app.py

import gradio as gr
from llama_cpp import Llama
from huggingface_hub import hf_hub_download
import os

# Persistent Storage
PERSISTENT_DIR = "/data"
MODEL_DIR = os.path.join(PERSISTENT_DIR, "models")
os.makedirs(MODEL_DIR, exist_ok=True)

os.environ["HF_HOME"] = os.path.join(PERSISTENT_DIR, "hf_cache")
os.environ["HF_HUB_CACHE"] = os.path.join(PERSISTENT_DIR, "hf_cache")

# أحدث Quantization متوازنة (Q4_K_M ≈ 806 MB)
filename = "gemma-3-1b-it-Q4_K_M.gguf"

model_path = hf_hub_download(
    repo_id="unsloth/gemma-3-1b-it-GGUF",
    filename=filename,
    local_dir=MODEL_DIR,
    resume_download=True,
    force_download=False
)

print(f"✅ النموذج محمل بنجاح: {model_path}")

llm = Llama(
    model_path=model_path,
    n_ctx=8192,
    n_threads=4,          # زد إلى 6-8 إذا أردت
    n_gpu_layers=0,
    n_batch=512,
    verbose=False,
    chat_format="gemma"
)

def chat(message, history):
    messages = history + [{"role": "user", "content": message}]
    response = llm.create_chat_completion(
        messages=messages,
        temperature=0.7,
        max_tokens=1024,
        stop=["<end_of_turn>"]
    )
    return response["choices"][0]["message"]["content"]

demo = gr.ChatInterface(
    fn=chat,
    title="🧠 Gemma 3 1B IT (GGUF) - Docker",
    description="أحدث إصدار • Persistent Storage • يُحمّل مرة واحدة فقط",
    examples=["مرحبا، كيف حالك؟", "اكتب قصة قصيرة بالعربية عن تونس"],
    theme=gr.themes.Soft()
)

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)