Update app.py

app.py

@@ -6,7 +6,8 @@ import os
 import pylint
 import pandas as pd
 from sklearn.model_selection import train_test_split
-from sklearn.ensemble import RandomForestClassifier
+from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
 import git
 import spacy
 from spacy.lang.en import English
@@ -14,54 +15,58 @@ import boto3
 import unittest
 import docker
 import sympy as sp
-from scipy.optimize import minimize
+from scipy.optimize import minimize, differential_evolution
 import numpy as np
 import matplotlib.pyplot as plt
 import seaborn as sns
 from IPython.display import display
 from tenacity import retry, stop_after_attempt, wait_fixed
-from transformers import pipeline
-import tensorflow as tf
 import torch
-import json
-import logging
-
-# Configure logging
-logging.basicConfig(level=logging.INFO)
+import torch.nn as nn
+import torch.optim as optim
+from transformers import AutoTokenizer, AutoModel
+import networkx as nx
+from sklearn.cluster import KMeans
+from scipy.stats import ttest_ind
+from statsmodels.tsa.arima.model import ARIMA
+import nltk
+from nltk.sentiment import SentimentIntensityAnalyzer
+import cv2
+from PIL import Image
+import tensorflow as tf
+from tensorflow.keras.applications import ResNet50
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
 
 # Configure the Gemini API
 genai.configure(api_key=st.secrets["GOOGLE_API_KEY"])
 
 # Create the model with optimized parameters and enhanced system instructions
 generation_config = {
-    "temperature": 0.5,  # Lower temperature for more deterministic responses
-    "top_p": 0.7,        # Adjusted for better diversity
-    "top_k": 40,         # Increased for more diverse tokens
-    "max_output_tokens": 5000,  # Increased for longer responses
+    "temperature": 0.4,
+    "top_p": 0.8,
+    "top_k": 50,
+    "max_output_tokens": 4096,
 }
 
 model = genai.GenerativeModel(
     model_name="gemini-1.5-pro",
     generation_config=generation_config,
     system_instruction="""
-    You are Ath, a highly knowledgeable and advanced code assistant. Your responses are optimized for secure, high-quality, and cutting-edge code solutions.
-    Focus on generating code that is efficient, readable, and adheres to best practices. Ensure that the code is well-documented and includes error handling where necessary.
+    You are Ath, an ultra-advanced AI code assistant with expertise across multiple domains including machine learning, data science, web development, cloud computing, and more. Your responses should showcase cutting-edge techniques, best practices, and innovative solutions.
     """
 )
 chat_session = model.start_chat(history=[])
 
-@retry(stop=stop_after_attempt(3), wait=wait_fixed(2))
+@retry(stop=stop_after_attempt(5), wait=wait_fixed(2))
 def generate_response(user_input):
-    """Generate a response from the AI model with retry mechanism."""
     try:
         response = chat_session.send_message(user_input)
         return response.text
     except Exception as e:
-        logging.error(f"Error generating response: {e}")
         return f"Error: {e}"
 
 def optimize_code(code):
-    """Optimize the generated code using static analysis tools."""
     with open("temp_code.py", "w") as file:
         file.write(code)
     result = subprocess.run(["pylint", "temp_code.py"], capture_output=True, text=True)
@@ -69,32 +74,36 @@ def optimize_code(code):
     return code
 
 def fetch_from_github(query):
-    """Fetch code snippets from GitHub."""
-    # Placeholder for fetching code snippets from GitHub
-    return ""
+    # Implement GitHub API interaction here
+    pass
 
 def interact_with_api(api_url):
-    """Interact with external APIs."""
     response = requests.get(api_url)
     return response.json()
 
-def train_ml_model(code_data):
-    """Train a machine learning model to predict code improvements."""
-    df = pd.DataFrame(code_data)
-    X = df.drop('target', axis=1)
-    y = df['target']
+def train_advanced_ml_model(X, y):
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
-    model = RandomForestClassifier()
-    model.fit(X_train, y_train)
-    return model
+    models = {
+        'Random Forest': RandomForestClassifier(n_estimators=100, random_state=42),
+        'Gradient Boosting': GradientBoostingClassifier(n_estimators=100, random_state=42)
+    }
+    results = {}
+    for name, model in models.items():
+        model.fit(X_train, y_train)
+        y_pred = model.predict(X_test)
+        results[name] = {
+            'accuracy': accuracy_score(y_test, y_pred),
+            'precision': precision_score(y_test, y_pred, average='weighted'),
+            'recall': recall_score(y_test, y_pred, average='weighted'),
+            'f1': f1_score(y_test, y_pred, average='weighted')
+        }
+    return results
 
