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‎.github/workflows/deploy.yml‎

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name: Deploy to GitHub Pages and Render
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on:
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push:
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branches: [main]
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workflow_dispatch:
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jobs:
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deploy-ui:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout
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uses: actions/checkout@v3
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- name: Setup Node.js
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uses: actions/setup-node@v3
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with:
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node-version: 18
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cache: 'npm'
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cache-dependency-path: ui/package-lock.json
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- name: Install dependencies
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run: |
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cd ui
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npm ci
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- name: Build
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run: |
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cd ui
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npm run build
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touch out/.nojekyll
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- name: Deploy
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uses: JamesIves/github-pages-deploy-action@v4
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with:
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folder: ui/out
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branch: gh-pages
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deploy-api-notification:
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runs-on: ubuntu-latest
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needs: deploy-ui
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steps:
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- name: Checkout
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uses: actions/checkout@v3
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- name: Notify about API deployment
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run: |
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echo "UI deployment complete. The API must be deployed separately to Render."
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echo "Follow these steps to deploy the API:"
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echo "1. Create a new Web Service on Render.com"
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echo "2. Connect your GitHub repository"
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echo "3. Select the 'api' directory as the root directory"
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echo "4. Set the build command to 'pip install -r requirements.txt'"
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echo "5. Set the start command to 'uvicorn main:app --host 0.0.0.0 --port $PORT'"
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echo "6. Add the environment variable PYTHON_VERSION=3.10.0"
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echo "7. Deploy the service"
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echo "8. Update the PRODUCTION_API_URL in ui/app/config.ts with your Render service URL"
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echo "9. Commit and push the changes to trigger a new UI deployment"

‎.gitignore‎

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# Python
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__pycache__/
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*.py[cod]
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*$py.class
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*.so
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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# Logs and outputs
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logs/
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output/
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*.log
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# Environment
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.env
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.venv
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env/
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venv/
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ENV/
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# Node.js
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node_modules/
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npm-debug.log
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yarn-error.log
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.next/
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# IDE
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.idea/
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.vscode/
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*.swp
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*.swo
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# OS
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.DS_Store
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Thumbs.db
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# Temp files
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*.tmp
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*.bak
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# Jupyter Notebook
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.ipynb_checkpoints
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# Playwright
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test-results/
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playwright-report/
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# Local OLLAMA models
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.ollama/

‎README.md‎

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# Integrated RPA Automation System
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A comprehensive Robotic Process Automation (RPA) system that integrates KGG and OptimusPrime frameworks with advanced features including natural language control, predictive maintenance, and cross-system workflows.
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## 🚀 Features
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### Integrated RPA System
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- Cross-system workflows combining KGG & OptimusPrime
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- Robust error handling with detailed logging
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- HTML reporting with execution metrics
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- Parameter passing between systems
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### Natural Language Control
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- Conversational interface for RPA workflows
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- Local LLM integration with OLLAMA
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- Interactive mode for continuous commands
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- Rule-based fallback for offline usage
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### Predictive Maintenance
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- Anomaly detection in automation logs
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- Component health monitoring with metrics
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- Actionable recommendations for optimization
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- Visual HTML reports with priority levels
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### UI Dashboard Integration
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- Web-based control panel
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- Real-time monitoring of agents
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- Visual workflow execution tracking
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- One-click automation triggering
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## 📋 Requirements
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- Python 3.10+
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- Conda environment management
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- Node.js (for UI dashboard)
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- OLLAMA (for local LLM capabilities)
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## 🔧 Installation
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```bash
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# Clone the repository
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git clone https://github.com/yourusername/integrated-rpa-system.git
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cd integrated-rpa-system
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# Create and activate conda environment
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conda env create -f environment.yml
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conda activate agent_f1_env
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# Install additional dependencies
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pip install -U langmem crewai
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# Install OLLAMA (macOS)
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brew install ollama
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ollama pull mistral
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# Install UI dependencies
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cd ui
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npm install
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```
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## 🏃‍♂️ Quick Start
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### Run Integrated RPA System
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```bash
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./run_integrated_rpa.sh
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```
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### Use Natural Language Interface
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```bash
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./run_nl_rpa.sh --interactive
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```
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### Run Predictive Maintenance
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```bash
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./run_predictive_maintenance.sh
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```
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### Start UI Dashboard
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```bash
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cd ui
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npm run dev
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```
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## 🌐 Deployment
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The RPA System is deployed using GitHub Pages for the UI and Render for the API backend.
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### UI Deployment
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The UI is automatically deployed to GitHub Pages when changes are pushed to the main branch. You can access the deployed UI at:
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**https://yacinewhatchandcode.github.io/MultiAgenticSytemYBE/**
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### API Deployment
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The API can be deployed to Render.com using the following steps:
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1. Create a new Web Service on Render.com
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2. Connect your GitHub repository
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3. Select the 'api' directory as the root directory
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4. Set the build command to `pip install -r requirements.txt`
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5. Set the start command to `uvicorn main:app --host 0.0.0.0 --port $PORT`
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6. Add the environment variable `PYTHON_VERSION=3.10.0`
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7. Deploy the service
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8. Update the `PRODUCTION_API_URL` in `ui/app/config.ts` with your Render service URL
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9. Commit and push the changes to trigger a new UI deployment
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### Local Development
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To run the system locally for development:
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1. Start the API:
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```bash
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./run_api.sh
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```
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2. Start the UI:
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```bash
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cd ui
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npm run dev
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```
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3. Access the UI at http://localhost:3000
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## 📁 Project Structure
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```
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├── integrated_rpa_automation.py # Core integration system
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├── nl_rpa_interface.py # Natural language interface
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├── predictive_maintenance.py # Predictive maintenance system
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├── run_integrated_rpa.sh # Runner script for integrated system
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├── run_nl_rpa.sh # Runner script for NL interface
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├── run_predictive_maintenance.sh # Runner script for maintenance
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├── environment.yml # Conda environment definition
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├── ui/ # Web dashboard
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├── samples/ # Sample workflows
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├── output/ # Output directory
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└── logs/ # Log files
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```
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## 🤝 Integration
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This system integrates with:
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- KGG RPA System
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- OptimusPrime Framework
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- OLLAMA for local LLM capabilities
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- Playwright for browser automation
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- Selenium for UI testing
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- OCR for image recognition
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## 📄 License
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MIT
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## 🙏 Acknowledgements
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- CrewAI for agent orchestration
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- Langchain for LLM integration
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- Playwright and Selenium for browser automation
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- OLLAMA for local LLM capabilities

‎api/Dockerfile‎

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FROM python:3.10-slim
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WORKDIR /app
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# Install system dependencies
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RUN apt-get update && apt-get install -y \
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build-essential \
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curl \
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git \
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&& rm -rf /var/lib/apt/lists/*
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# Copy requirements and install Python dependencies
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COPY requirements.txt .
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RUN pip install --no-cache-dir -r requirements.txt
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# Copy application code
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COPY . .
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# Expose port
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EXPOSE 8000
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# Run the application
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CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

‎api/Procfile‎

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web: uvicorn main:app --host 0.0.0.0 --port $PORT

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