import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# تحميل النموذج
model = AutoModelForCausalLM.from_pretrained("./my_lora_model")
tokenizer = AutoTokenizer.from_pretrained("./my_lora_model")

def generate_text(instruction, input_text):
    prompt = f"Instruction: {instruction}\nInput: {input_text}\nOutput:"
    
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(
        **inputs,
        max_length=inputs['input_ids'].shape[1] + 150,
        do_sample=True,
        temperature=0.7,
        top_p=0.9,
        pad_token_id=tokenizer.eos_token_id
    )
    
    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    response = generated_text[len(prompt):].strip()
    return response

# واجهة Gradio
with gr.Blocks() as demo:
    gr.Markdown("# مساعد الذكاء الاصطناعي العربي")
    
    instruction = gr.Textbox(label="التعليمات", lines=2)
    input_text = gr.Textbox(label="النص", lines=5)
    btn = gr.Button("توليد النص")
    output = gr.Textbox(label="النتيجة", lines=10)
    
    btn.click(generate_text, [instruction, input_text], output)

demo.launch()