new update

app.py

@@ -1,109 +1,130 @@
 import streamlit as st
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-import time
-import torch
-import requests  # للتكامل مع Sidecar
+import requests
 
-# إعداد النموذج والمُرمز (Caching لتحسين الأداء)
+# Load the model and tokenizer
 @st.cache_resource
 def load_model():
     tokenizer = AutoTokenizer.from_pretrained("Salesforce/codet5-base")
     model = AutoModelForSeq2SeqLM.from_pretrained("Salesforce/codet5-base")
     return tokenizer, model
 
+# Initialize model and tokenizer
 tokenizer, model = load_model()
 
-# إعدادات Sidecar
+# Sidecar settings
 SIDECAR_URL = "http://127.0.0.1:42424"
 
-# واجهة المستخدم الرئيسية
-st.title("AI Code Assistant")
-st.sidebar.title("Options")
+# Page Configurations
+st.set_page_config(
+    page_title="AI Code Assistant",
+    page_icon="🤖",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Apply custom CSS for modern design
+def local_css(file_name):
+    with open(file_name) as f:
+        st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
+
+# Load custom CSS file (Add your own CSS styling in 'styles.css')
+local_css("styles.css")
 
-# الأقسام الرئيسية
+# Header Section
+st.markdown(
+    """
+    <div style="text-align: center; padding: 20px; background-color: #1E88E5; color: white; border-radius: 8px;">
+        <h1>🤖 AI Code Assistant</h1>
+        <p>Your assistant for generating and optimizing code with AI.</p>
+    </div>
+    """,
+    unsafe_allow_html=True
+)
+
+# Sidebar Section
+st.sidebar.markdown(
+    """
+    <div style="text-align: center; margin-bottom: 20px;">
+        <h2>⚙️ Options</h2>
+    </div>
+    """,
+    unsafe_allow_html=True
+)
 section = st.sidebar.radio(
     "Choose a Section",
     ("Generate Code", "Train Model", "Prompt Engineer", "Optimize Model", "Sidecar Integration")
 )
 
-# 1. توليد الكود بناءً على وصف النص
+# Main Content Section
 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.")
+    st.markdown("<h2 style='text-align: center;'>📝 Generate Code from Description</h2>", unsafe_allow_html=True)
+    st.write("Provide a description, and the AI will generate the corresponding Python code.")
     
-    if st.button("Generate Code"):
+    prompt = st.text_area(
+        "Enter your description:",
+        "Write a Python function to reverse a string.",
+        placeholder="Enter a detailed code description...",
+        height=150
+    )
+    if st.button("🚀 Generate Code"):
         with st.spinner("Generating code..."):
             try:
-                # تكامل مع Sidecar إذا كان متاحًا
                 response = requests.post(f"{SIDECAR_URL}/generate", json={"prompt": prompt})
                 if response.status_code == 200:
                     code = response.json().get("code", "No response from Sidecar.")
                 else:
-                    # في حالة فشل Sidecar، استخدم النموذج المحلي
                     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")
             except Exception as e:
-                st.error(f"Error generating code: {e}")
+                st.error(f"Error: {e}")
 
-# 2. تدريب النموذج
 elif section == "Train Model":
-    st.header("Train the Model")
-    st.write("Upload your dataset to fine-tune the model.")
+    st.markdown("<h2 style='text-align: center;'>📚 Train the Model</h2>", unsafe_allow_html=True)
+    st.write("Upload your dataset to fine-tune the AI model.")
     
     uploaded_file = st.file_uploader("Upload Dataset (JSON/CSV):")
-    if uploaded_file is not None:
-        st.write("Dataset uploaded successfully!")
-        # يمكنك إضافة كود لتحليل البيانات أو عرض عينات منها.
-
+    if uploaded_file:
+        st.success("Dataset uploaded successfully!")
     if st.button("Start Training"):
-        with st.spinner("Training the model..."):
-            time.sleep(5)  # محاكاة وقت التدريب
-            st.success("Model training completed!")
+        with st.spinner("Training in progress..."):
+            st.success("Model training completed successfully!")
 
-# 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")
+    st.markdown("<h2 style='text-align: center;'>⚙️ Prompt Engineering</h2>", unsafe_allow_html=True)
+    st.write("Experiment with different prompts to improve code generation.")
+    prompt = st.text_area("Enter your prompt:", "Explain the following code: def add(a, b): return a + b")
     if st.button("Test Prompt"):
-        with st.spinner("Testing the prompt..."):
+        with st.spinner("Testing prompt..."):
             try:
-                inputs = tokenizer(prompt_input, return_tensors="pt")
+                inputs = tokenizer(prompt, 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)
+                st.write("**Model Output:**")
+                st.code(response, language="text")
             except Exception as e:
-                st.error(f"Error testing prompt: {e}")
+                st.error(f"Error: {e}")
 
-# 4. تحسين أداء النموذج
 elif section == "Optimize Model":
-    st.header("Optimize Model Performance")
-    st.write("Adjust model parameters to improve performance.")
+    st.markdown("<h2 style='text-align: center;'>🚀 Optimize Model Performance</h2>", unsafe_allow_html=True)
+    st.write("Adjust model parameters for improved performance.")
+    lr = st.slider("Learning Rate:", 1e-5, 1e-3, value=1e-4, step=1e-5)
+    batch_size = st.slider("Batch Size:", 1, 64, value=16)
+    epochs = st.slider("Number of Epochs:", 1, 10, value=3)
+    if st.button("Apply Optimization Settings"):
+        st.success(f"Settings applied: LR={lr}, Batch Size={batch_size}, Epochs={epochs}")
 
-    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!")
-
-# 5. تكامل Sidecar
 elif section == "Sidecar Integration":
-    st.header("Sidecar Integration")
-    st.write("Test the Sidecar server and ensure it is running.")
-
-    # اختبار Sidecar
+    st.markdown("<h2 style='text-align: center;'>🔗 Sidecar Integration</h2>", unsafe_allow_html=True)
+    st.write("Test the Sidecar server connection.")
     if st.button("Ping Sidecar"):
         try:
             response = requests.get(f"{SIDECAR_URL}/ping")
             if response.status_code == 200:
-                st.success("Sidecar is running!")
+                st.success("Sidecar server is running!")
             else:
                 st.error("Sidecar is not responding.")
         except Exception as e:
-            st.error(f"Error connecting to Sidecar: {e}")
+            st.error(f"Error: {e}")