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import logging
import time
from concurrent.futures import ThreadPoolExecutor
from contextvars import ContextVar
from typing import Any, Dict, Generator, List
from anthropic import Anthropic
from fastapi import FastAPI, HTTPException, Request, Response
from fastapi.responses import JSONResponse, StreamingResponse, HTMLResponse
from fastapi.security import HTTPBearer
from starlette.concurrency import run_in_threadpool
from pathlib import Path
import markdown
from pygments.formatters import HtmlFormatter
from schemas import OpenAIChatCompletionForm, FilterForm
# logger
logger = logging.getLogger()
# FastAPI app initialization
app = FastAPI()
security = HTTPBearer()
# Context variable for token storage
token_context = ContextVar('token', default=None)
# Endpoints that don't require authentication
PUBLIC_ENDPOINTS = {"/"}
# Available Anthropic models
AVAILABLE_MODELS = [
"claude-3-haiku-20240307",
"claude-3-opus-20240229",
"claude-3-sonnet-20240229",
"claude-3-5-sonnet-20241022"
]
@app.middleware("http")
async def auth_middleware(request: Request, call_next):
"""
Middleware for handling authentication and response logging.
Args:
request: The incoming HTTP request
call_next: The next middleware in the chain
Returns:
Response: The processed HTTP response
"""
if request.url.path in PUBLIC_ENDPOINTS:
start_time = time.perf_counter()
response = await call_next(request)
process_time = time.perf_counter() - start_time
response.headers["X-Process-Time"] = str(process_time)
return response
try:
auth_header = request.headers.get('Authorization')
if not auth_header:
raise HTTPException(
status_code=401,
detail="No authorization header"
)
scheme, token = auth_header.split()
if scheme.lower() != 'bearer':
raise HTTPException(
status_code=401,
detail="Invalid authentication scheme"
)
token_context.set(token)
start_time = time.perf_counter()
response = await call_next(request)
process_time = time.perf_counter() - start_time
response.headers["X-Process-Time"] = str(process_time)
return response
except HTTPException as http_ex:
logger.error(
f"HTTP Exception - Status: {http_ex.status_code} - "
f"Detail: {http_ex.detail} - Path: {request.url.path}"
)
return JSONResponse(
status_code=http_ex.status_code,
content={"detail": http_ex.detail}
)
except Exception as e:
logger.error(
f"Unexpected error in middleware - Error: {str(e)} - "
f"Path: {request.url.path}",
exc_info=True
)
return JSONResponse(
status_code=500,
content={"detail": "Internal server error"}
)
def get_anthropic_client():
"""
Get an authenticated Anthropic client using the current token.
Returns:
Anthropic: An authenticated Anthropic client instance
Raises:
HTTPException: If no authorization token is found
"""
token = token_context.get()
if not token:
raise HTTPException(status_code=401, detail="No authorization token found")
return Anthropic(api_key=token)
@app.get("/v1")
@app.get("/")
async def read_root():
"""Root endpoint for API health check."""
try:
# Lecture du README.md
readme_path = Path("README.md")
if not readme_path.exists():
return HTMLResponse(content="<h1>README.md non trouvé</h1>")
md_text = readme_path.read_text(encoding='utf-8')
md_text = '\n'.join(md_text.split('\n')[10:])
# Conversion Markdown vers HTML
html = markdown.markdown(
md_text,
extensions=[
'markdown.extensions.fenced_code',
'markdown.extensions.tables',
'markdown.extensions.codehilite',
'markdown.extensions.sane_lists'
]
)
# Lecture du CSS
css_file = Path("main.css")
custom_css = css_file.read_text(encoding='utf-8') if css_file.exists() else ""
# CSS pour la coloration syntaxique
code_css = HtmlFormatter(style='default').get_style_defs('.codehilite')
# Construction de la page HTML
html_content = f"""
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<style>
{custom_css}
{code_css}
</style>
</head>
<body>
<div class="markdown-body">
{html}
</div>
</body>
</html>
"""
return HTMLResponse(content=html_content)
except Exception as e:
return HTMLResponse(
content=f"<h1>Erreur: {str(e)}</h1>",
status_code=500
)
@app.get("/v1/models")
@app.get("/models")
async def get_models():
"""
Get available Anthropic models.
