Enhancing Web Page Elements Using Generative AI and User Behavior Data

import openai openai.api_key = 'YOUR_API_KEY' def generate_event_tracking_code(event_type, element_id): prompt = f""" Write JavaScript code that tracks the '{event_type}' event on the '{element_id}' element on a web page. The code should send the event data to the '/track_event' endpoint via a POST request. The data should include the event type, timestamp, and element ID. """ response = openai.Completion.create( engine="text-davinci-003", prompt=prompt, max_tokens=200, temperature=0, ) return response.choices[0].text.strip() # Example usage event_type = 'click' element_id = 'submitButton' code = generate_event_tracking_code(event_type, element_id) print(code)

// Tracking 'click' event on 'submitButton' element document.getElementById('submitButton').addEventListener('click', function(event) { var data = { eventType: 'click', timestamp: new Date().toISOString(), elementId: 'submitButton' }; fetch('/track_event', { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify(data) }); });

def generate_log_parser_code(log_format): prompt = f""" Write Python code that parses Apache server logs in the format '{log_format}'. The code should extract the IP, timestamp, request method, URL, and status code. """ response = openai.Completion.create( engine="text-davinci-003", prompt=prompt, max_tokens=300, temperature=0, ) return response.choices[0].text.strip() # Example usage log_format = '%h %l %u %t "%r" %>s %b' code = generate_log_parser_code(log_format) print(code)

import re log_pattern = re.compile(r'(?P<ip>\S+) \S+ \S+ \[(?P<timestamp>.+?)\] "(?P<method>\S+) (?P<url>\S+) \S+" (?P<status>\d{3}) \S+') with open('access.log', 'r') as f: for line in f: match = log_pattern.match(line) if match: data = match.groupdict() print(f"IP: {data['ip']}, Timestamp: {data['timestamp']}, Method: {data['method']}, URL: {data['url']}, Status: {data['status']}")

import openai def generate_anomaly_detection_code(): prompt = """ Write Python code that detects anomalies in a real-time stream of user behavior data. The data consists of event type, timestamp, and user ID. Use statistical methods to identify anomalies. """ response = openai.Completion.create( engine="text-davinci-003", prompt=prompt, max_tokens=500, temperature=0, ) return response.choices[0].text.strip() code = generate_anomaly_detection_code() print(code)

def generate_kpi_recommendations(website_type): prompt = f""" Suggest five key performance indicators (KPIs) suitable for a '{website_type}' website. Provide a brief explanation for each. """ response = openai.Completion.create( engine="text-davinci-003", prompt=prompt, max_tokens=500, temperature=0, ) return response.choices[0].text.strip() # Example usage website_type = 'e-commerce site' kpi_recommendations = generate_kpi_recommendations(website_type) print(kpi_recommendations)

def generate_data_visualization_code(data_description): prompt = f""" Write Python code to visualize the following data:\n\n{data_description}\n\nUse Matplotlib and highlight key patterns. """ response = openai.Completion.create( engine="text-davinci-003", prompt=prompt, max_tokens=700, temperature=0, ) return response.choices[0].text.strip() # Example usage data_description = "User-specific page dwell time and bounce rate data" code = generate_data_visualization_code(data_description) print(code)

import pandas as pd import matplotlib.pyplot as plt # Load data data = pd.read_csv('user_engagement.csv') # Page Dwell Time Histogram plt.figure(figsize=(10,5)) plt.hist(data['time_on_page'], bins=50, color='skyblue', edgecolor='black') plt.title('Distribution of Page Dwell Time') plt.xlabel('Dwell Time (seconds)') plt.ylabel('Number of Users') plt.show() # Bounce Rate Bar Chart plt.figure(figsize=(10,5)) bounce_rates = data.groupby('page')['bounce_rate'].mean() bounce_rates.plot(kind='bar', color='salmon') plt.title('Average Bounce Rate by Page') plt.xlabel('Page') plt.ylabel('Bounce Rate (%)') plt.show()

