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 load_dotenv() REPLICATE_API_TOKEN = os.getenv("REPLICATE_API_TOKEN") if not REPLICATE_API_TOKEN: raise ValueError("REPLICATE_API_TOKEN environment variable not set.") # FastAPI Init app = FastAPI(title="Replicate to OpenAI Compatibility Layer", version="7.1.0 (Streaming Space Fix)") # --- 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 # --- Supported Models --- SUPPORTED_MODELS = { "llama3-8b-instruct": "meta/meta-llama-3-8b-instruct", "claude-4.5-haiku": "anthropic/claude-4.5-haiku", "claude-4.5-sonnet": "anthropic/claude-4.5-sonnet", "llava-13b": "yorickvp/llava-13b:e272157381e2a3bf12df3a8edd1f38d1dbd736bbb7437277c8b34175f8fce358" } # --- Core Logic --- def prepare_replicate_input(request: OpenAIChatCompletionRequest) -> Dict[str, Any]: """ Formats the input for Replicate's API, flattening the message history into a single 'prompt' string and handling images separately. This is the required format for all their current chat/vision models. """ payload = {} prompt_parts = [] system_prompt = None image_input = None for msg in request.messages: if msg.role == "system": system_prompt = str(msg.content) elif msg.role == "assistant": prompt_parts.append(f"Assistant: {msg.content}") elif msg.role == "user": user_text_content = "" if isinstance(msg.content, list): for item in msg.content: if item.get("type") == "text": user_text_content += item.get("text", "") elif item.get("type") == "image_url": image_url_data = item.get("image_url", {}) image_input = image_url_data.get("url") else: user_text_content = str(msg.content) prompt_parts.append(f"User: {user_text_content}") prompt_parts.append("Assistant:") payload["prompt"] = "\n\n".join(prompt_parts) if system_prompt: payload["system_prompt"] = system_prompt if image_input: payload["image"] = image_input if request.max_tokens: payload["max_new_tokens"] = request.max_tokens if request.temperature: payload["temperature"] = request.temperature if request.top_p: payload["top_p"] = request.top_p return payload async def stream_replicate_sse(replicate_model_id: str, input_payload: dict): """Handles the full streaming lifecycle with robust token parsing.""" url = f"https://api.replicate.com/v1/models/{replicate_model_id}/predictions" headers = {"Authorization": f"Bearer {REPLICATE_API_TOKEN}", "Content-Type": "application/json"} async with httpx.AsyncClient(timeout=60.0) as client: try: response = await client.post(url, headers=headers, json={"input": input_payload, "stream": True}) response.raise_for_status() prediction = response.json() stream_url = prediction.get("urls", {}).get("stream") prediction_id = prediction.get("id", "stream-unknown") if not stream_url: yield json.dumps({"error": {"message": "Model did not return a stream URL."}}) return except httpx.HTTPStatusError as e: error_details = e.response.text try: error_json = e.response.json() error_details = error_json.get("detail", error_details) except json.JSONDecodeError: pass yield json.dumps({"error": {"message": f"Upstream Error: {error_details}", "type": "replicate_error"}}) return try: async with client.stream("GET", stream_url, headers={"Accept": "text/event-stream"}, timeout=None) as sse: current_event = None 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": # --- START OF STREAMING FIX --- # Replicate streams tokens that can be plain text or JSON-encoded strings. # We need to robustly parse them to preserve spaces correctly. content_token = "" try: # Attempt to parse data as JSON. This handles tokens like "\" Hello\"" decoded_data = json.loads(data) if isinstance(decoded_data, str): content_token = decoded_data else: # It's some other JSON type, convert to string content_token = str(decoded_data) except json.JSONDecodeError: # It's not valid JSON, so it's a plain text token. content_token = data if content_token: chunk = { "id": prediction_id, "object": "chat.completion.chunk", "created": int(time.time()), "model": replicate_model_id, "choices": [{"index": 0, "delta": {"content": content_token}, "finish_reason": None}] } yield json.dumps(chunk) # --- END OF STREAMING FIX --- elif current_event == "done": break except httpx.ReadTimeout: yield json.dumps({"error": {"message": "Stream timed out.", "type": "timeout_error"}}) return final_chunk = { "id": prediction_id, "object": "chat.completion.chunk", "created": int(time.time()), "model": replicate_model_id, "choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}] } yield json.dumps(final_chunk) yield "[DONE]" # --- Endpoints --- @app.get("/v1/models") async def list_models(): return ModelList(data=[ModelCard(id=k) for k in SUPPORTED_MODELS.keys()]) @app.post("/v1/chat/completions") async def create_chat_completion(request: OpenAIChatCompletionRequest): if request.model not in SUPPORTED_MODELS: raise HTTPException(status_code=404, detail=f"Model not found. Available models: {list(SUPPORTED_MODELS.keys())}") replicate_input = prepare_replicate_input(request) if request.stream: return EventSourceResponse(stream_replicate_sse(SUPPORTED_MODELS[request.model], replicate_input), media_type="text/event-stream") # Non-streaming fallback url = f"https://api.replicate.com/v1/models/{SUPPORTED_MODELS[request.model]}/predictions" headers = {"Authorization": f"Bearer {REPLICATE_API_TOKEN}", "Content-Type": "application/json", "Prefer": "wait=120"} async with httpx.AsyncClient() as client: try: resp = await client.post(url, headers=headers, json={"input": replicate_input}, timeout=130.0) resp.raise_for_status() pred = resp.json() output = "".join(pred.get("output", [])) return { "id": pred.get("id"), "object": "chat.completion", "created": int(time.time()), "model": request.model, "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=f"Error from Replicate API: {e.response.text}")