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.env

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+SECURE_1PSID=g.a000wggDrdz_LVKBo6Z8CvdWxP12JoNF4C4dEYJPGL7uc0KJVfZELwQKSVUPOi_4IHa7olel8AACgYKAWUSARUSFQHGX2MiPxpB3JHYmE2q0wDguZNRHRoVAUF8yKpa6mhrstdOMGpvDIUa_5zp0076
+SECURE_1PSIDTS=sidts-CjEBjplskE4IkRFwPaDjNxMhcCG6ew5OVVhzBdyp_C1yHqZry3rSlMmWf-2HZ_uWklWzEAA

.gitignore

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+.python-version
+.idea
+.venv
+uv.lock
+.env
+__pycache__

Dockerfile

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+FROM ghcr.io/astral-sh/uv:python3.12-bookworm-slim
+
+WORKDIR /app
+
+# Install dependencies
+COPY pyproject.toml .
+RUN uv sync
+
+# Copy application code
+COPY main.py .
+
+# Expose the port the app runs on
+EXPOSE 8000
+
+# Command to run the application
+CMD ["uv", "run", "uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

LICENSE

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+MIT License
+
+Copyright (c) 2025 RrOrange
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,11 +1,100 @@
----
-title: Gemini2api
-emoji: 🔥
-colorFrom: pink
-colorTo: green
-sdk: docker
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Gemi2Api-Server
+[HanaokaYuzu / Gemini-API](https://github.com/HanaokaYuzu/Gemini-API) 的服务端简单实现
+
+[![pE79pPf.png](https://s21.ax1x.com/2025/04/28/pE79pPf.png)](https://imgse.com/i/pE79pPf)
+
+## 快捷部署
+
+### Render
+
+[![Deploy to Render](https://render.com/images/deploy-to-render-button.svg)](https://render.com/deploy?repo=https://github.com/zhiyu1998/Gemi2Api-Server)
+
+## 直接运行
+
+0. 填入 `SECURE_1PSID` 和 `SECURE_1PSIDTS`（登录 Gemini 在浏览器开发工具中查找 Cookie）
+```properties
+SECURE_1PSID = "COOKIE VALUE HERE"
+SECURE_1PSIDTS = "COOKIE VALUE HERE"
+```
+1. `uv` 安装一下依赖
+> uv init
+> 
+> uv add fastapi uvicorn gemini-webapi
+
+> [!NOTE]  
+> 如果存在`pyproject.toml` 那么就使用下面的命令：  
+> uv sync
+
+或者 `pip` 也可以
+
+> pip install fastapi uvicorn gemini-webapi
+
+2. 激活一下环境
+> source venv/bin/activate
+
+3. 启动
+> uvicorn main:app --reload --host 127.0.0.1 --port 8000
+
+> [!WARNING] 
+> tips: 没有任何API Key，直接使用
+
+## 使用Docker运行（推荐）
+
+### 快速开始
+
+1. 克隆本项目
+   ```bash
+   git clone https://github.com/zhiyu1998/Gemi2Api-Server.git
+   ```
+
+2. 创建 `.env` 文件并填入你的 Gemini Cookie 凭据:
+   ```bash
+   cp .env.example .env
+   # 用编辑器打开 .env 文件，填入你的 Cookie 值
+   ```
+
+3. 启动服务:
+   ```bash
+   docker-compose up -d
+   ```
+
+4. 服务将在 http://0.0.0.0:8000 上运行
+
+### 其他 Docker 命令
+
+```bash
+# 查看日志
+docker-compose logs
+
+# 重启服务
+docker-compose restart
+
+# 停止服务
+docker-compose down
+
+# 重新构建并启动
+docker-compose up -d --build
+```
+
+## API端点
+
+- `GET /`: 服务状态检查
+- `GET /v1/models`: 获取可用模型列表
+- `POST /v1/chat/completions`: 与模型聊天 (类似OpenAI接口)
+
+## 常见问题
+
+### 服务器报 500 问题解决方案
+
+500 的问题一般是 IP 不太行 或者 请求太频繁（后者等待一段时间或者重新新建一个隐身标签登录一下重新给 Secure_1PSID 和 Secure_1PSIDTS 即可），见 issue：
+- [__Secure-1PSIDTS · Issue #6 · HanaokaYuzu/Gemini-API](https://github.com/HanaokaYuzu/Gemini-API/issues/6)
+- [Failed to initialize client. SECURE_1PSIDTS could get expired frequently · Issue #72 · HanaokaYuzu/Gemini-API](https://github.com/HanaokaYuzu/Gemini-API/issues/72)
+
+解决步骤：
+1. 使用隐身标签访问 [Google Gemini](https://gemini.google.com/) 并登录
+2. 打开浏览器开发工具 (F12)
+3. 切换到 "Application" 或 "应用程序" 标签
+4. 在左侧找到 "Cookies" > "gemini.google.com"
+5. 复制 `__Secure-1PSID` 和 `__Secure-1PSIDTS` 的值
+6. 更新 `.env` 文件
+7. 重新构建并启动: `docker-compose up -d --build`

