Bubble Maker

To create a comprehensive guide for building a custom GPT application for interacting with Power BI through Bubble.io, here are all the steps, outlined in a detailed, step-by-step manner: ### Step 1: Enhanced Input Validation Ensure that the user's query is meaningful by rejecting empty or whitespace-only strings to prevent unnecessary API calls and improve user experience. ```python def validate_input(user_query): if not isinstance(user_query, str) or not user_query.strip(): raise ValueError("Invalid input: please enter a valid text string.") ``` ### Step 2: Detailed Power BI API Setup and Dataset Access Set up the Power BI API with specific error messages for better diagnostics. This includes obtaining an authentication token and fetching the dataset based on its name. ```python import requests def get_dataset(auth_token, dataset_name): headers = {"Authorization": f"Bearer {auth_token}"} url = f"https://api.powerbi.com/v1.0/myorg/datasets?filter=name eq '{dataset_name}'" response = requests.get(url, headers=headers) if response.status_code != 200: raise Exception(f"Failed to access Power BI datasets. Status code: {response.status_code}") datasets = response.json().get('value', []) if not datasets: raise Exception("No matching dataset found.") return datasets[0] # Assuming the first dataset is the one we're interested in ``` ### Step 3: Improved Query Processing and Execution Process and execute the query on the Power BI dataset with feedback based on the API response for troubleshooting and accuracy. ```python def execute_query(dataset_id, auth_token, user_query): url = f"https://api.powerbi.com/v1.0/myorg/datasets/{dataset_id}/executeQueries" headers = {"Authorization": f"Bearer {auth_token}", "Content-Type": "application/json"} data = {"query": user_query, "datasetId": dataset_id} response = requests.post(url, headers=headers, json=data) if response.status_code != 200: raise Exception(f"Failed to execute query on Power BI dataset. Status code: {response.status_code}") query_result = response.json() if query_result.get('error'): raise Exception(f"Query execution failed. Error: {query_result.get('error')}") return query_result # Process this data further as needed ``` ### Step 4: User-friendly Output Formatting Format the output in a user-friendly manner, adjusting for errors during data formatting to ensure clear communication. ```python from tabulate import tabulate import matplotlib.pyplot as plt def format_output(data): if isinstance(data, list): # Assuming data is a list of dictionaries if not data: raise Exception("No data to format.") headers = data[0].keys() table = tabulate(data, headers, tablefmt="grid") return f"```\n{table}\n```" # Markdown code block elif isinstance(data, dict): # For chart plotting if not data: raise Exception("No data to format.") plt.figure(figsize=(10, 6)) plt.plot(data['x'], data['y']) plt.savefig('/tmp/chart.png') return "/tmp/chart.png" ``` ### Step 5: Comprehensive Error Handling in Output Delivery Implement comprehensive error handling throughout the process to ensure that any issues are caught and communicated clearly to the user. ```python def query_power_bi_dataset(user_query): try: validate_input(user_query) auth_token = "Your_Auth_Token_Here" # Securely obtained dataset_name = "Your_Dataset_Name_Here" dataset = get_dataset(auth_token, dataset_name) query_result = execute_query(dataset['id'], auth_token, user_query) formatted_output = format_output(query_result) return formatted_output except Exception as e: return f"An error occurred: {str(e)}" ``` ### Step 6: Dynamic Token Management Automatically manage the authentication tokens to maintain security and seamless integration. ```python import datetime import requests def refresh_auth_token(client_id, refresh_token): url = "https://login.microsoftonline.com/common/oauth2/token" data = { "grant_type": "refresh_token", "refresh_token": refresh_token, "client_id": client_id, "resource": "https://analysis.windows.net/powerbi/api", } response = requests.post(url, data=data) if response.status_code == 200: new_tokens = response.json() return new_tokens['access_token'], new_tokens['refresh_token'], datetime.datetime.now() + datetime.timedelta(seconds=new_tokens['expires_in']) else: raise Exception("Failed to refresh authentication token.") ``` ### Step 7: Asynchronous Data Processing Improve performance through asynchronous data processing, especially beneficial for large datasets or complex queries. ```python import asyncio import aiohttp async def async_execute_query(dataset_id, auth_token, user_query): url = f"https://api.powerbi.com/v1.0/myorg/datasets/{dataset_id}/executeQueries" headers = {"Authorization": f"Bearer {auth_token}", "Content-Type": "application/json"} data = {"query": user_query, "datasetId": dataset_id} async with aiohttp.ClientSession() as session: async with session.post(url, headers=headers, json=data) as response: if response.status == 200: return await response.json() else: raise Exception(f"Failed to execute query asynchronously. Status code: {response.status}") ``` ### Step 8: Enhancing User Interaction Enhance the user interface with interactive elements for a more engaging and customizable user experience. ```javascript // Example of implementing interactive UI elements in Bubble.io function setupInteractiveUI() { // Populate dataset dropdown populateDatasetDropdown(); // Setup query parameter input fields setupQueryParameterInputs(); // Setup execute query button document.getElementById("executeQueryButton").addEventListener("click", function() { var selectedDataset = getSelectedDataset(); var userQuery = getUserQuery(); executeQuery(selectedDataset, userQuery); }); } ``` ### Step 9: Scalability and Deployment Ensure the application's scalability and reliability by using cloud services and adhering to security best practices. - Use cloud platforms like Azure or AWS for deployment. - Secure API keys and tokens. - Use HTTPS for all external requests. - Conduct regular security audits. By following these steps, you can build a robust, user-friendly GPT application that seamlessly interacts with Power BI, providing a dynamic tool for data analysis and visualization.