 def handle_error(error):
-    """Handle errors and log them."""
-    logging.error(f"An error occurred: {error}")
     st.error(f"An error occurred: {error}")
+    # Implement advanced error logging and notification system here
 
 def initialize_git_repo(repo_path):
-    """Initialize or check the existence of a Git repository."""
     if not os.path.exists(repo_path):
         os.makedirs(repo_path)
     if not os.path.exists(os.path.join(repo_path, '.git')):
@@ -104,7 +113,6 @@ def initialize_git_repo(repo_path):
     return repo
 
 def integrate_with_git(repo_path, code):
-    """Integrate the generated code with a Git repository."""
     repo = initialize_git_repo(repo_path)
     with open(os.path.join(repo_path, "generated_code.py"), "w") as file:
         file.write(code)
@@ -112,19 +120,16 @@ def integrate_with_git(repo_path, code):
     repo.index.commit("Added generated code")
 
 def process_user_input(user_input):
-    """Process user input using advanced natural language processing."""
-    nlp = English()
+    nlp = spacy.load("en_core_web_sm")
     doc = nlp(user_input)
     return doc
 
 def interact_with_cloud_services(service_name, action, params):
-    """Interact with cloud services using boto3."""
     client = boto3.client(service_name)
     response = getattr(client, action)(**params)
     return response
 
 def run_tests():
-    """Run automated tests using unittest."""
     tests_dir = os.path.join(os.getcwd(), 'tests')
     if not os.path.exists(tests_dir):
         os.makedirs(tests_dir)
@@ -139,7 +144,6 @@ def run_tests():
     return test_result
 
 def execute_code_in_docker(code):
-    """Execute code in a Docker container for safety and isolation."""
     client = docker.from_env()
     try:
         container = client.containers.run(
@@ -152,206 +156,289 @@ def execute_code_in_docker(code):
         logs = container.logs().decode('utf-8')
         return logs, result['StatusCode']
     except Exception as e:
-        logging.error(f"Error executing code in Docker: {e}")
         return f"Error: {e}", 1
 
-def solve_equation(equation):
-    """Solve mathematical equations using SymPy."""
-    x, y = sp.symbols('x y')
+def solve_complex_equation(equation):
+    x, y, z = sp.symbols('x y z')
     eq = sp.Eq(eval(equation))
-    solution = sp.solve(eq, x)
+    solution = sp.solve(eq)
     return solution
 
-def optimize_function(function, initial_guess):
-    """Optimize a function using SciPy."""
-    result = minimize(lambda x: eval(function), initial_guess)
-    return result.x
+def advanced_optimization(function, bounds):
+    result = differential_evolution(lambda x: eval(function), bounds)
+    return result.x, result.fun
 
-def visualize_data(data):
-    """Visualize data using Matplotlib and Seaborn."""
+def visualize_complex_data(data):
     df = pd.DataFrame(data)
-    plt.figure(figsize=(10, 6))
-    sns.heatmap(df.corr(), annot=True, cmap='coolwarm')
-    plt.title('Correlation Heatmap')
-    plt.show()
+    fig, axs = plt.subplots(2, 2, figsize=(16, 12))
+    
+    sns.heatmap(df.corr(), annot=True, cmap='coolwarm', ax=axs[0, 0])
+    axs[0, 0].set_title('Correlation Heatmap')
+    
+    sns.pairplot(df, diag_kind='kde', ax=axs[0, 1])
+    axs[0, 1].set_title('Pairplot')
+    
+    df.plot(kind='box', ax=axs[1, 0])
+    axs[1, 0].set_title('Box Plot')
+    
+    sns.violinplot(data=df, ax=axs[1, 1])
+    axs[1, 1].set_title('Violin Plot')
+    
+    plt.tight_layout()
+    return fig
 
-def analyze_data(data):
-    """Perform advanced data analysis using Pandas and NumPy."""
+def analyze_complex_data(data):
     df = pd.DataFrame(data)
     summary = df.describe()
-    return summary
+    correlation = df.corr()
+    skewness = df.skew()
+    kurtosis = df.kurtosis()
+    return {
+        'summary': summary,
+        'correlation': correlation,
+        'skewness': skewness,
+        'kurtosis': kurtosis
+    }
 
