Update fine_tune_inference_test.py

fine_tune_inference_test.py

@@ -1,13 +1,22 @@
 import os
 import threading
 import uvicorn
-from fastapi import FastAPI, Request
-from fastapi.responses import HTMLResponse, JSONResponse
+from fastapi import FastAPI
+from fastapi.responses import HTMLResponse
 from pydantic import BaseModel
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 from datasets import load_dataset
 from peft import PeftModel
-import torch  # eksikse gerekli
+import torch
+from huggingface_hub import hf_hub_download
+import zipfile
+from datetime import datetime
+
+# ✅ Zamanlı log fonksiyonu (flush destekli)
+def log(message):
+    timestamp = datetime.now().strftime("%H:%M:%S")
+    print(f"[{timestamp}] {message}")
+    os.sys.stdout.flush()
 
 # ✅ Sabitler
 HF_TOKEN = os.environ.get("HF_TOKEN")
@@ -17,10 +26,9 @@ FINE_TUNE_REPO = "UcsTurkey/trained-zips"
 RAG_DATA_FILE = "merged_dataset_000_100.parquet"
 RAG_DATA_REPO = "UcsTurkey/turkish-general-culture-tokenized"
 
-# ✅ FastAPI app
 app = FastAPI()
 chat_history = []
-pipe = None  # ❗ Global olarak tanımlıyoruz
+pipe = None  # global text-generation pipeline
 
 class Message(BaseModel):
     user_input: str
@@ -53,68 +61,16 @@ def root():
 
 @app.post("/chat")
 def chat(msg: Message):
-    global pipe
-    if pipe is None:
-        return {"error": "Model henüz yüklenmedi, lütfen birkaç saniye sonra tekrar deneyin."}
-
-    user_input = msg.user_input.strip()
-    if not user_input:
-        return {"error": "Boş giriş"}
-
-    full_prompt = ""
-    for turn in chat_history:
-        full_prompt += f"Kullanıcı: {turn['user']}\nAsistan: {turn['bot']}\n"
-    full_prompt += f"Kullanıcı: {user_input}\nAsistan:"
-
-    result = pipe(full_prompt, max_new_tokens=200, do_sample=True, temperature=0.7)
-    answer = result[0]["generated_text"][len(full_prompt):].strip()
-
-    chat_history.append({"user": user_input, "bot": answer})
-    return {"answer": answer, "chat_history": chat_history}
-
-# ✅ Model ve RAG yükleme
-def setup_model():
-    global pipe
-    from huggingface_hub import hf_hub_download
-    import zipfile
-
-    print("📦 Fine-tune zip indiriliyor...")
-    zip_path = hf_hub_download(
-        repo_id=FINE_TUNE_REPO,
-        filename=FINE_TUNE_ZIP,
-        repo_type="model",
-        token=HF_TOKEN
-    )
-    extract_dir = "/app/extracted"
-    os.makedirs(extract_dir, exist_ok=True)
-    with zipfile.ZipFile(zip_path, "r") as zip_ref:
-        zip_ref.extractall(extract_dir)
-
-    print("🔁 Tokenizer yükleniyor...")
-    tokenizer = AutoTokenizer.from_pretrained(os.path.join(extract_dir, "output"))
-
-    print("🧠 Base model indiriliyor...")
-    base_model = AutoModelForCausalLM.from_pretrained(MODEL_BASE, torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32)
-
-    print("➕ LoRA adapter uygulanıyor...")
-    model = PeftModel.from_pretrained(base_model, os.path.join(extract_dir, "output"))
-
-    print("📚 RAG dataseti yükleniyor...")
-    rag = load_dataset(RAG_DATA_REPO, data_files=RAG_DATA_FILE, split="train", token=HF_TOKEN)
-    print(f"🔍 RAG boyutu: {len(rag)}")
-
-    # ✅ pipeline oluşturuluyor
-    pipe = pipeline(
-        "text-generation",
-        model=model,
-        tokenizer=tokenizer,
-        torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-        device=0 if torch.cuda.is_available() else -1
-    )
-
-# ✅ Uygulama başladığında modeli yükle
-threading.Thread(target=setup_model, daemon=True).start()
-
-# 🧘 Eğitim sonrası uygulama restart olmasın diye bekleme
-if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+    try:
+        global pipe
+        if pipe is None:
+            log("🚫 Hata: Model henüz yüklenmedi.")
+            return {"error": "Model yüklenmedi. Lütfen birkaç saniye sonra tekrar deneyin."}
+
+        user_input = msg.user_input.strip()
+        if not user_input:
+            return {"error": "Boş giriş"}
+
+        full_prompt = ""
+        for turn in chat_history:
+            full_prompt += f"Kullanıcı: {turn['user']}\nAsistan: {turn['bot]()_