Create app.py

app.py

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+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)}")