gemini API可能没有直接对应的父模型信息
+        }
+        openai_format["data"].append(openai_model)
+
+    return openai_format
+
+
+def convert_messages_to_gemini_format(messages):
+    """Convert OpenAI message format to Gemini format"""
+    gemini_messages = []
+    for message in messages:
+        gemini_message = {
+            "role": "user" if message["role"] == "user" else "model",
+            "parts": [{"text": message["content"]}],
+        }
+        gemini_messages.append(gemini_message)
+    return gemini_messages
+
+
+def convert_gemini_response_to_openai(response, model, stream=False):
+    """Convert Gemini response to OpenAI format"""
+    if stream:
+        # 处理流式响应
+        chunk = response
+        if not chunk["candidates"]:
+            return None
+
+        return {
+            "id": "chatcmpl-" + str(uuid.uuid4()),
+            "object": "chat.completion.chunk",
+            "created": int(time.time()),
+            "model": model,
+            "choices": [
+                {
+                    "index": 0,
+                    "delta": {
+                        "content": chunk["candidates"][0]["content"]["parts"][0]["text"]
+                    },
+                    "finish_reason": None,
+                }
+            ],
+        }
+    else:
+        # 处理普通响应
+        return {
+            "id": "chatcmpl-" + str(uuid.uuid4()),
+            "object": "chat.completion",
+            "created": int(time.time()),
+            "model": model,
+            "choices": [
+                {
+                    "index": 0,
+                    "message": {
+                        "role": "assistant",
+                        "content": response["candidates"][0]["content"]["parts"][0][
+                            "text"
+                        ],
+                    },
+                    "finish_reason": "stop",
+                }
+            ],
+            "usage": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0},
+        }
+
+
+@app.get("/v1/models")
+@app.get("/hf/v1/models")
+async def list_models(authorization: str = Header(None)):
+    await verify_authorization(authorization)
+    api_key = await get_next_working_key()
+    logger.info(f"Using API key: {api_key}")
+    try:
+        response = get_gemini_models(api_key)
+        logger.info("Successfully retrieved models list")
+        return response
+    except Exception as e:
+        logger.error(f"Error listing models: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/v1/chat/completions")
+@app.post("/hf/v1/chat/completions")
+async def chat_completion(request: ChatRequest, authorization: str = Header(None)):
+    await verify_authorization(authorization)
+    api_key = await get_next_working_key()
+    logger.info(f"Chat completion request - Model: {request.model}")
+    retries = 0
+    
+    while retries < MAX_RETRIES:
+        try:
+            logger.info(f"Attempt {retries + 1} with API key: {api_key}")
+            
+            if request.model in config.settings.MODEL_SEARCH:
+                # Gemini API调用部分
+                gemini_messages = convert_messages_to_gemini_format(request.messages)
+                # 调用Gemini API
+                payload = {
+                    "contents": gemini_messages,
+                    "generationConfig": {
+                        "temperature": request.temperature,
+                    },
+                    "tools": [{"googleSearch": {}}],
+                }
+                
+                if request.stream:
+                    logger.info("Streaming response enabled")
+
+                    async def generate():
+                        nonlocal api_key, retries
+                        while retries < MAX_RETRIES:
+                            try:
+                                async with httpx.AsyncClient() as client:
+                                    stream_url = f"https://generativelanguage.googleapis.com/v1beta/models/{request.model}:streamGenerateContent?alt=sse&key={api_key}"
+                                    async with client.stream("POST", stream_url, json=payload) as response:
+                                        if response.status_code == 429:
+                                            logger.warning(f"Rate limit reached for key: {api_key}")
+                                            api_key = await handle_api_failure(api_key)
+                                            logger.info(f"Retrying with new API key: {api_key}")
+                                            retries += 1
+                                            if retries >= MAX_RETRIES:
+                                                yield f"data: {json.dumps({'error': 'Max retries reached'})}\n\n"
+                                                break
+                                            continue
+                                            
+                                        if response.status_code != 200:
+                                            logger.error(f"Error in streaming response: {response.status_code}")
