Project Info
Inspiration
As our graduation date looms close, we are in the thick of the job search process, and tracking our job applications has a myriad of benefits that keep us proactive and organized during the process. Tracking helps us monitor deadlines and better prepare for interviews by retaining key details from job descriptions. Tracking also helps prevent sending duplicate applications to the same company and shows our progress over time, keeping us motivated and accountable during the often lengthy job hunt. However, with large amounts of job applications, tracking each one manually can be extremely tedious and quickly become overwhelming.
What it does
Enter JobCat, your personal job search assistant. JobCat keeps your application information organized and easy to access. It automatically scans and summarizes your job application status directly from your email inbox. JobCat tracks the company, position, mode (remote, onsite, etc.), location, salary, application date, status (interview, offer, rejected, etc.), and job description for a job application.
How we built it
JobCat is built with Reflex, Python, LangChain, and Groq. Our cat mascot was made using Figma.
Challenges we ran into
Unfamiliar frameworks and API: Reflex, LangChain, Groq Slow WiFi Running Python on Windows
Accomplishments we're proud of
Dynamic updates UI Design AI integration
What we learned
Reflex LangChain Groq
What's next
There are many additional features we hope to add to further enhance the job search experience: SMS notifications for application status updates Tailored interview prep based on the job posting and the user's experience Custom AI-powered resume and cover letter building
JobCat
JobCat keeps your application information organized and easy to access. It automatically scans and summarizes your job application status directly from your email inbox. It tracks the company, position, mode (remote, onsite, etc.), location, salary, application date, status (interview, offer, rejected, etc.), and job description for a job application.
Installation
Clone the repo.
Install dependencies:
`pip install -r requirements.txt`.
`pip install -r ai\requirements.txt`.
Migrate your database:
reflex db makemigrations
reflex db migrate
Run App:
reflex run
Analysis
View
Metric
- 19
- 9
- 4
Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.
Technology
- CSSIn code
- PythonIn code
- LangChainClaimed
2 of 3 appear in the indexed code. 1 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.
AI coding agents
No AI coding agent signals were found in this repository.
Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.
Codebase size
Source size
104 KB
Source files
47
Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.
Repository
avivamei/calhacks
60 files · 162 KB · @ bd1ee43
Structure
Interface
30 files · 50%Screens, components and styles rendered to the user.
Application logic
17 files · 28%Domain rules, services and shared utilities.
+1 more
Supporting
Layers are inferred from where files sit in the tree, not from reading the code. A project that names its directories unconventionally will read oddly here — open the file browser to check anything the diagram implies.
Languages
- Python99%
- Markdown1%
- CSS0%
Share of indexed source by file size. Binary and vendored files are excluded.
Dependencies
requirements.txt
pypi · 4- beautifulsoup4
- psycopg2-binary
- reflex
- reflex_google_auth
ai/requirements.txt
pypi · 3- langchain-google-community[gmail]
- langchain-groq
- python-dotenv
Declared in the repository’s manifests at the indexed commit. A declared package is not proof it is used, and runtime dependencies are listed first.
This project’s features have not been analysed yet.
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