app.py

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from transformers import pipeline

# 1. Definisikan "otak" aplikasi (sama persis seperti di Gradio)
MODEL_CONFIG = {
    # (Salin MODEL_CONFIG lengkap dari atas ke sini)
    "Lyon28/GPT-2-Tinny": {"task": "text-generation", "display_name": "GPT-2 (Tiny)"},
    "Lyon28/GPT-2": {"task": "text-generation", "display_name": "GPT-2"},
    "Lyon28/Distil_GPT-2": {"task": "text-generation", "display_name": "DistilGPT-2"},
    "Lyon28/GPT-Neo": {"task": "text-generation", "display_name": "GPT-Neo"},
    "Lyon28/Pythia": {"task": "text-generation", "display_name": "Pythia"},
    "Lyon28/Tinny-Llama": {"task": "text-generation", "display_name": "Tinny-Llama"},
    "Lyon28/Bert-Tinny": {"task": "fill-mask", "display_name": "BERT (Tiny)"},
    "Lyon28/Distilbert-Base-Uncased": {"task": "fill-mask", "display_name": "DistilBERT"},
    "Lyon28/Albert-Base-V2": {"task": "fill-mask", "display_name": "Albert v2"},
    "Lyon28/Electra-Small": {"task": "fill-mask", "display_name": "Electra (Small)"},
    "Lyon28/T5-Small": {"task": "text2text-generation", "display_name": "T5 (Small)"},
}

# 2. Buat "gudang" model (sama persis)
loaded_pipelines = {}

# 3. Definisikan format request yang diterima
class InferenceRequest(BaseModel):
    model_id: str
    prompt: str

app = FastAPI()

@app.get("/")
def read_root():
    return {"message": "Smart Inference API is running. Use the /inference endpoint."}

@app.post("/inference")
def smart_inference(request: InferenceRequest):
    model_id = request.model_id
    
    # Validasi: Cek apakah model_id ada di config kita
    if model_id not in MODEL_CONFIG:
        raise HTTPException(status_code=400, detail=f"Model '{model_id}' tidak valid atau tidak didukung.")
        
    task = MODEL_CONFIG[model_id]["task"]
    
    # Cek "gudang" (logika caching yang sama)
    if model_id not in loaded_pipelines:
        print(f"Memuat model: {model_id} untuk task: {task}...")
        try:
            pipe = pipeline(task, model=model_id, device=-1)
            loaded_pipelines[model_id] = pipe
            print("Model berhasil dimuat.")
        except Exception as e:
            raise HTTPException(status_code=500, detail=f"Gagal memuat model: {str(e)}")
            
    pipe = loaded_pipelines[model_id]
    
    # Jalankan inference
    try:
        result = pipe(request.prompt)
        return {"model_used": model_id, "task": task, "input_prompt": request.prompt, "output": result}
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Gagal melakukan inference: {str(e)}")