This repository hosts a centralized, multipage AI Automation & Web Scraping Portal built using Streamlit. It consolidates independent enterprise automation scripts, modular utilities, and high-throughput data extraction scrapers into a single interactive user interface.
Instead of executing scattered Python scripts via CLI, recruiters and users can experience, monitor, and trigger real-time automations directly from their web browsers.
- 📊 Deep GitHub Profile Analyzer (
1_GitHub.py): Integrates directly with the GitHub REST API to fetch comprehensive user analytics, repository statistics, tracking patterns, and contribution frequencies. - 🌤️ Real-Time Weather Forecaster (
2_Weather.py): Connects with global atmospheric APIs to extract live weather conditions, wind speeds, and predictive forecasts for any global coordinate. - 📚 High-Throughput E-Commerce Scraper (
3_Books.py): Uses BeautifulSoup to programmatically crawl e-commerce structures, parse raw HTML DOM elements, and package product details into download-ready CSV structures.
The project strictly follows modular programming paradigms to separate frontend UI rendering from underlying backend automation engines:
AI_Automation_Dashboard/
│
├── main.py # Main landing page & core application config
├── requirements.txt # Production dependencies & tracking packages
│
├── pages/ # Streamlit Multipage Frontend Architecture
│ ├── 1_GitHub.py # GitHub API interaction & rendering dashboard
│ ├── 2_Weather.py # Live Weather rendering and dashboard UI
│ └── 3_Books.py # E-Commerce parser interface & CSV exporter
│
└── utils/ # Pure Python Backend Automation Engines
├── __init__.py # Python package initializer
├── github.py # API queries, authorization & token logic
└── weather.py # Endpoint handling and raw JSON parsers
- Frontend Framework: Streamlit (Dynamic multi-page UI mapping)
- Data Extraction & Scraping: BeautifulSoup4 (HTML tree parsing & DOM traversing)
- HTTP & API Engines: Requests (Asynchronous endpoint communication)
- Data Operations: Pandas & NumPy (Exporting to structured matrix & CSV engines)
Want to run this Automation Suite locally? Follow these steps:
- Clone the Repository:
git clone [https://github.com/mhassan619/AI_Automation_Dashboard.git](https://github.com/mhassan619/AI_Automation_Dashboard.git)
cd AI_Automation_Dashboard
- Configure Virtual Environment (Recommended):
python -m venv env
source env/bin/activate # On Windows use: env\Scripts\activate
- Install Dependencies:
pip install -r requirements.txt
- Launch the Dashboard:
streamlit run main.py
- Error Handling: Every API call and scraper is wrapped inside strict try-except protocols to prevent UI breaking during connection timeouts.
- Modular Logic: Separate routing ensures that changing the API source or scraper tags inside utils/ will never disrupt the pages/ frontend rendering.
### 🌱 "Consistency beats motivation, every single day."
📩 Connect via Email • 🌐 Let's Network on LinkedIn
```