main.py

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+import asyncio
+import json
+from datetime import datetime, timezone
+import os
+import base64
+import tempfile
+
+from fastapi import FastAPI, HTTPException, Request
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel
+from typing import List, Optional, Dict, Any, Union
+import time
+import uuid
+import logging
+
+from gemini_webapi import GeminiClient, set_log_level
+from gemini_webapi.constants import Model
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+set_log_level("INFO")
+
+app = FastAPI(title="Gemini API FastAPI Server")
+
+# Add CORS middleware
+app.add_middleware(
+	CORSMiddleware,
+	allow_origins=["*"],
+	allow_credentials=True,
+	allow_methods=["*"],
+	allow_headers=["*"],
+)
+
+# Global client
+gemini_client = None
+
+# Authentication credentials
+SECURE_1PSID = os.environ.get("SECURE_1PSID", "")
+SECURE_1PSIDTS = os.environ.get("SECURE_1PSIDTS", "")
+
+# Print debug info at startup
+if not SECURE_1PSID or not SECURE_1PSIDTS:
+	logger.warning("⚠️ Gemini API credentials are not set or empty! Please check your environment variables.")
+	logger.warning("Make sure SECURE_1PSID and SECURE_1PSIDTS are correctly set in your .env file or environment.")
+	logger.warning("If using Docker, ensure the .env file is correctly mounted and formatted.")
+	logger.warning("Example format in .env file (no quotes):")
+	logger.warning("SECURE_1PSID=your_secure_1psid_value_here")
+	logger.warning("SECURE_1PSIDTS=your_secure_1psidts_value_here")
+else:
+	# Only log the first few characters for security
+	logger.info(f"Credentials found. SECURE_1PSID starts with: {SECURE_1PSID[:5]}...")
+	logger.info(f"Credentials found. SECURE_1PSIDTS starts with: {SECURE_1PSIDTS[:5]}...")
+
+
+# Pydantic models for API requests and responses
+class ContentItem(BaseModel):
+	type: str
+	text: Optional[str] = None
+	image_url: Optional[Dict[str, str]] = None
+
+
+class Message(BaseModel):
+	role: str
+	content: Union[str, List[ContentItem]]
+	name: Optional[str] = None
+
+
+class ChatCompletionRequest(BaseModel):
+	model: str
+	messages: List[Message]
+	temperature: Optional[float] = 0.7
+	top_p: Optional[float] = 1.0
+	n: Optional[int] = 1
+	stream: Optional[bool] = False
+	max_tokens: Optional[int] = None
+	presence_penalty: Optional[float] = 0
+	frequency_penalty: Optional[float] = 0
+	user: Optional[str] = None
+
+
+class Choice(BaseModel):
+	index: int
+	message: Message
+	finish_reason: str
+
+
+class Usage(BaseModel):
+	prompt_tokens: int
+	completion_tokens: int
+	total_tokens: int
+
+
+class ChatCompletionResponse(BaseModel):
+	id: str
+	object: str = "chat.completion"
+	created: int
+	model: str
+	choices: List[Choice]
+	usage: Usage
+
+
+class ModelData(BaseModel):
+	id: str
+	object: str = "model"
+	created: int
+	owned_by: str = "google"
+
+
+class ModelList(BaseModel):
+	object: str = "list"
+	data: List[ModelData]
+
+
+# Simple error handler middleware
+@app.middleware("http")
+async def error_handling(request: Request, call_next):
+	try:
+		return await call_next(request)
+	except Exception as e:
+		logger.error(f"Request failed: {str(e)}")
+		return JSONResponse(status_code=500, content={"error": {"message": str(e), "type": "internal_server_error"}})
+
+
+# Get list of available models
+@app.get("/v1/models")
+async def list_models():
+	"""返回 gemini_webapi 中声明的模型列表"""
+	now = int(datetime.now(tz=timezone.utc).timestamp())
+	data = [
+		{
+			"id": m.model_name,  # 如 "gemini-2.0-flash"
