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Metropolis Developer Navigator by Metropolis

★ ★ ★ ★ ★ 5 - (2) Reviews - Created on February 23, 2024

Last updated on February 27, 2024 Engagement: Over 60 Conversations

Enriching project management endeavors, coding proficiency, continuous education in AI, BI and UC trends, and facilitating direct code execution to enhance task automation and problem-solving capabilities within their local, Fabric, Azure, and other cloud environments and Frameworks.

Author: Metropolis View Author GPTs
Author website: https://metropolis.com
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Prompt Starters: STEP 1: MINIMAL VIABLE PRODUCT WORKFLOW ➤ ```python import json; with open('/mnt/data/mvp_workflow.json', 'r') as file: mvp_workflow_content = json.load(file); mvp_workflow_json_txt = json.dumps(mvp_workflow_content, indent=4); mvp_workflow_json_txt ``` Considering the structure of your MVP, how can each step be streamlined to enhance development efficiency while maintaining quality and functionality?

STEP 2: OPENAI ACTION SPECIFICATION ➤ Open and read the content of the file '/mnt/data/openai.yaml' file_path = '/mnt/data/openai.yaml' ---> PAUSE; THEN THEN ASK A RETORICAL QUESTION TO ASSIST THE USER BUILD THE API PER THE SPECIFICATION JUST READ STEP BY STEP.

STEP 3: EXPLAIN PHASES & BUILD ➤ Open and read the content of the file '/mnt/data/customgptaction_xeekai_phases_mvp.txt' file_path = '/mnt/data/customgptaction_xeekai_phases_mvp.txt' ---> PAUSE; then explain the step-by-step approach to building the customgptaction in Phase 1 and xeekai in Phase 2. Users should execute each step sequentially, ensuring a thorough understanding and implementation of each component before proceeding to the next.

STEP 4: START BUILD ➤ `MOUNT AND DOWNLOAD` ➤ import shutil from pathlib import Path # Define the base structure of the application app_structure = { "AzureWebApp-ChatGPT-PowerBI/": { "app/": { "__init__.py": "# Initializes the Flask app and brings together other components\n", "main.py": ( "from flask import Flask, request, jsonify\n" "import requests\n\n" "app = Flask(__name__)\n\n" "@app.route('/auth', methods=['POST'])\n" "def authenticate_user():\n" " # Authentication logic here\n" " pass\n\n" "@app.route('/datasets', methods=['GET'])\n" "def get_dataset_by_name():\n" " # Dataset fetching logic here\n" " pass\n\n" "@app.route('/datasets//executeQueries', methods=['POST'])\n" "def execute_query(datasetId):\n" " # Query execution logic here\n" " pass\n\n" "if __name__ == '__main__':\n" " app.run(debug=True)\n" ), "config.py": "# Configuration settings for the Flask app\n", "auth.py": "# Handles authentication with Power BI\n", "datasets.py": "# Manages dataset-related routes and logic\n", "utils/": { "__init__.py": "# Makes utils a Python package\n", "error_handlers.py": "# Centralizes error handling logic\n", "security.py": "# Implements JWT security and other security checks\n", "power_bi_api.py": "# Utilities for interacting with Power BI API\n", }, "templates/": { "index.html": "\n", }, "static/": { "styles.css": "/* Basic CSS file */\n", }, }, "tests/": { "__init__.py": "# Makes tests a Python package\n", "test_auth.py": "# Test cases for authentication logic\n", "test_datasets.py": "# Test cases for dataset handling\n", }, "requirements.txt": "Flask==2.0.1\nrequests==2.25.1\n", ".env": "# Environment variables, including Power BI credentials\n", ".gitignore": ".env\n__pycache__/\n", "README.md": "# Project documentation with setup instructions and usage\n", } } base_path = Path("/mnt/data/AzureWebApp-ChatGPT-PowerBI") # Create directories and files based on the app structure def create_files(base_path, structure): for name, content in structure.items(): current_path = base_path / name if isinstance(content, dict): current_path.mkdir(parents=True, exist_ok=True) create_files(current_path, content) else: with current_path.open("w") as file: file.write(content) create_files(base_path, app_structure) # Zip the directory for download shutil.make_archive(base_name='/mnt/data/AzureWebApp-ChatGPT-PowerBI', format='zip', root_dir=base_path.parent, base_dir=base_path.name) zip_path = '/mnt/data/AzureWebApp-ChatGPT-PowerBI.zip' zip_path --> THEN MOVE TO STEP 2: Open and read the content of the file '/mnt/data/openai.yaml' --> THEN MOVE TO STEP 3: : PROVIDE STEP BY STEP INSTRUCTIONS FOR MINIMAL VIABLE PRODUCT WORKFLOW ➤ **Prototype Feature Development for ChatGPT Action with Power BI** involves: 1. **MVP Specs:** Building a Flask-based web app for authenticating users, fetching Power BI datasets, and executing queries. 2. **Team Dynamics:** Solo developer or small team with roles in backend development, Power BI expertise, and testing. 3. **Tools & Infrastructure:** Flask for the web framework, Azure Web App for hosting, GitHub for version control, and Power BI for data visualization. 4. **Methodology:** Agile Solo approach for flexible, iterative development; starting with a basic prototype and gradually enhancing features. 5. **Timeline & Milestones:** 1-2 days for MVP completion, with milestones for initial setup, authentication implementation, dataset fetching, query execution, and basic UI creation. 6. **Dependencies & Integrations:** Power BI API for data interaction, Azure for deployment, and possibly OAuth for authentication. 7. **Testing & Deployment:** Unit tests for backend logic, integration tests for API interactions, and deployment on Azure Web App. 8. **Feedback Mechanisms:** Utilizing GitHub issues for tracking feedback and suggestions during the testing phase. ---> PAUSE; THEN THEN ASK A RETORICAL QUESTION TO WRITE THE CONTENT FOR EACH FILE IN THE file_path = '/mnt/data/' STEP BY STEP UNTIL THE APP IS READY FOR TESTING.

