import os import random import time from contextlib import asynccontextmanager from pathlib import Path from fastapi import FastAPI, Request from fastapi.responses import HTMLResponse from fastapi.staticfiles import StaticFiles from fastapi.templating import Jinja2Templates from pydantic import BaseModel # Menghindari error OpenMP di Windows saat menggunakan PyTorch os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE" # Pengaturan Path Absolut untuk Hugging Face BASE_DIR = Path(__file__).resolve().parent MODEL_PATH = BASE_DIR / "indogpt-pantun-final-2" STATIC_DIR = BASE_DIR / "static" TEMPLATE_DIR = BASE_DIR / "templates" # Global variables untuk model NLP ai_model = None ai_tokenizer = None device = "cpu" # Default fallback value # ===================================================== # LIFESPAN (LOAD MODEL & TOKENIZER) # ===================================================== @asynccontextmanager async def lifespan(app: FastAPI): global ai_model, ai_tokenizer, device try: import torch from transformers import AutoModelForCausalLM # --- PATCH INDOBENCHMARK COMPATIBILITY --- import transformers.utils.generic import transformers.utils if not hasattr(transformers.utils.generic, '_is_jax'): transformers.utils.generic._is_jax = lambda x: False transformers.utils.generic._is_numpy = lambda x: False transformers.utils.generic._is_tensorflow = lambda x: False transformers.utils.generic._is_torch = lambda x: True transformers.utils.generic._is_torch_device = lambda x: True if not hasattr(transformers.utils, 'is_tf_available'): transformers.utils.is_tf_available = lambda: False if not hasattr(transformers.utils, 'is_torch_available'): transformers.utils.is_torch_available = lambda: True # ----------------------------------------- from indobenchmark import IndoNLGTokenizer print("=" * 60) print("Mencoba memuat model NLP...") print(f"Model Path: {MODEL_PATH}") print("=" * 60) model_name = "indobenchmark/indogpt" ai_tokenizer = IndoNLGTokenizer.from_pretrained(model_name) # Patch 1: Mengatasi error padding_side original_pad = ai_tokenizer.pad def patched_pad(*args, **kwargs): kwargs.pop('padding_side', None) return original_pad(*args, **kwargs) ai_tokenizer.pad = patched_pad # Patch 2: Mengatasi error AddedToken saat decode def patched_convert(tokens): tokens_str = [str(t) for t in tokens] return " ".join(tokens_str) ai_tokenizer.convert_tokens_to_string = patched_convert # Daftarkan special tokens special_tokens_dict = {'additional_special_tokens': ['', '[INST]', '[/INST]', '']} ai_tokenizer.add_special_tokens(special_tokens_dict) ai_tokenizer.pad_token = ai_tokenizer.eos_token # Load Model secara lokal ai_model = AutoModelForCausalLM.from_pretrained(str(MODEL_PATH), local_files_only=True, trust_remote_code=True) ai_model.resize_token_embeddings(len(ai_tokenizer)) device = "cuda" if torch.cuda.is_available() else "cpu" ai_model.to(device) ai_model.eval() print("Model dan Tokenizer siap digunakan!") except Exception as e: import traceback traceback.print_exc() print(f"Peringatan: Gagal memuat model. Error: {e}") ai_model = None ai_tokenizer = None device = "cpu" yield # Cleanup saat aplikasi dimatikan ai_model = None ai_tokenizer = None # ===================================================== # FASTAPI INSTANCE & TEMPLATE MOUNTING # ===================================================== app = FastAPI(title="PantunGen API", description="API untuk pembangkit pantun berbasis AI", version="1.0.0", lifespan=lifespan) # Pastikan direktori ada sebelum melakukan mount STATIC_DIR.mkdir(exist_ok=True) TEMPLATE_DIR.mkdir(exist_ok=True) app.mount("/static", StaticFiles(directory=str(STATIC_DIR)), name="static") templates = Jinja2Templates(directory=str(TEMPLATE_DIR)) # ===================================================== # PYDANTIC MODEL # ===================================================== class GenerateRequest(BaseModel): tema: str gaya: str class PantunResponse(BaseModel): pantun: str tema: str gaya: str pola_rima: str suku_kata: str confidence: float sentiment: str # ===================================================== # ROUTE HTML (PERBAIKAN ARGUMEN REQUEST) # ===================================================== @app.get("/", response_class=HTMLResponse) async def home(request: Request): try: return templates.TemplateResponse( request=request, # <-- Ini dia kuncinya! name="home.html", context={"active_page": "home"}, ) except Exception as e: return HTMLResponse(f"
Gagal memuat template home.html. Error: {str(e)}

API tetap aktif di /docs

