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import os |
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import threading |
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import uvicorn |
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from fastapi import FastAPI |
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from fastapi.responses import HTMLResponse, JSONResponse |
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from pydantic import BaseModel |
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from transformers import AutoTokenizer, AutoModelForCausalLM, TextGenerationPipeline |
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from datasets import load_dataset |
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from peft import PeftModel |
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import torch |
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from huggingface_hub import hf_hub_download |
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import zipfile |
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from datetime import datetime |
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def log(message): |
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timestamp = datetime.now().strftime("%H:%M:%S") |
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print(f"[{timestamp}] {message}") |
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os.sys.stdout.flush() |
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HF_TOKEN = os.environ.get("HF_TOKEN") |
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MODEL_BASE = "UcsTurkey/kanarya-750m-fixed" |
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FINE_TUNE_ZIP = "trained_model_002_005.zip" |
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FINE_TUNE_REPO = "UcsTurkey/trained-zips" |
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RAG_DATA_FILE = "merged_dataset_000_100.parquet" |
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RAG_DATA_REPO = "UcsTurkey/turkish-general-culture-tokenized" |
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USE_RAG = False |
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app = FastAPI() |
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chat_history = [] |
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pipe = None |
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class Message(BaseModel): |
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user_input: str |
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@app.get("/health") |
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def health(): |
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return {"status": "ok"} |
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@app.get("/start", response_class=HTMLResponse) |
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def root(): |
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return """ |
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<html> |
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<head><title>Fine-Tune Chat</title></head> |
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<body> |
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<h2>📘 Fine-tune Chat Test</h2> |
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<textarea id=\"input\" rows=\"4\" cols=\"60\" placeholder=\"Bir şeyler yaz...\"></textarea><br><br> |
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<button onclick=\"send()\">Gönder</button> |
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<pre id=\"output\"></pre> |
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<script> |
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async function send() { |
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const input = document.getElementById(\"input\").value; |
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const res = await fetch("/chat", { |
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method: "POST", |
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headers: { "Content-Type": "application/json" }, |
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body: JSON.stringify({ user_input: input }) |
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}); |
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const data = await res.json(); |
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document.getElementById("output").innerText = data.answer || data.error || "Hata oluştu."; |
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} |
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</script> |
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</body> |
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</html> |
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""" |
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@app.post("/chat") |
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def chat(msg: Message): |
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try: |
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global pipe |
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if pipe is None: |
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log("🚫 Hata: Model henüz yüklenmedi.") |
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return {"error": "Model yüklenmedi. Lütfen birkaç saniye sonra tekrar deneyin."} |
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user_input = msg.user_input.strip() |
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if not user_input: |
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return {"error": "Boş giriş"} |
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full_prompt = "" |
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for turn in chat_history: |
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full_prompt += f"Kullanıcı: {turn['user']}\nAsistan: {turn['bot']}\n" |
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full_prompt += f"Kullanıcı: {user_input}\nAsistan:" |
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result = pipe(full_prompt, max_new_tokens=200, do_sample=True, temperature=0.7) |
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answer = result[0]["generated_text"][len(full_prompt):].strip() |
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chat_history.append({"user": user_input, "bot": answer}) |
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log(f"🗨️ Soru: {user_input} → Yanıt: {answer[:60]}...") |
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return {"answer": answer, "chat_history": chat_history} |
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except Exception as e: |
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log(f"❌ /chat sırasında hata oluştu: {e}") |
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return {"error": str(e)} |
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def setup_model(): |
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try: |
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global pipe |
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log("📦 Fine-tune zip indiriliyor...") |
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zip_path = hf_hub_download( |
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repo_id=FINE_TUNE_REPO, |
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filename=FINE_TUNE_ZIP, |
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repo_type="model", |
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token=HF_TOKEN |
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) |
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extract_dir = "/app/extracted" |
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os.makedirs(extract_dir, exist_ok=True) |
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with zipfile.ZipFile(zip_path, "r") as zip_ref: |
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zip_ref.extractall(extract_dir) |
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log("📂 Zip başarıyla açıldı.") |
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log("🔁 Tokenizer yükleniyor...") |
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tokenizer = AutoTokenizer.from_pretrained(os.path.join(extract_dir, "output")) |
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log("🧠 Base model indiriliyor...") |
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base_model = AutoModelForCausalLM.from_pretrained( |
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MODEL_BASE, |
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torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32 |
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) |
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log("➕ LoRA adapter uygulanıyor...") |
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peft_model = PeftModel.from_pretrained(base_model, os.path.join(extract_dir, "output")) |
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if USE_RAG: |
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log("📚 RAG dataseti yükleniyor...") |
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rag = load_dataset( |
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RAG_DATA_REPO, |
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data_files=RAG_DATA_FILE, |
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split="train", |
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token=HF_TOKEN |
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) |
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log(f"🔍 RAG boyutu: {len(rag)}") |
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log("🚀 Pipeline oluşturuluyor...") |
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pipe = TextGenerationPipeline( |
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model=peft_model.model, |
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tokenizer=tokenizer, |
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torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, |
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device=0 if torch.cuda.is_available() else -1 |
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) |
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log("✅ Model ve pipeline başarıyla yüklendi.") |
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except Exception as e: |
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log(f"❌ setup_model() sırasında hata oluştu: {e}") |
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threading.Thread(target=setup_model, daemon=True).start() |
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log("⌛ Model yükleniyor, istekler için hazır olunacak...") |
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while True: |
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try: |
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import time |
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time.sleep(60) |
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except Exception as e: |
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log(f"❌ Ana bekleme döngüsünde hata: {e}") |
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