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import os, torch, zipfile, threading, 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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from huggingface_hub import hf_hub_download |
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from datetime import datetime |
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import random |
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HF_TOKEN = os.environ.get("HF_TOKEN") |
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MODEL_BASE = "mistralai/Mistral-7B-Instruct-v0.2" |
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FINE_TUNE_ZIP = "trained_model_000_009.zip" |
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FINE_TUNE_REPO = "UcsTurkey/trained-zips" |
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USE_SAMPLING = False |
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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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def log(message): |
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timestamp = datetime.now().strftime("%H:%M:%S") |
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print(f"[{timestamp}] {message}") |
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os.sys.stdout.flush() |
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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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@app.get("/") |
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def health(): |
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return {"status": "ok"} |
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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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<body> |
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<h2>Mistral 7B Chat</h2> |
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<textarea id=\"input\" rows=\"4\" cols=\"60\" placeholder=\"SORU: ...\"></textarea><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 model, tokenizer |
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try: |
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if model is None: |
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return {"error": "Model yüklenmedi"} |
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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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prompt = f"SORU: {user_input}\nCEVAP:" |
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
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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=128, |
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do_sample=USE_SAMPLING, |
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temperature=0.7 if USE_SAMPLING else None, |
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top_p=0.9 if USE_SAMPLING else None, |
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top_k=50 if USE_SAMPLING else None, |
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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] |
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) |
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decoded = tokenizer.decode(output.sequences[0], skip_special_tokens=True) |
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answer = decoded[len(prompt):].strip() |
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if output.scores and len(output.scores) > 0: |
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first_token_score = output.scores[0][0] |
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if torch.isnan(first_token_score).any() or torch.isinf(first_token_score).any(): |
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log("⚠️ Geçersiz logit (NaN/Inf) tespit edildi.") |
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return {"answer": random.choice(FALLBACK_ANSWERS)} |
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max_score = torch.max(first_token_score).item() |
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log(f"🔍 İlk token skoru: {max_score:.4f}") |
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if max_score < CONFIDENCE_THRESHOLD: |
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answer = random.choice(FALLBACK_ANSWERS) |
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chat_history.append({"user": user_input, "bot": answer}) |
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log(f"Soru: {user_input} → Cevap: {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 hatası: {e}") |
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return {"error": str(e)} |
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def detect_env(): |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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supports_bf16 = torch.cuda.is_available() and torch.cuda.get_device_capability(0)[0] >= 8 |
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return device, supports_bf16 |
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def setup_model(): |
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global model, tokenizer |
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try: |
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log("📦 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_path = "/app/extracted" |
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os.makedirs(extract_path, exist_ok=True) |
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with zipfile.ZipFile(zip_path, "r") as zip_ref: |
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zip_ref.extractall(extract_path) |
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tokenizer = AutoTokenizer.from_pretrained(os.path.join(extract_path, "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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device, supports_bf16 = detect_env() |
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dtype = torch.bfloat16 if supports_bf16 else torch.float32 |
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log(f"🧠 Ortam: {device.upper()}, dtype: {dtype}") |
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base = AutoModelForCausalLM.from_pretrained(MODEL_BASE, torch_dtype=dtype).to(device) |
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peft = PeftModel.from_pretrained(base, os.path.join(extract_path, "output")) |
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model = peft.model.to(device) |
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model.eval() |
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log("✅ Model yüklendi.") |
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except Exception as e: |
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log(f"❌ Model setup hatası: {e}") |
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def run_server(): |
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log("🌐 Uvicorn başlatılıyor...") |
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uvicorn.run(app, host="0.0.0.0", port=7860) |
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log("🚀 Başlatılıyor...") |
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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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while True: |
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time.sleep(60) |
