| """LexiAI – Space sunucusu (tek dosya). |
| Ana dizindeki LoRA adapter'ını Qwen2.5-7B-Instruct üzerine yükler, |
| index.html'i "/" adresinde yayınlar, yorumu "/comment" adresinden akıtır. |
| Token gerekmez. GPU'lu Space gerekir (en az ~24 GB VRAM, ya da 4-bit için QUANT=4).""" |
| import os, json, threading |
| import torch, uvicorn |
| from fastapi import FastAPI, Request |
| from fastapi.responses import FileResponse, StreamingResponse, JSONResponse |
| from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, BitsAndBytesConfig |
| from peft import PeftModel |
|
|
| HERE = os.path.dirname(os.path.abspath(__file__)) |
| BASE = "Qwen/Qwen2.5-7B-Instruct" |
| SYSTEM = ("Sen Türk hukuku konusunda yardımcı bir asistansın. Bir sınıflandırıcının sonucunu ve belge metnini alırsın. " |
| "Türkçe, en fazla 120 kelimeyle yorumla: 1) Belge ne ve sınıflandırma makul mü, " |
| "2) Dikkat edilmesi gereken noktalar/riskler, 3) Önerilen sonraki adım. Kısa maddeler kullan. " |
| "Kesin hukuki tavsiye verme.") |
|
|
| tok = AutoTokenizer.from_pretrained(HERE if os.path.exists(os.path.join(HERE, "tokenizer.json")) else BASE) |
| kw = dict(device_map="auto") |
| if os.environ.get("QUANT") == "4": |
| kw["quantization_config"] = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16) |
| else: |
| kw["torch_dtype"] = torch.bfloat16 |
| model = AutoModelForCausalLM.from_pretrained(BASE, **kw) |
| if os.path.exists(os.path.join(HERE, "adapter_config.json")): |
| model = PeftModel.from_pretrained(model, HERE) |
| model.eval() |
| lock = threading.Lock() |
|
|
| app = FastAPI() |
|
|
| @app.get("/") |
| def home(): |
| return FileResponse(os.path.join(HERE, "index.html")) |
|
|
| @app.get("/health") |
| def health(): |
| return {"ok": True} |
|
|
| @app.post("/comment") |
| async def comment(req: Request): |
| try: |
| d = await req.json() |
| except Exception: |
| return JSONResponse({"error": "Geçersiz istek"}, status_code=400) |
| info = str(d.get("info", ""))[:1000] |
| text = str(d.get("text", ""))[:3000] |
| msgs = [{"role": "system", "content": SYSTEM}, |
| {"role": "user", "content": info + "\n\nBelge metni:\n" + text}] |
| ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device) |
| streamer = TextIteratorStreamer(tok, skip_prompt=True, skip_special_tokens=True) |
|
|
| def work(): |
| with lock, torch.inference_mode(): |
| model.generate(input_ids=ids, attention_mask=torch.ones_like(ids), streamer=streamer, |
| max_new_tokens=350, do_sample=True, temperature=0.3, top_p=0.9) |
|
|
| threading.Thread(target=work, daemon=True).start() |
| return StreamingResponse((t for t in streamer), media_type="text/plain; charset=utf-8") |
|
|
| if __name__ == "__main__": |
| uvicorn.run(app, host="0.0.0.0", port=int(os.environ.get("PORT", 7860))) |
|
|