+			"object": "model",
+			"created": now,
+			"owned_by": "google-gemini-web",
+		}
+		for m in Model
+	]
+	print(data)
+	return {"object": "list", "data": data}
+
+
+# Helper to convert between Gemini and OpenAI model names
+def map_model_name(openai_model_name: str) -> Model:
+	"""根据模型名称字符串查找匹配的 Model 枚举值"""
+	# 打印所有可用模型以便调试
+	all_models = [m.model_name if hasattr(m, "model_name") else str(m) for m in Model]
+	logger.info(f"Available models: {all_models}")
+
+	# 首先尝试直接查找匹配的模型名称
+	for m in Model:
+		model_name = m.model_name if hasattr(m, "model_name") else str(m)
+		if openai_model_name.lower() in model_name.lower():
+			return m
+
+	# 如果找不到匹配项，使用默认映射
+	model_keywords = {
+		"gemini-pro": ["pro", "2.0"],
+		"gemini-pro-vision": ["vision", "pro"],
+		"gemini-flash": ["flash", "2.0"],
+		"gemini-1.5-pro": ["1.5", "pro"],
+		"gemini-1.5-flash": ["1.5", "flash"],
+	}
+
+	# 根据关键词匹配
+	keywords = model_keywords.get(openai_model_name, ["pro"])  # 默认使用pro模型
+
+	for m in Model:
+		model_name = m.model_name if hasattr(m, "model_name") else str(m)
+		if all(kw.lower() in model_name.lower() for kw in keywords):
+			return m
+
+	# ��果还是找不到，返回第一个模型
+	return next(iter(Model))
+
+
+# Prepare conversation history from OpenAI messages format
+def prepare_conversation(messages: List[Message]) -> tuple:
+	conversation = ""
+	temp_files = []
+
+	for msg in messages:
+		if isinstance(msg.content, str):
+			# String content handling
+			if msg.role == "system":
+				conversation += f"System: {msg.content}\n\n"
+			elif msg.role == "user":
+				conversation += f"Human: {msg.content}\n\n"
+			elif msg.role == "assistant":
+				conversation += f"Assistant: {msg.content}\n\n"
+		else:
+			# Mixed content handling
+			if msg.role == "user":
+				conversation += "Human: "
+			elif msg.role == "system":
+				conversation += "System: "
+			elif msg.role == "assistant":
+				conversation += "Assistant: "
+
+			for item in msg.content:
+				if item.type == "text":
+					conversation += item.text or ""
+				elif item.type == "image_url" and item.image_url:
+					# Handle image
+					image_url = item.image_url.get("url", "")
+					if image_url.startswith("data:image/"):
+						# Process base64 encoded image
+						try:
+							# Extract the base64 part
+							base64_data = image_url.split(",")[1]
+							image_data = base64.b64decode(base64_data)
+
+							# Create temporary file to hold the image
+							with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmp:
+								tmp.write(image_data)
+								temp_files.append(tmp.name)
+						except Exception as e:
+							logger.error(f"Error processing base64 image: {str(e)}")
+
+			conversation += "\n\n"
+
+	# Add a final prompt for the assistant to respond to
+	conversation += "Assistant: "
+
+	return conversation, temp_files
+
+
+# Dependency to get the initialized Gemini client
+async def get_gemini_client():
+	global gemini_client
+	if gemini_client is None:
+		try:
+			gemini_client = GeminiClient(SECURE_1PSID, SECURE_1PSIDTS)
+			await gemini_client.init(timeout=300)
+		except Exception as e:
+			logger.error(f"Failed to initialize Gemini client: {str(e)}")
+			raise HTTPException(status_code=500, detail=f"Failed to initialize Gemini client: {str(e)}")
+	return gemini_client
+
+
+@app.post("/v1/chat/completions")
+async def create_chat_completion(request: ChatCompletionRequest):
+	try:
+		# 确保客户端已初始化
+		global gemini_client
+		if gemini_client is None:
+			gemini_client = GeminiClient(SECURE_1PSID, SECURE_1PSIDTS)