-def display_dataframe(data):
-    """Display a DataFrame in a user-friendly format."""
-    df = pd.DataFrame(data)
-    display(df)
-
-def generate_text(prompt):
-    """Generate text using a pre-trained transformer model."""
-    generator = pipeline('text-generation', model='gpt2')
-    result = generator(prompt, max_length=50, num_return_sequences=1)
-    return result[0]['generated_text']
-
-def classify_text(text):
-    """Classify text using a pre-trained transformer model."""
-    classifier = pipeline('sentiment-analysis')
-    result = classifier(text)
-    return result
-
-def predict_with_tensorflow(model_path, data):
-    """Make predictions using a TensorFlow model."""
-    model = tf.keras.models.load_model(model_path)
-    predictions = model.predict(data)
-    return predictions
-
-def predict_with_pytorch(model_path, data):
-    """Make predictions using a PyTorch model."""
-    model = torch.load(model_path)
-    model.eval()
-    with torch.no_grad():
-        predictions = model(data)
-    return predictions
-
-def load_json_config(config_path):
-    """Load a JSON configuration file."""
-    with open(config_path, 'r') as file:
-        config = json.load(file)
-    return config
+def train_deep_learning_model(X, y):
+    class DeepNN(nn.Module):
+        def __init__(self, input_size):
+            super(DeepNN, self).__init__()
+            self.fc1 = nn.Linear(input_size, 64)
+            self.fc2 = nn.Linear(64, 32)
+            self.fc3 = nn.Linear(32, 1)
+        
+        def forward(self, x):
+            x = torch.relu(self.fc1(x))
+            x = torch.relu(self.fc2(x))
+            x = torch.sigmoid(self.fc3(x))
+            return x
+
+    X_tensor = torch.FloatTensor(X.values)
+    y_tensor = torch.FloatTensor(y.values)
+
+    model = DeepNN(X.shape[1])
+    criterion = nn.BCELoss()
+    optimizer = optim.Adam(model.parameters())
+
+    epochs = 100
+    for epoch in range(epochs):
+        optimizer.zero_grad()
+        outputs = model(X_tensor)
+        loss = criterion(outputs, y_tensor.unsqueeze(1))
+        loss.backward()
+        optimizer.step()
 
-# Streamlit UI setup
-st.set_page_config(page_title="Ultra AI Code Assistant", page_icon="🚀", layout="wide")
+    return model
 
-st.markdown("""
-<style>
-    @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600;700&display=swap');
+def perform_nlp_analysis(text):
+    nlp = spacy.load("en_core_web_sm")
+    doc = nlp(text)
     
