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="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}")