app.py

import streamlit as st
import streamlit.components.v1 as components
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import json
from streamlit.runtime.scriptrunner import add_script_run_ctx
import threading

# تكوين النموذج والتوكنايزر
@st.cache_resource
def load_model():
    tokenizer = AutoTokenizer.from_pretrained("amd/AMD-OLMo-1B")
    model = AutoModelForCausalLM.from_pretrained(
        "amd/AMD-OLMo-1B",
        torch_dtype=torch.float16,
        device_map="auto"
    )
    return model, tokenizer

def generate_response(prompt, model, tokenizer):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_length=200,
            num_return_sequences=1,
            temperature=0.7,
            top_p=0.9,
            repetition_penalty=1.2,
            pad_token_id=tokenizer.eos_token_id
        )
    
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response.replace(prompt, "").strip()

def main():
    st.set_page_config(
        page_title="سبيدي",
        page_icon="💬",
        layout="wide"
    )
    
    # إخفاء عناصر Streamlit الافتراضية
    hide_streamlit_style = """
        <style>
        #MainMenu {visibility: hidden;}
        footer {visibility: hidden;}
        header {visibility: hidden;}
        </style>
    """
    st.markdown(hide_streamlit_style, unsafe_allow_html=True)
    
    # تحميل النموذج والتوكنايزر
    model, tokenizer = load_model()
    
    # قراءة ملف HTML
    def read_html():
        with open('index.html', 'r', encoding='utf-8') as file:
            return file.read()
    
    # معالجة الرسائل الواردة من JavaScript
    def handle_message(message_data):
        try:
            data = json.loads(message_data)
            user_message = data.get('message', '')
            
            if user_message:
                response = generate_response(user_message, model, tokenizer)
                return {"response": response}
            
            return {"response": "عذراً، لم أفهم رسالتك"}
            
        except Exception as e:
            return {"response": f"عذراً، حدث خطأ: {str(e)}"}
    
    # تكوين معالج الرسائل
    def message_handler(message_data):
        ctx = add_script_run_ctx()
        response = handle_message(message_data)
        ctx.enqueue(json.dumps(response))
    
    # عرض الواجهة
    components.html(
        read_html(),
        height=800,
        on_message=message_handler
    )

if __name__ == "__main__":
    main()