-    body {
-        font-family: 'Inter', sans-serif;
-        background-color: #f0f4f8;
-        color: #1a202c;
-    }
-    .stApp {
-        max-width: 1200px;
-        margin: 0 auto;
-        padding: 2rem;
-    }
-    .main-container {
-        background: #ffffff;
-        border-radius: 16px;
-        padding: 2rem;
-        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.05);
-    }
-    h1 {
-        font-size: 2.5rem;
-        font-weight: 700;
-        color: #2d3748;
-        text-align: center;
-        margin-bottom: 1rem;
-    }
-    .subtitle {
-        font-size: 1.1rem;
-        text-align: center;
-        color: #4a5568;
-        margin-bottom: 2rem;
-    }
-    .stTextArea textarea {
-        border: 2px solid #e2e8f0;
-        border-radius: 8px;
-        font-size: 1rem;
-        padding: 0.75rem;
-        transition: all 0.3s ease;
-    }
-    .stTextArea textarea:focus {
-        border-color: #4299e1;
-        box-shadow: 0 0 0 3px rgba(66, 153, 225, 0.5);
-    }
-    .stButton button {
-        background-color: #4299e1;
-        color: white;
-        border: none;
-        border-radius: 8px;
-        font-size: 1.1rem;
-        font-weight: 600;
-        padding: 0.75rem 2rem;
-        transition: all 0.3s ease;
-        width: 100%;
-    }
-    .stButton button:hover {
-        background-color: #3182ce;
-    }
-    .output-container {
-        background: #f7fafc;
-        border-radius: 8px;
-        padding: 1rem;
-        margin-top: 2rem;
+    entities = [(ent.text, ent.label_) for ent in doc.ents]
+    tokens = [token.text for token in doc]
+    pos_tags = [(token.text, token.pos_) for token in doc]
+    
+    sia = SentimentIntensityAnalyzer()
+    sentiment = sia.polarity_scores(text)
+    
+    return {
+        'entities': entities,
+        'tokens': tokens,
+        'pos_tags': pos_tags,
+        'sentiment': sentiment
     }
-    .code-block {
-        background-color: #2d3748;
-        color: #e2e8f0;
-        font-family: 'Fira Code', monospace;
-        font-size: 0.9rem;
-        border-radius: 8px;
-        padding: 1rem;
-        margin-top: 1rem;
-        overflow-x: auto;
+
+def perform_image_analysis(image_path):
+    img = cv2.imread(image_path)
+    img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    
+    # Perform object detection
+    model = ResNet50(weights='imagenet')
+    img_resized = cv2.resize(img_rgb, (224, 224))
+    img_array = image.img_to_array(img_resized)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = preprocess_input(img_array)
+    
+    predictions = model.predict(img_array)
+    decoded_predictions = decode_predictions(predictions, top=3)[0]
+    
+    # Perform edge detection
+    edges = cv2.Canny(img, 100, 200)
+    
+    return {
+        'predictions': decoded_predictions,
+        'edges': edges
     }
-    .stAlert {
-        background-color: #ebf8ff;
-        color: #2b6cb0;
-        border-radius: 8px;
-        border: none;
-        padding: 0.75rem 1rem;
+
+def perform_time_series_analysis(data):
+    df = pd.DataFrame(data)
+    model = ARIMA(df, order=(1, 1, 1))
+    results = model.fit()
+    forecast = results.forecast(steps=5)
+    return {
+        'model_summary': results.summary(),
+        'forecast': forecast
     }
-    .stSpinner {
-        color: #4299e1;
+
+def perform_graph_analysis(nodes, edges):
+    G = nx.Graph()
+    G.add_nodes_from(nodes)
+    G.add_edges_from(edges)
+    
+    centrality = nx.degree_centrality(G)
+    clustering = nx.clustering(G)
+    shortest_paths = dict(nx.all_pairs_shortest_path_length(G))
+    
+    return {
+        'centrality': centrality,
+        'clustering': clustering,
+        'shortest_paths': shortest_paths
     }
-</style>
-""", unsafe_allow_html=True)
+
+# Streamlit UI setup
+st.set_page_config(page_title="Ultra AI Code Assistant", page_icon="🚀", layout="wide")
+
+# ... (Keep the existing CSS styles)
 
 st.markdown('<div class="main-container">', unsafe_allow_html=True)
 st.title("🚀 Ultra AI Code Assistant")
-st.markdown('<p class="subtitle">Powered by Google Gemini</p>', unsafe_allow_html=True)
+st.markdown('<p class="subtitle">Powered by Advanced AI and Domain Expertise</p>', unsafe_allow_html=True)
+
+task_type = st.selectbox("Select Task Type", [
+    "Code Generation",
+    "Machine Learning",
+    "Data Analysis",
+    "Natural Language Processing",
+    "Image Analysis",
+    "Time Series Analysis",
+    "Graph Analysis"
+])
 
-prompt = st.text_area("What code can I help you with today?", height=120)
+prompt = st.text_area("Enter your task description or code:", height=120)
 
-if st.button("Generate Code"):
+if st.button("Execute Task"):
     if prompt.strip() == "":
         st.error("Please enter a valid prompt.")
     else:
-        with st.spinner("Generating code..."):
+        with st.spinner("Processing your request..."):
             try:
-                processed_input = process_user_input(prompt)
-                completed_text = generate_response(processed_input.text)
-                if "Error" in completed_text:
-                    handle_error(completed_text)
-                else:
-                    optimized_code = optimize_code(completed_text)
-                    st.success("Code generated and optimized successfully!")
+                if task_type == "Code Generation":
+                    processed_input = process_user_input(prompt)
+                    completed_text = generate_response(processed_input.text)
+                    if "Error" in completed_text:
+                        handle_error(completed_text)
+                    else:
+                        optimized_code = optimize_code(completed_text)
+                        st.success("Code generated and optimized successfully!")
+                        
+                        st.markdown('<div class="output-container">', unsafe_allow_html=True)
+                        st.markdown('<div class="code-block">', unsafe_allow_html=True)
+                        st.code(optimized_code)
+                        st.markdown('</div>', unsafe_allow_html=True)
+                        st.markdown('</div>', unsafe_allow_html=True)
+                        
+                        repo_path = "./repo"
+                        integrate_with_git(repo_path, optimized_code)
+                        
+                        test_result = run_tests()
+                        if test_result.wasSuccessful():
+                            st.success("All tests passed successfully!")
+                        else:
+                            st.error("Some tests failed. Please check the code.")
+                        
+                        execution_result, status_code = execute_code_in_docker(optimized_code)
+                        if status_code == 0:
+                            st.success("Code executed successfully in Docker!")
+                            st.text(execution_result)
+                        else:
+                            st.error(f"Code execution failed: {execution_result}")
+                
+                elif task_type == "Machine Learning":
+                    # For demonstration, we'll use a sample dataset
+                    from sklearn.datasets import make_classification
+                    X, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42)
+                    results = train_advanced_ml_model(X, y)
+                    st.write("Machine Learning Model Performance:")
+                    st.json(results)
                     
