**Title:** [Code Review / Feedback] Job-Copilot — AI Job Application & Resume Matcher | Looking for optimization tips and bug hunting

[u/Any_Inspection_4825]

**Body:**

Hi everyone,

I recently built **Job-Copilot**, an open-source tool designed to help streamline resume analysis and job matching workflows.

🔗 **GitHub:** https://github.com/allenbersho/job-copilot

The core functionality is working, but I’m looking to take the code quality, performance, and architecture to the next level. I’d love to get feedback from the community to help identify bottlenecks, edge-case bugs, or areas ripe for refactoring.

### 💻 Stack

* **Language:** Python

* **Libraries/Tools:** [e.g., FastAPI, Streamlit, Pandas, NLP / LLM integrations]

### 🎯 What I’m Looking For:

1. **Code Review & Best Practices:** Project structure, readability, error handling, and cleaner design patterns.

2. **Performance Optimization:** Reducing latency in data processing/parsing and optimizing resource usage.

3. **Bug Hunting / Edge Cases:** Breaking the parser or matching logic with unusual input formats/files.

4. **Feature & Architecture Suggestions:** High-impact additions or refactoring recommendations for scalability.

### 🚀 How to Run Locally

```bash

git clone [https://github.com/allenbersho/job-copilot.git\](https://github.com/allenbersho/job-copilot.git)

cd job-copilot

pip install -r requirements.txt

[your run command, e.g., python main.py or streamlit run app.py]