app.py

# ============================================================
# FINAL 16GB SAFE TRANSLATION SYSTEM
# NLLB + RAG + LLM (POST-EDITOR)
# Jawa → Indonesia → English
# ============================================================

import torch
import faiss
import pandas as pd
import gradio as gr
from sentence_transformers import SentenceTransformer
from transformers import (
    AutoTokenizer,
    AutoModelForSeq2SeqLM,
    AutoModelForCausalLM
)

device = "cuda" if torch.cuda.is_available() else "cpu"

# ============================================================
# NLLB MODEL
# ============================================================

NLLB = "facebook/nllb-200-distilled-600M"

nllb_tok = AutoTokenizer.from_pretrained(NLLB)
nllb = AutoModelForSeq2SeqLM.from_pretrained(NLLB).to(device)
nllb.eval()

JV = "jav_Latn"
ID = "ind_Latn"
EN = "eng_Latn"


from transformers import AutoTokenizer, AutoModelForCausalLM


# ============================================================
# LIGHTWEIGHT LLM (POST-EDITOR)
# ============================================================

LLM = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"

# =========================
# 1. LOAD TOKENIZER
# =========================
llm_tok = AutoTokenizer.from_pretrained(LLM)

# =========================
# 2. FIX PAD TOKEN (WAJIB)
# =========================
llm_tok.pad_token = llm_tok.eos_token
llm_tok.pad_token_id = llm_tok.eos_token_id

# =========================
# 3. LOAD MODEL
# =========================

llm = AutoModelForCausalLM.from_pretrained(
    LLM,
    torch_dtype=torch.float16 if device == "cuda" else torch.float32,
    low_cpu_mem_usage=True
).to(device)

llm.eval()

# ============================================================
# KNOWLEDGE BASE
# ============================================================

kb = pd.read_csv("kb_jawa_ngoko_krama_indonesia_100k.csv")
kb["jv"] = kb["jv"].astype(str)
kb["id"] = kb["id"].astype(str)

pairs = list(zip(kb["jv"], kb["id"]))



# ============================================================
# RAG INDEX
# ============================================================

embedder = SentenceTransformer(
    "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
)

emb = embedder.encode(
    kb["jv"].tolist(),
    batch_size=128,
    convert_to_numpy=True,
    show_progress_bar=True
)


index = faiss.IndexFlatL2(emb.shape[1])
index.add(emb)

def retrieve(text, k=5):
    v = embedder.encode([text])
    _, I = index.search(v, k)
    return "\n".join(
        f"{pairs[i][0]} → {pairs[i][1]}" for i in I[0]
    )




# ============================================================
# TRANSLATION CORE
# ============================================================

def nllb_translate(text, src, tgt):
    nllb_tok.src_lang = src
    inputs = nllb_tok(
        text,
        return_tensors="pt",
        truncation=True,
        max_length=512
    ).to(device)

    out = nllb.generate(
        **inputs,
        forced_bos_token_id=nllb_tok.convert_tokens_to_ids(tgt),
        max_length=256
    )

    return nllb_tok.decode(out[0], skip_special_tokens=True)
  

# ============================================================
# POST-EDITING (NO LEAK)
# ============================================================

def refine_id(text, context):

    prompt = f"""
Perbaiki hasil terjemahan Bahasa Indonesia agar alami dan baku.

Jangan menerjemahkan ulang.
Jangan menambahkan informasi baru.
Jangan menampilkan referensi.

Kalimat:
{text}

Versi yang lebih alami:
"""

    inp = llm_tok(prompt, return_tensors="pt").to(device)

    out = llm.generate(
        **inp,
        max_new_tokens=80,
        temperature=0.15,
        do_sample=False
    )

    return llm_tok.decode(
        out[0],
        skip_special_tokens=True
    ).split("alami:")[-1].strip()

# ============================================================
# FULL PIPELINE
# ============================================================

def pipeline(text):

    # Jawa → Indonesia
    id_raw = nllb_translate(text, JV, ID)

    # RAG grounding (hidden)
    ctx = retrieve(text)

    # Post-edit
    id_final = refine_id(id_raw, ctx)

    # Indonesia → English
    en_final = nllb_translate(id_final, ID, EN)

    return id_final, en_final

# ============================================================
# GRADIO UI
# ============================================================

with gr.Blocks(title="Mesin Translasi Bahasa Jawa") as demo:
    gr.Markdown(
        """
        ## 🌾 Mesin Translasi Bahasa Jawa  
        **Jawa → Indonesia → English**

        ✔ NLLB-200 (Meta)  
        ✔ RAG Parallel Corpus (hidden)  
        ✔ LLM post-editor (linguistic refinement)  
        ✔ Output bersih tanpa referensi  
        ✔ Aman 16GB  

        ---
        """
    )

    inp = gr.Textbox(
        label="Input Bahasa Jawa",
        lines=4,
        placeholder="Contoh: Aku arep lunga menyang pasar sesuk."
    )

    with gr.Row():
        out_id = gr.Textbox(label="Bahasa Indonesia", lines=4)
        out_en = gr.Textbox(label="English Translation", lines=4)

    gr.Button("🔄 Terjemahkan").click(
        pipeline,
        inp,
        [out_id, out_en]
    )

demo.launch()