import os
import threading
import uvicorn
from fastapi import FastAPI
from fastapi.responses import HTMLResponse
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from datasets import load_dataset
from peft import PeftModel
import torch
from huggingface_hub import hf_hub_download
import zipfile
from datetime import datetime
# ✅ Zamanlı log fonksiyonu (flush destekli)
def log(message):
timestamp = datetime.now().strftime("%H:%M:%S")
print(f"[{timestamp}] {message}")
os.sys.stdout.flush()
# ✅ Sabitler
HF_TOKEN = os.environ.get("HF_TOKEN")
MODEL_BASE = "UcsTurkey/kanarya-750m-fixed"
FINE_TUNE_ZIP = "trained_model_000_100.zip"
FINE_TUNE_REPO = "UcsTurkey/trained-zips"
RAG_DATA_FILE = "merged_dataset_000_100.parquet"
RAG_DATA_REPO = "UcsTurkey/turkish-general-culture-tokenized"
app = FastAPI()
chat_history = []
pipe = None # global text-generation pipeline
class Message(BaseModel):
user_input: str
@app.get("/", response_class=HTMLResponse)
def root():
return """
Fine-Tune Chat
📘 Fine-tune Chat Test
"""
@app.post("/chat")
def chat(msg: Message):
try:
global pipe
if pipe is None:
log("🚫 Hata: Model henüz yüklenmedi.")
return {"error": "Model yüklenmedi. Lütfen birkaç saniye sonra tekrar deneyin."}
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]()_