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import os
import threading
import uvicorn
from fastapi import FastAPI, Request
from fastapi.responses import HTMLResponse
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("/", response_class=HTMLResponse)
def root():
return """
<html>
<head><title>Fine-Tune Chat</title></head>
<body>
<h2>📘 Fine-tune Chat Test</h2>
<textarea id="input" rows="4" cols="60" placeholder="Bir şeyler yaz..."></textarea><br><br>
<button onclick="send()">Gönder</button>
<pre id="output"></pre>
<script>
async function send() {
const input = document.getElementById("input").value;
const res = await fetch("/chat", {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({ user_input: input })
});
const data = await res.json();
document.getElementById("output").innerText = data.answer || data.error || "Hata oluştu.";
}
</script>
</body>
</html>
"""
@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)
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