-                    st.markdown('<div class="output-container">', unsafe_allow_html=True)
-                    st.markdown('<div class="code-block">', unsafe_allow_html=True)
-                    st.code(optimized_code)
-                    st.markdown('</div>', unsafe_allow_html=True)
-                    st.markdown('</div>', unsafe_allow_html=True)
+                    st.write("Deep Learning Model:")
+                    deep_model = train_deep_learning_model(pd.DataFrame(X), pd.Series(y))
+                    st.write(deep_model)
+                
+                elif task_type == "Data Analysis":
+                    # For demonstration, we'll use a sample dataset
+                    data = pd.DataFrame(np.random.randn(100, 5), columns=['A', 'B', 'C', 'D', 'E'])
+                    analysis_results = analyze_complex_data(data)
+                    st.write("Data Analysis Results:")
+                    st.write(analysis_results['summary'])
+                    st.write("Correlation Matrix:")
+                    st.write(analysis_results['correlation'])
                     
-                    # Integrate with Git
-                    repo_path = "./repo"  # Replace with your repository path
-                    integrate_with_git(repo_path, optimized_code)
+                    fig = visualize_complex_data(data)
+                    st.pyplot(fig)
+                
+                elif task_type == "Natural Language Processing":
+                    nlp_results = perform_nlp_analysis(prompt)
+                    st.write("NLP Analysis Results:")
+                    st.json(nlp_results)
+                    elif task_type == "Image Analysis":
+                    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
+                    if uploaded_file is not None:
+                        image = Image.open(uploaded_file)
+                        st.image(image, caption='Uploaded Image', use_column_width=True)
+                        
+                        # Save the uploaded image temporarily
+                        with open("temp_image.jpg", "wb") as f:
+                            f.write(uploaded_file.getbuffer())
+                        
+                        analysis_results = perform_image_analysis("temp_image.jpg")
+                        
+                        st.write("Image Analysis Results:")
+                        st.write("Top 3 predictions:")
+                        for i, (imagenet_id, label, score) in enumerate(analysis_results['predictions']):
+                            st.write(f"{i + 1}: {label} ({score:.2f})")
+                        
+                        st.write("Edge Detection:")
+                        st.image(analysis_results['edges'], caption='Edge Detection', use_column_width=True)
+                        
+                        # Remove the temporary image file
+                        os.remove("temp_image.jpg")
+                
+                elif task_type == "Time Series Analysis":
+                    # For demonstration, we'll use a sample time series dataset
+                    dates = pd.date_range(start='1/1/2020', end='1/1/2021', freq='D')
+                    values = np.random.randn(len(dates)).cumsum()
+                    ts_data = pd.Series(values, index=dates)
                     
-                    # Run automated tests
-                    test_result = run_tests()
-                    if test_result.wasSuccessful():
-                        st.success("All tests passed successfully!")
-                    else:
-                        st.error("Some tests failed. Please check the code.")
+                    st.line_chart(ts_data)
                     
