Upload fine-tune inference test code

Dockerfile

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+FROM python:3.10-slim
+
+WORKDIR /app
+
+# Sistem araçları
+RUN apt-get update && apt-get install -y git unzip && apt-get clean
+
+# Hugging Face cache klasörleri ve yazma izinleri
+RUN mkdir -p /app/.cache /app/output /app/extracted && chmod -R 777 /app
+
+ENV HF_HOME=/app/.cache \
+    HF_DATASETS_CACHE=/app/.cache \
+    HF_HUB_CACHE=/app/.cache \
+    TRANSFORMERS_CACHE=/app/.cache \
+    OUTPUT_DIR=/app/output
+
+COPY requirements.txt ./
+RUN pip install --upgrade pip && pip install -r requirements.txt
+
+COPY fine_tune_inference_test.py ./
+
+CMD ["python", "fine_tune_inference_test.py"]

fine_tune_inference_test.py

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+import os
+import threading
+import uvicorn
+from fastapi import FastAPI, Request
+from pydantic import BaseModel
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+from datasets import load_dataset
+from fastapi.responses import JSONResponse
+
+# ✅ Sabitler
+HF_TOKEN = os.environ.get("HF_TOKEN")
+MODEL_BASE = "UcsTurkey/kanarya-750m-fixed"
+FINE_TUNE_ZIP = "trained_model_000_099.zip"  # 👈 Değiştirilebilir
+FINE_TUNE_REPO = "UcsTurkey/trained-zips"
+RAG_DATA_FILE = "merged_dataset_000_099.parquet" # 👈 Değiştirilebilir
+RAG_DATA_REPO = "UcsTurkey/turkish-general-culture-tokenized"
+
+# ✅ FastAPI app
+app = FastAPI()
+chat_history = []
+
+class Message(BaseModel):
+    user_input: str
+
+@app.get("/")
+def health():
+    return {"status": "ok"}
+
+@app.post("/chat")
+def chat(msg: Message):
+    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 ve model yükleniyor...")
+    tokenizer = AutoTokenizer.from_pretrained(os.path.join(extract_dir, "output"))
+    model = AutoModelForCausalLM.from_pretrained(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)}")
+
+    pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+# ✅ 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)

requirements.txt

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+fastapi
+uvicorn
+transformers
+torch
+huggingface_hub
+datasets
+pydantic