fine_tune_inference_test.py

import os
import threading
import uvicorn
from fastapi import FastAPI, Request
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from datasets import load_dataset
from fastapi.responses import JSONResponse

# ✅ Sabitler
HF_TOKEN = os.environ.get("HF_TOKEN")
MODEL_BASE = "UcsTurkey/kanarya-750m-fixed"
FINE_TUNE_ZIP = "trained_model_000_100.zip"  # 👈 Değiştirilebilir
FINE_TUNE_REPO = "UcsTurkey/trained-zips"
RAG_DATA_FILE = "merged_dataset_000_100.parquet" # 👈 Değiştirilebilir
RAG_DATA_REPO = "UcsTurkey/turkish-general-culture-tokenized"

# ✅ FastAPI app
app = FastAPI()
chat_history = []

class Message(BaseModel):
    user_input: str

@app.get("/")
def health():
    return {"status": "ok"}

@app.post("/chat")
def chat(msg: Message):
    user_input = msg.user_input.strip()
    if not user_input:
        return {"error": "Boş giriş"}

    full_prompt = ""
    for turn in chat_history:
        full_prompt += f"Kullanıcı: {turn['user']}\nAsistan: {turn['bot']}\n"
    full_prompt += f"Kullanıcı: {user_input}\nAsistan:"

    result = pipe(full_prompt, max_new_tokens=200, do_sample=True, temperature=0.7)
    answer = result[0]["generated_text"][len(full_prompt):].strip()

    chat_history.append({"user": user_input, "bot": answer})
    return {"answer": answer, "chat_history": chat_history}


# ✅ Model ve RAG yükleme
def setup_model():
    global pipe
    from huggingface_hub import hf_hub_download
    import zipfile

    print("📦 Fine-tune zip indiriliyor...")
    zip_path = hf_hub_download(
        repo_id=FINE_TUNE_REPO,
        filename=FINE_TUNE_ZIP,
        repo_type="model",
        token=HF_TOKEN
    )
    extract_dir = "/app/extracted"
    os.makedirs(extract_dir, exist_ok=True)
    with zipfile.ZipFile(zip_path, "r") as zip_ref:
        zip_ref.extractall(extract_dir)

    print("🔁 Tokenizer ve model yükleniyor...")
    tokenizer = AutoTokenizer.from_pretrained(os.path.join(extract_dir, "output"))
    model = AutoModelForCausalLM.from_pretrained(os.path.join(extract_dir, "output"))

    print("📚 RAG dataseti yükleniyor...")
    rag = load_dataset(RAG_DATA_REPO, data_files=RAG_DATA_FILE, split="train", token=HF_TOKEN)
    print(f"🔍 RAG boyutu: {len(rag)}")

    pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

# ✅ Uygulama başladığında modeli yükle
threading.Thread(target=setup_model, daemon=True).start()

# 🧘 Eğitim sonrası uygulama restart olmasın diye bekleme
if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=7860)