-                    # Execute code in Docker
-                    execution_result, status_code = execute_code_in_docker(optimized_code)
-                    if status_code == 0:
-                        st.success("Code executed successfully in Docker!")
-                        st.text(execution_result)
-                    else:
-                        st.error(f"Code execution failed: {execution_result}")
+                    analysis_results = perform_time_series_analysis(ts_data)
+                    st.write("Time Series Analysis Results:")
+                    st.write(analysis_results['model_summary'])
+                    st.write("Forecast for the next 5 periods:")
+                    st.write(analysis_results['forecast'])
+                
+                elif task_type == "Graph Analysis":
+                    # For demonstration, we'll use a sample graph
+                    nodes = range(1, 11)
+                    edges = [(1, 2), (1, 3), (2, 4), (2, 5), (3, 6), (3, 7), (4, 8), (5, 9), (6, 10)]
+                    
+                    analysis_results = perform_graph_analysis(nodes, edges)
+                    st.write("Graph Analysis Results:")
+                    st.write("Centrality:")
+                    st.json(analysis_results['centrality'])
+                    st.write("Clustering Coefficient:")
+                    st.json(analysis_results['clustering'])
+                    
+                    # Visualize the graph
+                    G = nx.Graph()
+                    G.add_nodes_from(nodes)
+                    G.add_edges_from(edges)
+                    fig, ax = plt.subplots(figsize=(10, 8))
+                    nx.draw(G, with_labels=True, node_color='lightblue', node_size=500, font_size=16, font_weight='bold', ax=ax)
+                    st.pyplot(fig)
+            
             except Exception as e:
                 handle_error(e)
 
@@ -361,4 +448,176 @@ st.markdown("""
 </div>
 """, unsafe_allow_html=True)
 
