| import gradio as gr |
| import torch |
| import sys |
| from io import StringIO |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| from peft import PeftModel |
|
|
| print("Loading Base Model...") |
| base_model_name = "Qwen/Qwen2.5-Coder-1.5B-Instruct" |
| tokenizer = AutoTokenizer.from_pretrained(base_model_name) |
| base_model = AutoModelForCausalLM.from_pretrained( |
| base_model_name, |
| torch_dtype=torch.float16, |
| device_map="auto" |
| ) |
|
|
| print("Loading Trained LoRA Weights...") |
| try: |
| model = PeftModel.from_pretrained(base_model, "MaxKio/lammo-coder-Max") |
| except Exception as e: |
| print(f"Warning: Could not load custom weights, using base model: {e}") |
| model = base_model |
|
|
| def run_python_code(code_str): |
| """بيئة معزولة بسيطة لتشغيل كود البايثون المولد وعرض النتيجة""" |
| old_stdout = sys.stdout |
| redirected_output = sys.stdout = StringIO() |
| try: |
| exec(code_str, {}) |
| sys.stdout = old_stdout |
| return redirected_output.getvalue() or "تم تنفيذ الكود بنجاح دون مخرجات (stdout)." |
| except Exception as e: |
| sys.stdout = old_stdout |
| return f"خطأ أثناء تشغيل الكود: {e}" |
|
|
| def respond(message, chat_history): |
| messages = [] |
| for val in chat_history: |
| if val[0]: |
| messages.append({"role": "user", "content": val[0]}) |
| if val[1]: |
| messages.append({"role": "assistant", "content": val[1]}) |
| |
| messages.append({"role": "user", "content": message}) |
| |
| text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
| model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
| |
| generated_ids = model.generate( |
| **model_inputs, |
| max_new_tokens=512, |
| do_sample=True, |
| temperature=0.7, |
| top_p=0.9 |
| ) |
| |
| generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)] |
| response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
| return response |
|
|
| with gr.Blocks(theme=gr.themes.Soft()) as demo: |
| gr.Markdown("""# <center> 🤖 AgentM - Coding Assistant 🤖 </center> |
| ### <center>مساعدك البرمجي الشخصي القوي مع إمكانية تشغيل الأكواد البرمجية مباشرة ومحاكاة بيئة المطورين</center>""") |
| |
| with gr.Tab("الدردشة البرمجية"): |
| chatbot = gr.ChatInterface(fn=respond) |
| |
| with gr.Tab("منصة تشغيل الأكواد (Coding Sandbox)"): |
| gr.Markdown("### اكتب كود بايثون هنا لتشغيله ورؤية النتيجة فوراً") |
| code_input = gr.Code(language="python", label="كود البايثون") |
| run_btn = gr.Button("تشغيل الكود ⚡", variant="primary") |
| code_output = gr.Textbox(label="مخرجات التنفيذ") |
| |
| run_btn.click(fn=run_python_code, inputs=code_input, outputs=code_output) |
|
|
| demo.launch() |
|
|