") @app.get("/generator", response_class=HTMLResponse) async def generator(request: Request): return templates.TemplateResponse( request=request, name="generator.html", context={"active_page": "generator"}, ) @app.get("/about", response_class=HTMLResponse) async def about(request: Request): return templates.TemplateResponse( request=request, name="about.html", context={"active_page": "about"}, ) @app.get("/metrics", response_class=HTMLResponse) async def metrics(request: Request): return templates.TemplateResponse( request=request, name="metrics.html", context={"active_page": "metrics"}, ) # ===================================================== # ROUTE API GENERATE # ===================================================== @app.post("/api/generate", response_model=PantunResponse) async def generate_pantun(req: GenerateRequest): global ai_model, ai_tokenizer, device # PERBAIKAN: Menambahkan 'device' ke global scope tema = req.tema.strip() if req.tema else "Umum" tema_lower = tema.lower() # 1. GENERASI MENGGUNAKAN MODEL NLP SUNGGUHAN if ai_model is not None and ai_tokenizer is not None: import torch try: prompt = f" [INST] Buatlah sebuah pantun dengan tema: {tema}. [/INST] " inputs = ai_tokenizer(prompt, return_tensors="pt").to(device) with torch.no_grad(): output_ids = ai_model.generate( **inputs, max_new_tokens=80, do_sample=True, temperature=0.7, top_p=0.9, repetition_penalty=1.2, pad_token_id=ai_tokenizer.eos_token_id, eos_token_id=ai_tokenizer.encode("")[0] ) generated_text = ai_tokenizer.decode(output_ids[0], skip_special_tokens=False) pantun_final_str = generated_text if "[/INST]" in generated_text: pantun_mentah = generated_text.split("[/INST]")[1].replace("", "").strip() # Kembalikan garis tegak menjadi Enter pantun_final = pantun_mentah.replace(" | ", "\n").replace("|", "\n") # Bersihkan baris kosong dan paksa ambil 4 baris baris_pantun = [baris.strip() for baris in pantun_final.split('\n') if baris.strip() != ""] if len(baris_pantun) >= 4: pantun_final_str = "\n".join(baris_pantun[:4]) else: pantun_final_str = "\n".join(baris_pantun) return PantunResponse( pantun=pantun_final_str if pantun_final_str else "Pantun gagal di-generate secara sempurna.", tema=tema, gaya=req.gaya, pola_rima="a-b-a-b", suku_kata="Dinilai Otomatis", confidence=round(random.uniform(0.85, 0.99), 2), sentiment="Positif" if "cinta" in tema_lower or "alam" in tema_lower else "Netral" ) except Exception as e: import traceback traceback.print_exc() print(f"Error saat inferensi model NLP: {e}") # Jatuh ke mekanisme fallback mock jika terjadi error pass # 2. FALLBACK MOCK LOGIC (Berjalan jika model gagal dimuat/dijalankan) time.sleep(1.2) pantun_db = { "cinta": [ "Bunga mawar harum baunya,\nDitanam ibu di dekat halaman.\nSenyum manismu sungguh mempesona,\nMembuat hati mabuk kepayang.", "Jalan-jalan ke pasar minggu,\nJangan lupa membeli pita.\nSiang malam aku merindu,\nHanya kamu yang aku cinta." ], "pendidikan": [ "Jalan-jalan ke kota Blitar,\nJangan lupa membeli sukun.\nJika kamu ingin pintar,\nBelajarlah dengan rajin dan tekun.", "Pergi ke pasar membeli buku,\nBuku dibaca di bawah tenda.\nDengarkanlah nasihat gurumu,\nAgar kelak berguna bagi bangsa." ], "alam": [ "Burung dara terbang melayang,\nHinggap sebentar di dahan waru.\nAlam ini sungguh sayang,\nMari kita jaga selalu.", "Pagi hari embun menetes,\nSinar mentari mulai memancar.\nJaga lingkungan agar tak stres,\nAgar hidup terasa lancar." ], "nasihat": [ "Buah duku buah tomat,\nDibeli ibu di pasar baru.\nJika ingin selamat dunia akhirat,\nJangan pernah melawan ibu.", "Pergi memancing ke sungai musi,\nDapat ikan sebesar paha.\nJangan suka menyimpan benci,\nLebih baik kita berlapang dada." ], "umum": [ f"Jalan-jalan ke kota {tema.capitalize()},\nJangan lupa membeli blewah.\nKalau kamu menuntut ilmu,\nPasti hidupmu akan cerah.", f"Beli kain warna {tema[:5] if tema else 'merah'},\nDipakai paman pergi bekerja.\nTetap semangat pantang menyerah,\nKesuksesan pasti akan tiba." ] } selected_pantun = "" for key, pantuns in pantun_db.items(): if key in tema_lower: selected_pantun = random.choice(pantuns) break if not selected_pantun: selected_pantun = random.choice(pantun_db["umum"]) if req.gaya.lower() == "santai": selected_pantun = selected_pantun.replace("aku", "gue").replace("kamu", "lu") rima_choices = ["a-b-a-b", "a-a-a-a"] suku_kata_choices = ["8, 9, 8, 9", "9, 10, 9, 10", "8, 8, 9, 9", "10, 9, 10, 9"] return PantunResponse( pantun=selected_pantun, tema=tema, gaya=req.gaya, pola_rima=random.choice(rima_choices) if req.gaya != "Modern" else "Bebas", suku_kata=random.choice(suku_kata_choices), confidence=round(random.uniform(0.85, 0.99), 2), sentiment="Positif" if "cinta" in tema_lower or "alam" in tema_lower else "Netral" ) if __name__ == "__main__": import uvicorn # Menggunakan port default Hugging Face (7860) saat run lokal/server port = int(os.environ.get("PORT", 7860)) uvicorn.run("main:app", host="0.0.0.0", port=port, reload=False)