Add application file

app.py

@@ -0,0 +1,85 @@
+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