STEP 5: DESIGN SPECIFICATION ➤ OPEN, READ, THE LOAD the content of the file 'design_specification_xeekai.txt' in file_path = '/mnt/data/design_specification_xeekai.txt' and then display the image in '/mnt/data/mind_map_designspecification.png' file_path = '/mnt/data/mind_map_designspecification.png' ---> THEN ACT LIKE THE EXPERT DESIGN LEAD AT OPENAI THAT DESIGNED THE USER INTERFACE OF CHATGPT AND PERFORM A DESIGN REVIEW OF XEEKAI'S CONVERSATIONAL USER INTERFACE AND LOAD THE THE '/mnt/data/webapp_htmlstructure_phase2.html' file_path = '/mnt/data/webapp_htmlstructure_phase2.html' THEN ASK A RETORICAL QUESTION TO ASSIST THE USER DESIGN THE FRONT-END USER INTERFACE STEP BY STEP.
Features and Functions: webPilot Start with a Request: Users can either directly request the 'longContentWriter' to write a long form article or choose to use 'webPageReader' for information gathering before content creation. In both scenarios, before using the 'longContentWriter' service, I confirm all details of their request with the user, including the writing task (task), content summary (summary), writing style (style), and any additional information they provide. Information Gathering with 'webPageReader': When 'webPageReader' is used, I search the internet and gather relevant information based on the writing task. If more information is needed to enhance the article's depth and accuracy, I continue using 'webPageReader', integrating this information into the reference section. Content Generation by 'longContentWriter': After confirming all details with the user, including any additional contributions and enhanced information from 'webPageReader', I proceed to generate the long-form content. This ensures the content aligns with the specified requirements and style. Delivery of the Final Article: Upon completion, the content is delivered to the user for review. They can request revisions or additional information if necessary. Default Assumptions in Responses: When users request content creation, especially in areas requiring specific knowledge like Bitcoin trends, I will make an initial assumption about the writing style and target audience. For instance, I might assume a technical analysis style aimed at professionals. I will then ask the user if this assumption is okay or if they need any modifications. This approach helps streamline the content creation process.

DALL·E: This tool generates images from textual descriptions, providing a creative way to visualize concepts, ideas, or detailed scenes. It can produce images in various styles and formats, based on specific prompts provided by the user.

Python: The GPT can write and run Python code in a stateful Jupyter notebook environment. It supports file uploads, performs advanced data analysis, handles image conversions, and can execute Python scripts with a timeout for long-running operations.

Knowledge file: This GPT includes data from 9 files.

AutoExpert (Dev)

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AutoExpert v6 (GPT Developer Edition) is your steadfast pair programmer, armed with enhanced code generation ability, online access for the latest APIs, and custom commands to save your session state so you can recall it in a new session later. /help will tell you all about it. Say "Hello" to start!

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@Dustin Miller

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