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.env.example

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+SECURE_1PSID=your_psid_value_here
+SECURE_1PSIDTS=your_psidts_value_here
+API_KEY=your_api_key_here

.gitignore

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

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"]

docker-compose.yml

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+version: "3"
+
+services:
+  gemini-api:
+    build: .
+    ports:
+      - "8000:8000"
+    volumes:
+      - ./main.py:/app/main.py
+      - ./pyproject.toml:/app/pyproject.toml
+    env_file:
+      - .env
+    restart: unless-stopped

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, Depends, Header
+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", "")
+API_KEY = os.environ.get("API_KEY", "")
+
+# 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]}...")
+
+if not API_KEY:
+	logger.warning("⚠️ API_KEY is not set or empty! API authentication will not work.")
+	logger.warning("Make sure API_KEY is correctly set in your .env file or environment.")
+else:
+	logger.info(f"API_KEY found. API_KEY starts with: {API_KEY[: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]
+
+
+# Authentication dependency
+async def verify_api_key(authorization: str = Header(None)):
+	if not API_KEY:
+		# If API_KEY is not set in environment, skip validation (for development)
+		logger.warning("API key validation skipped - no API_KEY set in environment")
+		return
+
+	if not authorization:
+		raise HTTPException(status_code=401, detail="Missing Authorization header")
+	
+	try:
+		scheme, token = authorization.split()
+		if scheme.lower() != "bearer":
+			raise HTTPException(status_code=401, detail="Invalid authentication scheme. Use Bearer token")
+		
+		if token != API_KEY:
+			raise HTTPException(status_code=401, detail="Invalid API key")
+	except ValueError:
+		raise HTTPException(status_code=401, detail="Invalid authorization format. Use 'Bearer YOUR_API_KEY'")
+	
+	return token
+
+
+# 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
+
+	# 如果还是找不到，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, api_key: str = Depends(verify_api_key)):
+	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")

pyproject.toml

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+[project]
+name = "gemi2api-server"
+version = "0.1.1"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.11"
+dependencies = [
+    "browser-cookie3>=0.20.1",
+    "fastapi>=0.115.12",
+    "gemini-webapi>=1.11.0",
+    "uvicorn[standard]>=0.34.1",
+]
+[[tool.uv.index]]
+url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple"
+default = true
+
+[dependency-groups]
+dev = [
+    "ruff>=0.11.7",
+]
+
+[tool.ruff]
+line-length = 150  # 设置最大行长度
+select = ["E", "F", "W", "I"]  # 启用的规则（E: pycodestyle, F: pyflakes, W: pycodestyle warnings, I: isort）
+ignore = ["E501"]  # 忽略特定规则（如行长度警告）
+
+[tool.ruff.format]
+quote-style = "double"  # 使用双引号
+indent-style = "tab"  # 使用空格缩进