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import os, torch, threading, uvicorn, time, traceback, zipfile, random |
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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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HF_TOKEN = os.getenv("HF_TOKEN") |
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MODEL_BASE = "malhajar/Mistral-7B-Instruct-v0.2-turkish" |
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USE_FINE_TUNE = False |
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FINE_TUNE_REPO = "UcsTurkey/trained-zips" |
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FINE_TUNE_ZIP = "trained_model_000_009.zip" |
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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 = time.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 Instruct Chat</h2> |
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<textarea id=\"input\" rows=\"4\" cols=\"60\" placeholder=\"Write your instruction...\"></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 or tokenizer is None: |
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return {"error": "Model veya tokenizer henüz 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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messages = [{"role": "user", "content": user_input}] |
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inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True) |
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inputs = {k: v.to(model.device) for k, v in inputs.items()} |
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generate_args = { |
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"max_new_tokens": 512, |
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"return_dict_in_generate": True, |
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"output_scores": True, |
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"do_sample": USE_SAMPLING |
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} |
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if USE_SAMPLING: |
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generate_args.update({ |
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"temperature": 0.7, |
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"top_p": 0.9, |
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"top_k": 50 |
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}) |
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with torch.no_grad(): |
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output = model.generate(**inputs, **generate_args) |
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prompt_text = tokenizer.decode(inputs["input_ids"][0], skip_special_tokens=True) |
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decoded = tokenizer.decode(output.sequences[0], skip_special_tokens=True) |
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answer = decoded.replace(prompt_text, "").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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traceback.print_exc() |
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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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return device |
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def setup_model(): |
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global model, tokenizer |
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try: |
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device = detect_env() |
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dtype = torch.float32 |
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if USE_FINE_TUNE: |
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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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tokenizer = AutoTokenizer.from_pretrained(os.path.join(extract_dir, "output"), use_fast=False) |
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base_model = AutoModelForCausalLM.from_pretrained(MODEL_BASE, torch_dtype=dtype).to(device) |
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model = PeftModel.from_pretrained(base_model, os.path.join(extract_dir, "output")).to(device) |
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else: |
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log("🧠 Ana model indiriliyor...") |
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tokenizer = AutoTokenizer.from_pretrained(MODEL_BASE, use_fast=False) |
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model = AutoModelForCausalLM.from_pretrained(MODEL_BASE, torch_dtype=dtype).to(device) |
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tokenizer.pad_token = tokenizer.pad_token or tokenizer.eos_token |
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model.eval() |
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log("✅ Model başarıyla yüklendi.") |
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except Exception as e: |
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log(f"❌ Model yüklenirken hata: {e}") |
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traceback.print_exc() |
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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("===== 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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while True: |
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time.sleep(60) |
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