main.py

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="2.3.0 (Definitive Streaming 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

# --- Model Mapping ---
SUPPORTED_MODELS = {
    "llama3-8b-instruct": "meta/meta-llama-3-8b-instruct",
    "claude-4.5-haiku": "anthropic/claude-4.5-haiku"
}

# --- Helper Functions ---

def prepare_replicate_input(request: OpenAIChatCompletionRequest) -> Dict[str, Any]:
    """Prepares the input payload for Replicate, handling model-specific formats."""
    payload = {}
    
    if "claude" in request.model:
        prompt_parts = []
        system_prompt = None
        image_url = None
        for msg in request.messages:
            if msg.role == "system":
                system_prompt = str(msg.content)
            elif msg.role == "user":
                if isinstance(msg.content, list):
                    for item in msg.content:
                        if item.get("type") == "text":
                            prompt_parts.append(f"User: {item.get('text', '')}")
                        elif item.get("type") == "image_url":
                            image_url = item.get("image_url", {}).get("url")
                else:
                    prompt_parts.append(f"User: {msg.content}")
            elif msg.role == "assistant":
                prompt_parts.append(f"Assistant: {msg.content}")
        prompt_parts.append("Assistant:")
        payload["prompt"] = "\n".join(prompt_parts)
        if system_prompt: payload["system_prompt"] = system_prompt
        if image_url: payload["image"] = image_url
    else:
        payload["messages"] = [msg.dict() for msg in request.messages]

    if request.max_tokens is not None: payload["max_new_tokens"] = request.max_tokens
    if request.temperature is not None: payload["temperature"] = request.temperature
    if request.top_p is not None: payload["top_p"] = request.top_p
    return payload

async def stream_replicate_native_sse(model_id: str, payload: dict):
    """Connects to Replicate's native SSE stream for token-by-token streaming."""
    url = f"https://api.replicate.com/v1/models/{model_id}/predictions"
    headers = {"Authorization": f"Bearer {REPLICATE_API_TOKEN}", "Content-Type": "application/json"}
    
    async with httpx.AsyncClient(timeout=300) as client:
        prediction = None
        try:
            response = await client.post(url, headers=headers, json={"input": payload, "stream": True})
            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":
                            # *** THIS IS THE DEFINITIVE FIX ***
                            # Wrap the JSON parsing in a try-except block to gracefully
                            # handle empty or malformed data lines without crashing.
                            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:
                                # This will silently ignore any non-JSON data, like empty strings.
                                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())}")
    
    replicate_model_id = SUPPORTED_MODELS[model_key]
    replicate_input = prepare_replicate_input(request)

    if request.stream:
        return EventSourceResponse(stream_replicate_native_sse(replicate_model_id, replicate_input))
    
    url = f"https://api.replicate.com/v1/models/{replicate_model_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)