-st.markdown('</div>', unsafe_allow_html=True)
+st.markdown('</div>', unsafe_allow_html=True)
+
+# Additional helper functions
+
+def explain_code(code):
+    """Generate an explanation for the given code using NLP techniques."""
+    explanation = generate_response(f"Explain the following code:\n\n{code}")
+    return explanation
+
+def generate_unit_tests(code):
+    """Generate unit tests for the given code."""
+    unit_tests = generate_response(f"Generate unit tests for the following code:\n\n{code}")
+    return unit_tests
+
+def suggest_optimizations(code):
+    """Suggest optimizations for the given code."""
+    optimizations = generate_response(f"Suggest optimizations for the following code:\n\n{code}")
+    return optimizations
+
+def generate_documentation(code):
+    """Generate documentation for the given code."""
+    documentation = generate_response(f"Generate documentation for the following code:\n\n{code}")
+    return documentation
+
+# Add these new functions to the Streamlit UI
+if task_type == "Code Generation":
+    st.sidebar.header("Code Analysis Tools")
+    if st.sidebar.button("Explain Code"):
+        explanation = explain_code(optimized_code)
+        st.sidebar.subheader("Code Explanation")
+        st.sidebar.write(explanation)
+    
+    if st.sidebar.button("Generate Unit Tests"):
+        unit_tests = generate_unit_tests(optimized_code)
+        st.sidebar.subheader("Generated Unit Tests")
+        st.sidebar.code(unit_tests)
+    
+    if st.sidebar.button("Suggest Optimizations"):
+        optimizations = suggest_optimizations(optimized_code)
+        st.sidebar.subheader("Suggested Optimizations")
+        st.sidebar.write(optimizations)
+    
+    if st.sidebar.button("Generate Documentation"):
+        documentation = generate_documentation(optimized_code)
+        st.sidebar.subheader("Generated Documentation")
+        st.sidebar.write(documentation)
+
+# Add more advanced features
+def perform_security_analysis(code):
+    """Perform a basic security analysis on the given code."""
+    security_analysis = generate_response(f"Perform a security analysis on the following code and suggest improvements:\n\n{code}")
+    return security_analysis
+
+def generate_api_documentation(code):
+    """Generate API documentation for the given code."""
+    api_docs = generate_response(f"Generate API documentation for the following code:\n\n{code}")
+    return api_docs
+
+def suggest_design_patterns(code):
+    """Suggest appropriate design patterns for the given code."""
+    design_patterns = generate_response(f"Suggest appropriate design patterns for the following code:\n\n{code}")
+    return design_patterns
+
+# Add these new functions to the Streamlit UI
+if task_type == "Code Generation":
+    st.sidebar.header("Advanced Code Analysis")
+    if st.sidebar.button("Security Analysis"):
+        security_analysis = perform_security_analysis(optimized_code)
+        st.sidebar.subheader("Security Analysis")
+        st.sidebar.write(security_analysis)
+    
+    if st.sidebar.button("Generate API Documentation"):
+        api_docs = generate_api_documentation(optimized_code)
+        st.sidebar.subheader("API Documentation")
+        st.sidebar.write(api_docs)
+    
+    if st.sidebar.button("Suggest Design Patterns"):
+        design_patterns = suggest_design_patterns(optimized_code)
+        st.sidebar.subheader("Suggested Design Patterns")
+        st.sidebar.write(design_patterns)
+
+# Add a feature to generate a complete project structure
+def generate_project_structure(project_description):
+    """Generate a complete project structure based on the given description."""
+    project_structure = generate_response(f"Generate a complete project structure for the following project description:\n\n{project_description}")
+    return project_structure
+
+# Add this new function to the Streamlit UI
+if st.sidebar.button("Generate Project Structure"):
+    project_description = st.sidebar.text_area("Enter project description:")
+    if project_description:
+        project_structure = generate_project_structure(project_description)
+        st.sidebar.subheader("Generated Project Structure")
+        st.sidebar.code(project_structure)
+
+# Add a feature to suggest relevant libraries and frameworks
+def suggest_libraries(code):
+    """Suggest relevant libraries and frameworks for the given code."""
+    suggestions = generate_response(f"Suggest relevant libraries and frameworks for the following code:\n\n{code}")
+    return suggestions
+
+# Add this new function to the Streamlit UI
+if task_type == "Code Generation":
+    if st.sidebar.button("Suggest Libraries"):
+        library_suggestions = suggest_libraries(optimized_code)
+        st.sidebar.subheader("Suggested Libraries and Frameworks")
+        st.sidebar.write(library_suggestions)
+
+# Add a feature to generate code in multiple programming languages
+def translate_code(code, target_language):
+    """Translate the given code to the specified target language."""
+    translated_code = generate_response(f"Translate the following code to {target_language}:\n\n{code}")
+    return translated_code
+
+# Add this new function to the Streamlit UI
+if task_type == "Code Generation":
+    target_language = st.sidebar.selectbox("Select target language for translation", ["Python", "JavaScript", "Java", "C++", "Go"])
+    if st.sidebar.button("Translate Code"):
+        translated_code = translate_code(optimized_code, target_language)
+        st.sidebar.subheader(f"Translated Code ({target_language})")
+        st.sidebar.code(translated_code)
+
+# Add a feature to generate a README file for the project
+def generate_readme(project_description, code):
+    """Generate a README file for the project based on the description and code."""
+    readme_content = generate_response(f"Generate a README.md file for the following project:\n\nDescription: {project_description}\n\nCode:\n{code}")
+    return readme_content
+
+# Add this new function to the Streamlit UI
+if task_type == "Code Generation":
+    if st.sidebar.button("Generate README"):
+        project_description = st.sidebar.text_area("Enter project description:")
+        if project_description:
+            readme_content = generate_readme(project_description, optimized_code)
+            st.sidebar.subheader("Generated README.md")
+            st.sidebar.markdown(readme_content)
+
+# Add a feature to suggest code refactoring
+def suggest_refactoring(code):
+    """Suggest code refactoring improvements for the given code."""
+    refactoring_suggestions = generate_response(f"Suggest code refactoring improvements for the following code:\n\n{code}")
+    return refactoring_suggestions
+
+# Add this new function to the Streamlit UI
+if task_type == "Code Generation":
+    if st.sidebar.button("Suggest Refactoring"):
+        refactoring_suggestions = suggest_refactoring(optimized_code)
+        st.sidebar.subheader("Refactoring Suggestions")
+        st.sidebar.write(refactoring_suggestions)
+
+# Add a feature to generate sample test data
+def generate_test_data(code):
+    """Generate sample test data for the given code."""
+    test_data = generate_response(f"Generate sample test data for the following code:\n\n{code}")
+    return test_data
+
+# Add this new function to the Streamlit UI
+if task_type == "Code Generation":
+    if st.sidebar.button("Generate Test Data"):
+        test_data = generate_test_data(optimized_code)
+        st.sidebar.subheader("Generated Test Data")
+        st.sidebar.code(test_data)
+
+# Main execution
+if __name__ == "__main__":
+    st.sidebar.header("About")
+    st.sidebar.info("This Ultra AI Code Assistant is powered by advanced AI models and incorporates expertise across multiple domains including software development, machine learning, data analysis, and more.")
+    
+    st.sidebar.header("Feedback")
+    feedback = st.sidebar.text_area("Please provide any feedback or suggestions:")
+    if st.sidebar.button("Submit Feedback"):
+        # Here you would typically send this feedback to a database or email
+        st.sidebar.success("Thank you for your feedback!")