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
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="4.2.0 (Prompt Format Fixed)")

# --- 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"
}

# --- Core Logic ---
def prepare_replicate_input(request: OpenAIChatCompletionRequest) -> Dict[str, Any]:
    """
    Formats the input for Replicate API. This function now correctly builds a
    single prompt string from the message history, which is required by
    Replicate's endpoints for models like Claude and Llama 3.
    """
    payload = {}
    
    # --- PROMPT FORMAT FIX START ---
    prompt_parts = []
    system_prompt = None
    
    for msg in request.messages:
        if msg.role == "system":
            # Extract system prompt, as it's a separate parameter for many models
            system_prompt = str(msg.content)
        elif msg.role == "user":
            # Format user messages
            content = msg.content
            if isinstance(content, list): # Handle potential future vision models
                 text_parts = [item.get("text", "") for item in content if item.get("type") == "text"]
                 content = " ".join(text_parts)
            prompt_parts.append(f"User: {content}")
        elif msg.role == "assistant":
            # Format assistant messages
            prompt_parts.append(f"Assistant: {msg.content}")
    
    # Add the final "Assistant:" turn to prompt the model for a response.
    # This is a standard convention for many chat models when using a single prompt string.
    prompt_parts.append("Assistant:")
    
    # The main input is a single 'prompt' string with turns separated by newlines.
    payload["prompt"] = "\n\n".join(prompt_parts)
    
    if system_prompt:
         payload["system_prompt"] = system_prompt
         
    # --- PROMPT FORMAT FIX END ---

    # Map common OpenAI parameters to Replicate equivalents
    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 using standard Replicate endpoints."""
    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":
                            if data:
                                chunk = {
                                    "id": prediction_id, "object": "chat.completion.chunk", "created": int(time.time()), "model": replicate_model_id,
                                    "choices": [{"index": 0, "delta": {"content": data}, "finish_reason": None}]
                                }
                                yield json.dumps(chunk)
                                
                        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():
    """Lists the currently supported 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):
    """Handles chat completion requests, streaming or non-streaming."""
    if request.model not in SUPPORTED_MODELS:
        raise HTTPException(status_code=404, detail=f"Model not found. Available models: {list(SUPPORTED_MODELS.keys())}")
    
    replicate_id = SUPPORTED_MODELS[request.model]
    replicate_input = prepare_replicate_input(request)

    if request.stream:
        return EventSourceResponse(stream_replicate_sse(replicate_id, replicate_input), media_type="text/event-stream")

    # Non-streaming fallback
    url = f"https://api.replicate.com/v1/models/{replicate_id}/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}")