Returns:
JSONResponse: List of available models and their details
"""
get_anthropic_client() # Verify token validity
models = [
{
"id": model_id,
"object": "model",
"name": f"🤖 {model_id}",
"created": int(time.time()),
"owned_by": "anthropic",
"pipeline": {"type": "custom", "valves": False}
}
for model_id in AVAILABLE_MODELS
]
return JSONResponse(
content={
"data": models,
"object": "list",
"pipelines": True,
}
)
def stream_message(
model: str,
messages: List[Dict[str, Any]]
) -> Generator[str, None, None]:
"""
Stream messages using the specified model.
Args:
model: The model identifier to use
messages: List of messages to process
Returns:
Generator: Stream of SSE formatted responses
"""
client = get_anthropic_client()
response = client.messages.create(
model=model,
max_tokens=1024,
messages=messages,
stream=True
)
def event_stream() -> Generator[str, None, None]:
message_id = None
for chunk in response:
if not message_id:
message_id = f"chatcmpl-{int(time.time())}"
if chunk.type == 'content_block_delta':
data = {
"id": message_id,
"object": "chat.completion.chunk",
"created": int(time.time()),
"model": model,
"choices": [
{
"index": 0,
"delta": {
"content": (
chunk.delta.text
if hasattr(chunk.delta, 'text')
else ""
)
},
"logprobs": None,
"finish_reason": None,
}
],
}
yield f"data: {json.dumps(data)}\n\n"
elif chunk.type == 'content_block_stop':
data = {
"id": message_id,
"object": "chat.completion.chunk",
"created": int(time.time()),
"model": model,
"choices": [
{
"index": 0,
"delta": {},
"logprobs": None,
"finish_reason": "stop",
}
],
}
yield f"data: {json.dumps(data)}\n\n"
yield "data: [DONE]\n\n"
return event_stream()
def send_message(model: str, messages: List[Dict[str, Any]]) -> Dict[str, Any]:
"""
Send a message via the Anthropic provider without streaming.
Args:
model: The model identifier to use
messages: List of messages to process
Returns:
dict: The formatted response from the model
"""
client = get_anthropic_client()
response = client.messages.create(
model=model,
max_tokens=1024,
messages=messages
)
content = response.content[0].text if response.content else ""
return {
"id": response.id,
"object": "chat.completion",
"created": int(time.time()),
"model": model,
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": content,
},
"logprobs": None,
"finish_reason": "stop",
}
],
}
@app.post("/v1/chat/completions")
@app.post("/chat/completions")
async def generate_chat_completion(form_data: OpenAIChatCompletionForm):
"""
Generate chat completions from the model.
Args:
form_data: The chat completion request parameters
Returns:
Union[StreamingResponse, dict]: Either a streaming response or a complete message
"""
messages = [
{"role": message.role, "content": message.content}
for message in form_data.messages
]
model = form_data.model
def job():
"""Handle both streaming and non-streaming modes."""
if form_data.stream:
return StreamingResponse(
stream_message(model=model, messages=messages),
media_type="text/event-stream"
)
return send_message(model=model, messages=messages)
with ThreadPoolExecutor() as executor:
return await run_in_threadpool(job)
@app.post("/v1/{pipeline_id}/filter/inlet")
@app.post("/{pipeline_id}/filter/inlet")
async def filter_inlet(pipeline_id: str, form_data: FilterForm):
"""
Handle inlet filtering for the pipeline.
Args:
pipeline_id: The ID of the pipeline
form_data: The filter parameters
Returns:
dict: The processed request body
"""
return form_data.body
@app.post("/v1/{pipeline_id}/filter/outlet")
@app.post("/{pipeline_id}/filter/outlet")
async def filter_outlet(pipeline_id: str, form_data: FilterForm):
"""
Handle outlet filtering for the pipeline.
Args:
pipeline_id: The ID of the pipeline
form_data: The filter parameters
Returns:
dict: The processed request body
"""
return form_data.body |