app.py

from fastapi import FastAPI
from pydantic import BaseModel
import uvicorn
from translator import translate

app = FastAPI()

# ===== Model untuk request/response =====
class ChatRequest(BaseModel):
    message: str
    history: list[dict] = []
    system_message: str = "You are a friendly chatbot."
    max_tokens: int = 512
    temperature: float = 0.7
    top_p: float = 0.95

class TranslateRequest(BaseModel):
    text: str
    direction: str  # "ID → EN" atau "EN → ID"

# ===== Endpoint Chatbot =====
@app.post("/chat")
def chat_endpoint(req: ChatRequest):
    # ⚡ Di sini kamu bisa sambungkan dengan InferenceClient seperti di kode awal
    return {"response": f"Simulasi jawaban untuk: {req.message}"}

# ===== Endpoint Translator =====
@app.post("/translate")
def translate_endpoint(req: TranslateRequest):
    result = translate(req.text, req.direction)
    return {"translation": result}

# ====== Run local (Space akan otomatis pakai uvicorn) ======
if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=7860)