Update Dockerfile

Dockerfile

@@ -1,8 +1,153 @@
-FROM python:3.9-slim
+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
 
-WORKDIR /app
-COPY . .
+# Inisialisasi aplikasi FastAPI
+app = FastAPI(
+    title="Lyon28 Model Inference API",
+    description="API untuk mengakses 11 model machine learning",
+    version="1.0.0"
+)
 
-RUN pip install --no-cache-dir -r requirements.txt
+# Konfigurasi CORS untuk frontend eksternal
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
 
-CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]
+# 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)