Shazam

Shazam is a project that integrates with Azure services to process queries and execute workflows using Promptflow. This project enables seamless management of Azure resources and testing of custom workflows with ease.

Table of Contents

• Overview
• Features
• Prerequisites
• Project Structure
• Azure Module Details
• Frontend Module Details
• Scraping Module Details
• Running Instructions

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Overview

Shazam is designed to simplify the interaction with Azure services by offering a robust framework for running and testing workflows. It uses tools like Azure CLI and Promptflow to enable users to manage and execute queries efficiently.

Features

• Azure CLI Integration: Seamlessly log in and manage Azure resources.
• Promptflow Workflows: Test and execute custom workflows for various use cases.
• Error Handling: Built-in mechanisms to provide informative error messages.

Prerequisites

• Homebrew (MacOS):

  brew update && brew install azure-cli

• Azure CLI:

    az login

• **Python Packages:

    python -m pip install --upgrade pip
    pip install promptflow azure-identity
  

Project Structure

├── .idea/                 # IDE configuration files
├── copilot_flow/          # Workflow and flow definitions
├── tests.py               # Testing scripts
├── package.json           # Project dependencies
├── README.md              # Project documentation
├── .gitignore             # Git ignore rules
├── azure/                 # Azure module for managing workflows
├── Front-end-02/          # Frontend components
├── shazam-scraping/       # Scraping scripts and collected data

Azure Module Details

The azure folder contains the core functionality of Shazam, focusing on Azure integration and Promptflow workflows.

Key Files:

• .env: Environment configuration for Azure credentials and settings.
• copilot_flow/bridge.js: JavaScript file for bridging workflows.
• copilot_flow/chat.prompty: Prompt template for chat-based workflows.
• copilot_flow/copilot.py: Python script handling copilot functionality.
• copilot_flow/flow.flex.yaml: Configuration file for flexible workflows.
• copilot_flow/input_with_chat_history.json: Sample input data with chat history.
• copilot_flow/oneQuestionEval.py: Script for evaluating single question workflows.
• copilot_flow/queryIntent.prompty: Prompt template for intent-based queries.

Frontend Module Details

The Front-end-02 folder contains components and scripts related to the user-facing part of Shazam. This module is intended for building and enhancing the frontend interface.

Key Files:

• index.html: The main HTML file for the frontend interface.
• style.css: Stylesheet for the user interface.
• app.js: Core JavaScript logic for handling user interactions.
• components/: A directory containing reusable frontend components.

Scraping Module Details

The shazam-scraping folder contains scripts and data used to scrape documentation and resources from the ISO New England website. This module automates data collection for use within the project.

Key Files:

• scraper.py: The main script for scraping data.
• requirements.txt: Python dependencies required for running the scraper.
• data/: Directory containing the scraped documentation and resources.

Running Instructions

1. Install Dependencies:
   Ensure all prerequisites are installed as outlined in the prerequisites section.

   brew update && brew install azure-cli
   az login
   python -m pip install --upgrade pip
   pip install promptflow azure-identity
   

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2. Run a workflow: Use the following command to test a workflow using Promptflow:

pf flow test --flow ./copilot_flow --inputs chat_input="sample query"

Replace "sample query" with your desired input for testing.

3. Run a Scraper: To scrape data from the ISO New England website, run: python shazam-scraping/scraper.py The scraped data will be saved in the shazam-scraping/data directory.

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4.Run a server and frontend together:

concurrently \"node ../azure/copilot_flow/server.js \" \"next dev\"