Create app.py

app.py

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+"""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)))