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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, Request |
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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, pipeline |
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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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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_000_100.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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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("/", 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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global pipe |
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if pipe is None: |
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return {"error": "Model henüz 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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return {"answer": answer, "chat_history": chat_history} |
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def setup_model(): |
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global pipe |
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from huggingface_hub import hf_hub_download |
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import zipfile |
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print("📦 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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print("🔁 Tokenizer yükleniyor...") |
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tokenizer = AutoTokenizer.from_pretrained(os.path.join(extract_dir, "output")) |
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print("🧠 Base model indiriliyor...") |
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base_model = AutoModelForCausalLM.from_pretrained(MODEL_BASE, torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32) |
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print("➕ LoRA adapter uygulanıyor...") |
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model = PeftModel.from_pretrained(base_model, os.path.join(extract_dir, "output")) |
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print("📚 RAG dataseti yükleniyor...") |
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rag = load_dataset(RAG_DATA_REPO, data_files=RAG_DATA_FILE, split="train", token=HF_TOKEN) |
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print(f"🔍 RAG boyutu: {len(rag)}") |
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pipe = pipeline( |
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"text-generation", |
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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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threading.Thread(target=setup_model, daemon=True).start() |
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if __name__ == "__main__": |
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uvicorn.run(app, host="0.0.0.0", port=7860) |
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