+			await gemini_client.init(timeout=300)
+			logger.info("Gemini client initialized successfully")
+
+		# 转换消息为对话格式
+		conversation, temp_files = prepare_conversation(request.messages)
+		logger.info(f"Prepared conversation: {conversation}")
+		logger.info(f"Temp files: {temp_files}")
+
+		# 获取适当的模型
+		model = map_model_name(request.model)
+		logger.info(f"Using model: {model}")
+
+		# 生成响应
+		logger.info("Sending request to Gemini...")
+		if temp_files:
+			# With files
+			response = await gemini_client.generate_content(conversation, files=temp_files, model=model)
+		else:
+			# Text only
+			response = await gemini_client.generate_content(conversation, model=model)
+
+		# 清理临时文件
+		for temp_file in temp_files:
+			try:
+				os.unlink(temp_file)
+			except Exception as e:
+				logger.warning(f"Failed to delete temp file {temp_file}: {str(e)}")
+
+		# 提取文本响应
+		reply_text = ""
+		if hasattr(response, "text"):
+			reply_text = response.text
+		else:
+			reply_text = str(response)
+
+		logger.info(f"Response: {reply_text}")
+
+		if not reply_text or reply_text.strip() == "":
+			logger.warning("Empty response received from Gemini")
+			reply_text = "服务器返回了空响应。请检查 Gemini API 凭据是否有效。"
+
+		# 创建响应对象
+		completion_id = f"chatcmpl-{uuid.uuid4()}"
+		created_time = int(time.time())
+
+		# 检查客户端是否请求流式响应
+		if request.stream:
+			# 实现流式响应
+			async def generate_stream():
+				# 创建 SSE 格式的流式响应
+				# 先发送开始事件
+				data = {
+					"id": completion_id,
+					"object": "chat.completion.chunk",
+					"created": created_time,
+					"model": request.model,
+					"choices": [{"index": 0, "delta": {"role": "assistant"}, "finish_reason": None}],
+				}
+				yield f"data: {json.dumps(data)}\n\n"
+
+				# 模拟流式输出 - 将文本按字符分割发送
+				for char in reply_text:
+					data = {
+						"id": completion_id,
+						"object": "chat.completion.chunk",
+						"created": created_time,
+						"model": request.model,
+						"choices": [{"index": 0, "delta": {"content": char}, "finish_reason": None}],
+					}
+					yield f"data: {json.dumps(data)}\n\n"
+					# 可选：添加短暂延迟以模拟真实的流式输出
+					await asyncio.sleep(0.01)
+
+				# 发送结束事件
+				data = {
+					"id": completion_id,
+					"object": "chat.completion.chunk",
+					"created": created_time,
+					"model": request.model,
+					"choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}],
+				}
+				yield f"data: {json.dumps(data)}\n\n"
+				yield "data: [DONE]\n\n"
+
+			return StreamingResponse(generate_stream(), media_type="text/event-stream")
+		else:
+			# 非流式响应（原来的逻辑）
+			result = {
+				"id": completion_id,
+				"object": "chat.completion",
+				"created": created_time,
+				"model": request.model,
+				"choices": [{"index": 0, "message": {"role": "assistant", "content": reply_text}, "finish_reason": "stop"}],
+				"usage": {
+					"prompt_tokens": len(conversation.split()),
+					"completion_tokens": len(reply_text.split()),
+					"total_tokens": len(conversation.split()) + len(reply_text.split()),
+				},
+			}
+
+			logger.info(f"Returning response: {result}")
+			return result
+
+	except Exception as e:
+		logger.error(f"Error generating completion: {str(e)}", exc_info=True)
+		raise HTTPException(status_code=500, detail=f"Error generating completion: {str(e)}")
+
+
+@app.get("/")
+async def root():
+	return {"status": "online", "message": "Gemini API FastAPI Server is running"}
+
+
+if __name__ == "__main__":
+	import uvicorn
+
+	uvicorn.run("main:app", host="0.0.0.0", port=8000, log_level="info")