+                                            yield f"data: {json.dumps({'error': f'API error: {response.status_code}'})}\n\n"
+                                            break
+                                            
+                                        async for line in response.aiter_lines():
+                                            if line.startswith("data: "):
+                                                try:
+                                                    chunk = json.loads(line[6:])
+                                                    openai_chunk = convert_gemini_response_to_openai(
+                                                        chunk, request.model, stream=True
+                                                    )
+                                                    if openai_chunk:
+                                                        yield f"data: {json.dumps(openai_chunk)}\n\n"
+                                                except json.JSONDecodeError:
+                                                    continue
+                                        yield "data: [DONE]\n\n"
+                                        return
+                            except Exception as e:
+                                logger.error(f"Stream error: {str(e)}")
+                                api_key = await handle_api_failure(api_key)
+                                retries += 1
+                                if retries >= MAX_RETRIES:
+                                    yield f"data: {json.dumps({'error': 'Max retries reached'})}\n\n"
+                                    break
+                                continue
+                                
+                    return StreamingResponse(content=generate(), media_type="text/event-stream")
+                else:
+                    # 非流式响应
+                    async with httpx.AsyncClient() as client:
+                        non_stream_url = f"https://generativelanguage.googleapis.com/v1beta/models/{request.model}:generateContent?key={api_key}"
+                        response = await client.post(non_stream_url, json=payload)
+                        gemini_response = response.json()
+                        logger.info("Chat completion successful")
+                        return convert_gemini_response_to_openai(gemini_response, request.model)
+            
+            # OpenAI API调用部分
+            client = openai.OpenAI(api_key=api_key, base_url=config.settings.BASE_URL)
+            response = client.chat.completions.create(
+                model=request.model,
+                messages=request.messages,
+                temperature=request.temperature,
+                stream=request.stream if hasattr(request, "stream") else False,
+            )
+            
+            if hasattr(request, "stream") and request.stream:
+                logger.info("Streaming response enabled")
+
+                async def generate():
+                    for chunk in response:
+                        yield f"data: {chunk.model_dump_json()}\n\n"
+                logger.info("Chat completion successful")
+                return StreamingResponse(content=generate(), media_type="text/event-stream")
+            
+            logger.info("Chat completion successful")
+            return response
+
+        except Exception as e:
+            logger.error(f"Error in chat completion: {str(e)}")
+            api_key = await handle_api_failure(api_key)
+            retries += 1
+            
+            if retries >= MAX_RETRIES:
+                logger.error("Max retries reached, giving up")
+                raise HTTPException(status_code=500, detail="Max retries reached with all available API keys")
+            
+            logger.info(f"Retrying with new API key: {api_key}")
+            continue
+
+    raise HTTPException(status_code=500, detail="Unexpected error in chat completion")
+
+
+@app.post("/v1/embeddings")
+@app.post("/hf/v1/embeddings")
+async def embedding(request: EmbeddingRequest, authorization: str = Header(None)):
+    await verify_authorization(authorization)
+    api_key = await get_next_working_key()
+    logger.info(f"Using API key: {api_key}")
+
+    try:
+        client = openai.OpenAI(api_key=api_key, base_url=config.settings.BASE_URL)
+        response = client.embeddings.create(input=request.input, model=request.model)
+        logger.info("Embedding successful")
+        return response
+    except Exception as e:
+        logger.error(f"Error in embedding: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.get("/health")
+@app.get("/")
+async def health_check():
+    logger.info("Health check endpoint called")
+    return {"status": "healthy"}
+
+
+if __name__ == "__main__":
     uvicorn.run(app, host="0.0.0.0", port=8000)

requirements.txt

@@ -1,6 +1,7 @@
-fastapi
-openai
-pydantic
-pydantic_settings
-uvicorn
-requests
+fastapi
+httpx
+openai
+pydantic
+pydantic_settings
+requests
+uvicorn