app.py

import streamlit as st
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import time
import torch

# إعداد النموذج والمُرمز
@st.cache_resource
def load_model():
    tokenizer = AutoTokenizer.from_pretrained("Salesforce/codet5-base")
    model = AutoModelForSeq2SeqLM.from_pretrained("Salesforce/codet5-base")
    return tokenizer, model

tokenizer, model = load_model()

# واجهة المستخدم الرئيسية
st.title("AI Code Assistant")
st.sidebar.title("Options")

# الأقسام الرئيسية
section = st.sidebar.radio(
    "Choose a Section",
    ("Generate Code", "Train Model", "Prompt Engineer", "Optimize Model")
)

# 1. توليد الكود بناءً على وصف النص
if section == "Generate Code":
    st.header("Generate Code from Description")
    prompt = st.text_area("Enter your description:", "Write a Python function to reverse a string.")
    
    if st.button("Generate Code"):
        with st.spinner("Generating code..."):
            inputs = tokenizer(prompt, return_tensors="pt")
            outputs = model.generate(inputs["input_ids"], max_length=100)
            code = tokenizer.decode(outputs[0], skip_special_tokens=True)
            st.code(code, language="python")

# 2. تدريب النموذج
elif section == "Train Model":
    st.header("Train the Model")
    st.write("Upload your dataset to fine-tune the model.")
    
    uploaded_file = st.file_uploader("Upload Dataset (JSON/CSV):")
    if uploaded_file is not None:
        st.write("Dataset uploaded successfully!")
        # يمكنك إضافة الكود لتحليل البيانات أو عرض عينات منها هنا.

    if st.button("Start Training"):
        with st.spinner("Training the model..."):
            time.sleep(5)  # محاكاة وقت التدريب
            st.success("Model training completed!")

# 3. تحسين الـ Prompts
elif section == "Prompt Engineer":
    st.header("Prompt Engineering")
    st.write("Experiment with different prompts to get the best results.")
    
    prompt_input = st.text_area("Enter a prompt:", "Explain this code: def add(a, b): return a + b")
    if st.button("Test Prompt"):
        with st.spinner("Testing the prompt..."):
            inputs = tokenizer(prompt_input, return_tensors="pt")
            outputs = model.generate(inputs["input_ids"], max_length=100)
            response = tokenizer.decode(outputs[0], skip_special_tokens=True)
            st.write("Model Response:")
            st.code(response)

# 4. تحسين أداء النموذج
elif section == "Optimize Model":
    st.header("Optimize Model Performance")
    st.write("Adjust model parameters to improve performance.")

    learning_rate = st.slider("Learning Rate:", 1e-5, 1e-3, 1e-4, step=1e-5)
    batch_size = st.slider("Batch Size:", 1, 64, 8, step=1)
    epochs = st.slider("Number of Epochs:", 1, 10, 3)
    
    if st.button("Apply Settings"):
        st.write(f"Settings Applied:\n- Learning Rate: {learning_rate}\n- Batch Size: {batch_size}\n- Epochs: {epochs}")
        st.success("Optimization settings saved!")

---

### **الخطوة الثالثة: تشغيل التطبيق**

- قم بتشغيل التطبيق باستخدام الأمر:
  ```bash
  streamlit run app.py