README.md

metadata

license: apache-2.0
title: AI Workflow Analyzer
sdk: streamlit
emoji: 📊
colorFrom: purple
colorTo: blue
short_description: An AI-powered tool that analyzes workflows, identifies ineff
python_version: 3.11
sdk_version: 1.55.0

AI Workflow Analyzer

An AI-powered tool that analyzes workflows, identifies inefficiencies, and suggests automation opportunities using Large Language Models.

Overview

This project helps teams understand and improve their internal processes by leveraging AI. Users can input a workflow description, and the system returns a structured analysis including problems, automation opportunities, and AI-driven solutions.

Features

• Analyze workflows in real time
• Identify inefficiencies and bottlenecks
• Suggest AI-based automation strategies
• Generate prototype ideas for implementation
• Export analysis as a Markdown (.md) file
• Simple and interactive UI using Streamlit

Demo

Live demo

Users can:

1. Enter a workflow description
2. Receive structured AI analysis
3. Download the results as a Markdown report

Example Input

• Our engineering team manually reviews logs and writes daily summaries. This process is slow and inconsistent.

Example Output

• Problems

  • Manual and repetitive work
  • Time-consuming process
  • Inconsistent results

• Automation Opportunities

  • Automate log summarization
  • Standardize reporting

• AI Solution

  • Use an LLM to extract key insights and generate summaries

• Prototype Idea

  • Python script that processes logs and generates summaries using an AI API

Tech Stack

• Python
• Streamlit
• Google Gemini API (LLM)

Project Structure

src/
 ├── main.py
 ├── analyzer.py
 ├── prompts.py

Installation

git clone https://github.com/YamenRM/workflow-analyzer.git
cd workflow-analyzer
pip install -r requirements.txt

Setup

Set your API key as an environment variable:

• Windows (PowerShell)

   setx GEMINI_API_KEY "your_api_key_here"
  

• Linux / macOS

   export GEMINI_API_KEY="your_api_key_here"
  

Run the App

streamlit run main.py

Use Cases

• Engineering workflow optimization
• AI adoption analysis
• Process automation planning
• Internal tooling prototyping

Future Improvements

• Structured JSON output
• Workflow visualization (diagrams)
• History tracking of analyses
• Multi-workflow comparison
• Integration with team tools (e.g., GitLab, Notion)

##Author

YamenRM