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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 |
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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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import random |
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def log(message): |
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timestamp = datetime.now().strftime("%H:%M:%S") |
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try: |
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print(f"[{timestamp}] {message}") |
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except UnicodeEncodeError: |
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safe_message = message.encode("utf-8", errors="replace").decode("utf-8", errors="ignore") |
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print(f"[{timestamp}] {safe_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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CONFIDENCE_THRESHOLD = -1.5 |
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FALLBACK_ANSWERS = [ |
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"Bu konuda maalesef bilgim yok.", |
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"Ne demek istediğinizi tam anlayamadım.", |
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"Bu soruya şu an yanıt veremiyorum." |
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] |
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app = FastAPI() |
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chat_history = [] |
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model = None |
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tokenizer = None |
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class Message(BaseModel): |
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user_input: str |
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def detect_environment(): |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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supports_bfloat16 = False |
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gpu_name = "Yok" |
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if device == "cuda": |
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props = torch.cuda.get_device_properties(0) |
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gpu_name = props.name |
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major, _ = torch.cuda.get_device_capability(0) |
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supports_bfloat16 = major >= 8 |
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expected = { |
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"gpu": "Nvidia A100", |
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"min_vram": "16GB", |
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"cpu": "8 vCPU" |
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} |
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return { |
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"device": device, |
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"gpu_name": gpu_name, |
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"supports_bfloat16": supports_bfloat16, |
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"expected_config": expected |
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} |
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@app.get("/") |
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def health(): |
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return {"status": "ok"} |
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@app.get("/status") |
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def status(): |
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env = detect_environment() |
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return { |
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"device": env["device"], |
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"gpu": env["gpu_name"], |
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"supports_bfloat16": env["supports_bfloat16"], |
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"expected_config": env["expected_config"], |
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"note": "Sistem bu bilgilerle çalışıyor. bfloat16 desteklenmiyorsa performans sınırlı olabilir." |
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} |
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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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log(f"Kullanıcı mesajı alındı: {msg}") |
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global model, tokenizer |
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if model is None or tokenizer 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 = f"SORU: {user_input}\nCEVAP:" |
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log(f"Prompt: {full_prompt}") |
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inputs = tokenizer(full_prompt, return_tensors="pt") |
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inputs = {k: v.to(model.device) for k, v in inputs.items()} |
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log(f"Tokenizer input_ids: {inputs['input_ids']}") |
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log(f"input shape: {inputs['input_ids'].shape}") |
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try: |
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with torch.no_grad(): |
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output = model.generate( |
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**inputs, |
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max_new_tokens=200, |
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do_sample=True, |
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temperature=0.7, |
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top_k=50, |
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top_p=0.95, |
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return_dict_in_generate=True, |
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output_scores=True, |
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suppress_tokens=[tokenizer.pad_token_id] if tokenizer.pad_token_id is not None else None |
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) |
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except Exception as e: |
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log("generate() sırasında istisna oluştu, input dump ediliyor...") |
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log(f"input_ids: {inputs['input_ids']}") |
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log(f"attention_mask: {inputs.get('attention_mask', 'yok')}") |
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log(f"Hata tipi: {type(e).__name__} → {e}") |
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fallback = random.choice(FALLBACK_ANSWERS) |
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return {"answer": fallback, "chat_history": chat_history} |
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generated_ids = output.sequences[0] |
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generated_text = tokenizer.decode(generated_ids, skip_special_tokens=True) |
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answer = generated_text[len(full_prompt):].strip() |
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if output.scores and len(output.scores) > 0: |
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first_token_logit = output.scores[0][0] |
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if torch.isnan(first_token_logit).any() or torch.isinf(first_token_logit).any(): |
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log("Geçersiz logit (NaN/Inf) tespit edildi, fallback cevabı gönderiliyor.") |
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fallback = random.choice(FALLBACK_ANSWERS) |
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return {"answer": fallback, "chat_history": chat_history} |
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top_logit_score = torch.max(first_token_logit).item() |
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log(f"İlk token logit skoru: {top_logit_score:.4f}") |
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if top_logit_score < CONFIDENCE_THRESHOLD: |
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fallback = random.choice(FALLBACK_ANSWERS) |
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log(f"Düşük güven: fallback cevabı gönderiliyor: {fallback}") |
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answer = fallback |
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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 model, tokenizer |
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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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if tokenizer.pad_token is None: |
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tokenizer.pad_token = tokenizer.eos_token |
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env = detect_environment() |
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device = env["device"] |
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dtype = torch.bfloat16 if env["supports_bfloat16"] else (torch.float16 if device == "cuda" else torch.float32) |
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log(f"Ortam: GPU = {env['gpu_name']}, Device = {device}, bfloat16 destekleniyor mu: {env['supports_bfloat16']}") |
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log(f"Model {device.upper()} üzerinde {dtype} precision ile yüklenecek.") |
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log("Beklenen minimum sistem konfigürasyonu:") |
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log(f"- GPU: {env['expected_config']['gpu']}") |
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log(f"- GPU Bellek: {env['expected_config']['min_vram']}") |
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log(f"- CPU: {env['expected_config']['cpu']}") |
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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=dtype |
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).to(device) |
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log("LoRA adapter uygulanıyor...") |
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peft_model = PeftModel.from_pretrained( |
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base_model, |
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os.path.join(extract_dir, "output") |
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) |
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model = peft_model.model.to(device) |
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model.eval() |
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log(f"Model başarıyla yüklendi. dtype={next(model.parameters()).dtype}, device={next(model.parameters()).device}") |
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except Exception as e: |
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log(f"setup_model() sırasında hata oluştu: {e}") |
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def run_server(): |
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log("Uvicorn sunucusu başlatılıyor...") |
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uvicorn.run(app, host="0.0.0.0", port=7860) |
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log("===== Application Startup =====") |
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threading.Thread(target=setup_model, daemon=True).start() |
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threading.Thread(target=run_server, daemon=True).start() |
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log("Model yükleniyor, istekler ve API sunucusu hazırlanıyor...") |
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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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