import os import httpx import json import time from fastapi import FastAPI, HTTPException from fastapi.responses import JSONResponse from pydantic import BaseModel, Field from typing import List, Dict, Any, Optional, Union, Literal from dotenv import load_dotenv from sse_starlette.sse import EventSourceResponse # Load environment variables from .env file load_dotenv() # --- Configuration --- REPLICATE_API_TOKEN = os.getenv("REPLICATE_API_TOKEN") if not REPLICATE_API_TOKEN: raise ValueError("REPLICATE_API_TOKEN environment variable not set.") # --- FastAPI App Initialization --- app = FastAPI( title="Replicate to OpenAI Compatibility Layer", version="4.0.0 (Stable & Correct)", ) # --- Pydantic Models --- class ModelCard(BaseModel): id: str; object: str = "model"; created: int = Field(default_factory=lambda: int(time.time())); owned_by: str = "replicate" class ModelList(BaseModel): object: str = "list"; data: List[ModelCard] = [] class ChatMessage(BaseModel): role: Literal["system", "user", "assistant", "tool"]; content: Union[str, List[Dict[str, Any]]] class OpenAIChatCompletionRequest(BaseModel): model: str; messages: List[ChatMessage]; temperature: Optional[float] = 0.7; top_p: Optional[float] = 1.0; max_tokens: Optional[int] = None; stream: Optional[bool] = False # --- Model Mapping (Simplified for direct endpoint usage) --- SUPPORTED_MODELS = { "llama3-8b-instruct": { "id": "meta/meta-llama-3-8b-instruct", "input_type": "messages" }, "claude-4.5-haiku": { "id": "anthropic/claude-4.5-haiku", "input_type": "prompt" } } # --- Helper Functions --- def prepare_replicate_input(request: OpenAIChatCompletionRequest, model_details: dict) -> Dict[str, Any]: """Prepares the 'input' dictionary for Replicate, handling model-specific formats.""" input_payload = {} if model_details["input_type"] == "prompt": prompt_parts = [] system_prompt = None for msg in request.messages: if msg.role == "system": system_prompt = str(msg.content) elif msg.role == "user": prompt_parts.append(f"User: {msg.content}") elif msg.role == "assistant": prompt_parts.append(f"Assistant: {msg.content}") prompt_parts.append("Assistant:") input_payload["prompt"] = "\n".join(prompt_parts) if system_prompt: input_payload["system_prompt"] = system_prompt else: # "messages" input_payload["messages"] = [msg.dict() for msg in request.messages] if request.max_tokens is not None: input_payload["max_new_tokens"] = request.max_tokens if request.temperature is not None: input_payload["temperature"] = request.temperature if request.top_p is not None: input_payload["top_p"] = request.top_p return input_payload async def stream_replicate_native_sse(model_id: str, input_payload: dict): """Connects to Replicate's native SSE stream using the model-specific endpoint.""" url = f"https://api.replicate.com/v1/models/{model_id}/predictions" headers = {"Authorization": f"Bearer {REPLICATE_API_TOKEN}", "Content-Type": "application/json"} # The request body is now simple and correct request_body = {"input": input_payload, "stream": True} async with httpx.AsyncClient(timeout=300) as client: prediction = None try: response = await client.post(url, headers=headers, json=request_body) response.raise_for_status() prediction = response.json() stream_url = prediction.get("urls", {}).get("stream") if not stream_url: error_detail = prediction.get("detail", "Failed to get stream URL.") yield json.dumps({"error": {"message": error_detail}}) return except httpx.HTTPStatusError as e: try: yield json.dumps({"error": {"message": json.dumps(e.response.json())}}) except: yield json.dumps({"error": {"message": e.response.text}}) return try: async with client.stream("GET", stream_url, headers={"Accept": "text/event-stream"}) as sse: sse.raise_for_status() current_event = "" async for line in sse.aiter_lines(): if line.startswith("event:"): current_event = line[len("event:"):].strip() elif line.startswith("data:"): data = line[len("data:"):].strip() if current_event == "output": try: content = json.loads(data) chunk = { "id": prediction["id"], "object": "chat.completion.chunk", "created": int(time.time()), "model": model_id, "choices": [{"index": 0, "delta": {"content": content}, "finish_reason": None}] } yield json.dumps(chunk) except json.JSONDecodeError: # Silently ignore malformed or empty data lines pass elif current_event == "done": break except Exception as e: yield json.dumps({"error": {"message": f"Streaming error: {str(e)}"}}) done_chunk = { "id": prediction["id"] if prediction else "unknown", "object": "chat.completion.chunk", "created": int(time.time()), "model": model_id, "choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}] } yield json.dumps(done_chunk) yield "[DONE]" # --- API Endpoints --- @app.get("/v1/models", response_model=ModelList) async def list_models(): return ModelList(data=[ModelCard(id=model_name) for model_name in SUPPORTED_MODELS.keys()]) @app.post("/v1/chat/completions") async def create_chat_completion(request: OpenAIChatCompletionRequest): model_key = request.model if model_key not in SUPPORTED_MODELS: raise HTTPException(status_code=404, detail=f"Model not found. Supported models: {list(SUPPORTED_MODELS.keys())}") model_details = SUPPORTED_MODELS[model_key] replicate_input = prepare_replicate_input(request, model_details) if request.stream: return EventSourceResponse(stream_replicate_native_sse(model_details["id"], replicate_input)) # Synchronous Request url = f"https://api.replicate.com/v1/models/{model_details['id']}/predictions" headers = {"Authorization": f"Bearer {REPLICATE_API_TOKEN}", "Content-Type": "application/json", "Prefer": "wait=120"} async with httpx.AsyncClient(timeout=150) as client: try: response = await client.post(url, headers=headers, json={"input": replicate_input}) response.raise_for_status() prediction = response.json() output = "".join(prediction.get("output", [])) return JSONResponse(content={ "id": prediction["id"], "object": "chat.completion", "created": int(time.time()), "model": model_key, "choices": [{"index": 0, "message": {"role": "assistant", "content": output}, "finish_reason": "stop"}], "usage": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0} }) except httpx.HTTPStatusError as e: raise HTTPException(status_code=e.response.status_code, detail=e.response.text)