from sklearn.cluster import KMeans import pandas as pd # Load data data = pd.read_csv('user_behavior.csv') # Select features features = data[['time_on_site', 'pages_visited', 'conversions']] # Create clustering model kmeans = KMeans(n_clusters=3) kmeans.fit(features) # Add cluster labels data['cluster'] = kmeans.labels_ # Review results print(data.groupby('cluster').mean())

from sklearn.ensemble import RandomForestClassifier import pandas as pd # Load data data = pd.read_csv('user_interaction.csv') # Set features and target variable X = data[['button_size', 'image_position', 'text_length', 'color_scheme']] y = data['conversion'] # Create Random Forest model model = RandomForestClassifier() model.fit(X, y) # Check feature importance importances = model.feature_importances_ feature_names = X.columns for name, importance in zip(feature_names, importances): print(f"{name}: {importance}")

def generate_ab_test_plan(element_to_test, goal_metric): prompt = f""" Design an A/B test for the '{element_to_test}' element on a web page. The goal metric is '{goal_metric}'. Include the testing method and expected outcomes. """ response = openai.Completion.create( engine="text-davinci-003", prompt=prompt, max_tokens=500, temperature=0, ) return response.choices[0].text.strip() # Example usage element_to_test = 'CTA button size' goal_metric = 'click-through rate' ab_test_plan = generate_ab_test_plan(element_to_test, goal_metric) print(ab_test_plan)

def generate_ab_test_code(tool_name, element_id, variation_code): prompt = f""" Write code using {tool_name} to conduct an A/B test on the '{element_id}' element. In version B, the following change should be made:\n\n{variation_code} """ response = openai.Completion.create( engine="text-davinci-003", prompt=prompt, max_tokens=500, temperature=0, ) return response.choices[0].text.strip() # Example usage tool_name = 'Google Optimize' element_id = 'ctaButton' variation_code = "Change the button font size from 18px to 24px" ab_test_code = generate_ab_test_code(tool_name, element_id, variation_code) print(ab_test_code)

// Google Optimize code if (window.dataLayer) { dataLayer.push({'event': 'optimize.activate'}); } // Apply changes for Version B document.getElementById('ctaButton').style.fontSize = '24px';

def generate_ui_improvement_suggestions(data_insights): prompt = f""" Based on the following user behavior data analysis, provide suggestions for UI/UX improvements on the web page:\n\n{data_insights} """ response = openai.Completion.create( engine="text-davinci-003", prompt=prompt, max_tokens=700, temperature=0, ) return response.choices[0].text.strip() # Example usage data_insights = """ - Users are not scrolling to the bottom of the page - Key information is located in the middle of the page - Low click-through rate on the sidebar menu """ suggestions = generate_ui_improvement_suggestions(data_insights) print(suggestions)

def generate_code_modification(original_code, improvement_suggestion): prompt = f""" Given the original HTML/CSS code:\n\n{original_code}\n\nImplement the following improvements and provide the modified code:\n\n{improvement_suggestion} """ response = openai.Completion.create( engine="text-davinci-003", prompt=prompt, max_tokens=1000, temperature=0, ) return response.choices[0].text.strip() # Example usage original_code = """ <!-- Original Code --> <div id="main-content"> <h2>Product Introduction</h2> <p>Detailed product description...</p> </div> <div id="sidebar"> <!-- Sidebar Menu --> </div> """ improvement_suggestion = "Move the main content to the top and remove the sidebar menu." modified_code = generate_code_modification(original_code, improvement_suggestion) print(modified_code)

<!-- Modified Code --> <div id="main-content"> <h2>Product Introduction</h2> <p>Detailed product description...</p> </div> <!-- Sidebar menu removed -->

<!-- Existing CTA Button --> <div id="cta-section"> <button id="signupButton">Sign Up Now</button> </div> <!-- Improved CTA Button Placed at the Top --> <div id="header"> <button id="signupButton">Sign Up Now</button> </div>

/* Enlarged Button Size */ #signupButton { font-size: 20px; padding: 15px 30px; }