Update Dockerfile

Dockerfile

@@ -1,153 +1,8 @@
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
-from transformers import pipeline
-import torch
-from fastapi.middleware.cors import CORSMiddleware
-from typing import Dict, Any
+FROM python:3.9-slim
 
-# Inisialisasi aplikasi FastAPI
-app = FastAPI(
-    title="Lyon28 Model Inference API",
-    description="API untuk mengakses 11 model machine learning",
-    version="1.0.0"
-)
+WORKDIR /app
+COPY . .
 
-# Konfigurasi CORS untuk frontend eksternal
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
+RUN pip install --no-cache-dir -r requirements.txt
 
-# Konfigurasi Model
-MODEL_MAP = {
-    "tinny-llama": "Lyon28/Tinny-Llama",
-    "pythia": "Lyon28/Pythia",
-    "bert-tinny": "Lyon28/Bert-Tinny",
-    "albert-base-v2": "Lyon28/Albert-Base-V2",
-    "t5-small": "Lyon28/T5-Small",
-    "gpt-2": "Lyon28/GPT-2",
-    "gpt-neo": "Lyon28/GPT-Neo",
-    "distilbert-base-uncased": "Lyon28/Distilbert-Base-Uncased",
-    "distil-gpt-2": "Lyon28/Distil_GPT-2",
-    "gpt-2-tinny": "Lyon28/GPT-2-Tinny",
-    "electra-small": "Lyon28/Electra-Small"
-}
-
-TASK_MAP = {
-    "text-generation": ["gpt-2", "gpt-neo", "distil-gpt-2", "gpt-2-tinny", "tinny-llama", "pythia"],
-    "text-classification": ["bert-tinny", "albert-base-v2", "distilbert-base-uncased", "electra-small"],
-    "text2text-generation": ["t5-small"]
-}
-
-class InferenceRequest(BaseModel):
-    text: str
-    max_length: int = 100
-    temperature: float = 0.9
-    top_p: float = 0.95
-
-# Helper functions
-def get_task(model_id: str) -> str:
-    for task, models in TASK_MAP.items():
-        if model_id in models:
-            return task
-    return "text-generation"
-
-# Event startup untuk inisialisasi model
-@app.on_event("startup")
-async def load_models():
-    app.state.pipelines = {}
-    print("🟢 Semua model siap digunakan!")
-
-# Endpoint utama
-@app.get("/")
-async def root():
-    return {
-        "message": "Selamat datang di Lyon28 Model API",
-        "endpoints": {
-            "documentation": "/docs",
-            "model_list": "/models",
-            "health_check": "/health",
-            "inference": "/inference/{model_id}"
-        },
-        "total_models": len(MODEL_MAP)
-    }
-
-# Endpoint untuk list model
-@app.get("/models")
-async def list_models():
-    return {
-        "available_models": list(MODEL_MAP.keys()),
-        "total_models": len(MODEL_MAP)
-    }
-
-# Endpoint health check
-@app.get("/health")
-async def health_check():
-    return {
-        "status": "healthy",
-        "gpu_available": torch.cuda.is_available(),
-        "gpu_type": torch.cuda.get_device_name(0) if torch.cuda.is_available() else "CPU-only"
-    }
-
-# Endpoint inference utama
-@app.post("/inference/{model_id}")
-async def model_inference(model_id: str, request: InferenceRequest):
-    try:
-        # Validasi model ID
-        if model_id not in MODEL_MAP:
-            raise HTTPException(
-                status_code=404,
-                detail=f"Model {model_id} tidak ditemukan. Cek /models untuk list model yang tersedia."
-            )
-
-        # Dapatkan task yang sesuai
-        task = get_task(model_id)
-        
-        # Load model jika belum ada di memory
-        if model_id not in app.state.pipelines:
-            app.state.pipelines[model_id] = pipeline(
-                task=task,
-                model=MODEL_MAP[model_id],
-                device=0 if torch.cuda.is_available() else -1,
-                torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
-            )
-            print(f"✅ Model {model_id} berhasil dimuat!")
-
-        pipe = app.state.pipelines[model_id]
-
-        # Proses berdasarkan task
-        if task == "text-generation":
-            result = pipe(
-                request.text,
-                max_length=request.max_length,
-                temperature=request.temperature,
-                top_p=request.top_p
-            )[0]['generated_text']
-        
-        elif task == "text-classification":
-            output = pipe(request.text)[0]
-            result = {
-                "label": output['label'],
-                "confidence": round(output['score'], 4)
-            }
-        
-        elif task == "text2text-generation":
-            result = pipe(
-                request.text,
-                max_length=request.max_length
-            )[0]['generated_text']
-
-        return {"result": result}
-    
-    except Exception as e:
-        raise HTTPException(
-            status_code=500,
-            detail=f"Error processing request: {str(e)}"
-        )
-
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]