app.py

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

# إعداد الموديل (تم تثبيت Qwen مباشرة لتجنب الأخطاء)
llm = Llama(
    model_path=hf_hub_download(
        repo_id="Qwen/Qwen2.5-1.5B-Instruct-GGUF",
        filename="qwen2.5-1.5b-instruct-q4_k_m.gguf",
    ),
    n_ctx=2048,
    n_gpu_layers=0, # تم جعله 0 ليعمل باستقرار على CPU
    verbose=False
) 

def generate_text(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    temp = ""
    
    # تعديل صيغة البرومبت لتناسب Qwen (ChatML Format)
    input_prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n"
    for interaction in history:
        input_prompt += f"<|im_start|>user\n{interaction[0]}<|im_end|>\n<|im_start|>assistant\n{interaction[1]}<|im_end|>\n"

    input_prompt += f"<|im_start|>user\n{message}<|im_end|>\n<|im_start|>assistant\n"

    output = llm(
        input_prompt,
        temperature=temperature,
        top_p=top_p,
        top_k=40,
        repeat_penalty=1.1,
        max_tokens=max_tokens,
        stop=[
            "<|im_end|>",
            "<|endoftext|>",
        ],
        stream=True,
    )
    for out in output:
        stream = copy.deepcopy(out)
        temp += stream["choices"][0]["text"]
        yield temp

demo = gr.ChatInterface(
    generate_text,
    title="Qwen 2.5 (1.5B) - Fast Server",
    description="Running Qwen 2.5 on CPU via llama.cpp",
    examples=[
        ['Hello, introduce yourself.'],
        ['Explain quantum physics simply.'],
        ['Write a python code to sum two numbers.']
    ],
    cache_examples=False,
    retry_btn=None,
    undo_btn="Delete Previous",
    clear_btn="Clear",
    additional_inputs=[
        gr.Textbox(value="You are a helpful AI assistant.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
)

if __name__ == "__main__":
    demo.launch()