Add profile generation use case

.gitignore

@@ -61,3 +61,6 @@ coverage.xml
 owl/camel/types/__pycache__/
 owl/camel/__pycache__/
 owl/camel/utils/__pycache_/
+/community_usecase/Profile_generation/Ahmed Eltawil _ Computer, Electrical and Mathematical Sciences and Engineering_files/
+/community_usecase/Profile_generation/my-user-data/
+/community_usecase/Profile_generation/my-user-data-dir/

community_usecase/Profile_generation/README.md

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+# Scholar Profile Generation
+
+This project provides an end-to-end pipeline for automatically generating a polished HTML profile for an academic **scholar** starting from their Google Scholar (or similar) profile link.
+
+The pipeline is powered by [CAMEL-AI](https://github.com/camel-ai/camel) agents and Playwright-based headless browsing and is exposed both as a **CLI tool** and as a **Flask REST API** with an accompanying (very) minimal front-end.
+
+---
+
+## Key Features
+
+* **Smart link discovery** – Uses the Exa search engine, via `camel-ai`'s `SearchToolkit`, to find high-quality links related to the scholar (homepage, Google Scholar, institutional pages, social media, etc.).
+* **Headless crawling & markdown extraction** – `BrowserNonVisualToolkit` (Playwright under the hood) visits each link, extracts the main content, and stores it as individual markdown files.
+* **Asynchronous Web Agent** – Link crawling and content extraction are orchestrated with **asyncio**, allowing multiple pages to be processed **concurrently** and cutting the end-to-end runtime significantly compared with sequential scraping.
+* **Automatic HTML generation** – Aggregated markdown is parsed with an LLM to fill an HTML template (`template.html`) resulting in a share-ready profile page.
+* **Progress tracking** – Long-running jobs execute in a background thread. Progress can be polled via `/api/progress/<job_id>`.
+* **Dual usage modes** –
+  * **CLI**: `python app.py --url <scholar_url>`
+  * **Web**: `python app.py` (serves on `http://localhost:5000`)
+
+---
+
+## Project Structure
+
+```
+community_usecase/Profile_generation/
+├── app.py                # Main entry point (CLI & Flask server)
+├── template.html         # Bootstrap-based HTML template filled by the pipeline
+├── templates/            # Front-end assets (index.html, CSS, JS)
+└── workflow.png          # Architecture diagram
+```
+
+---
+
+## Quick Start
+
+1. **Install Python dependencies**
+
+   ```bash
+   python -m venv .venv
+   source .venv/bin/activate          # On Windows use: .venv\Scripts\activate
+   pip install --upgrade pip
+   pip install -r requirements.txt
+   ```
+
+2. **Set environment variables**
+
+   The pipeline relies on LLMs and Exa search.
+
+   ```bash
+   export OPENAI_API_KEY=<your-openai-key>
+   export EXA_API_KEY=<your-exa-key>
+   ```
+
+3. **Run the web server**
+
+   ```bash
+   python app.py
+   # Open http://localhost:5000 in your browser
+   ```
+
+   Or generate a profile once via CLI:
+
+   ```bash
+   python app.py --url "https://scholar.google.com/citations?user=<SCHOLAR_ID>"
+   ```
+
+   The resulting HTML file will be placed in the project directory (e.g. `profile.html`).
+
+---
+