PADJ BOPR Hackathon Project

Inspiration

We noticed a common pain point in both academic and professional settings: people have big ideas, ambitious projects, or detailed assignments, but they often struggle to break them down into manageable pieces. It’s easy to feel overwhelmed when faced with a wall of requirements in a Word document, a dense PDF, or even just scattered notes. Instead of clear next steps, you’re left with stress and disorganization.

Our team wanted to create a tool that doesn’t just organize tasks—it links them together into a coherent execution plan, showing exactly when to work on what. That’s how PADJ BOPR was born: a smart planning assistant designed to take raw requirements and turn them into actionable, scheduled roadmaps.

What it does

PADJ BOPR takes in project requirements—whether that’s a school assignment, a formal requirements document, or even a photo of handwritten notes—and uses a large language model to automatically break them down into dependency-linked subtasks.

Subtasks are mapped onto a calendar view, showing the logical order of execution and recommended timeframes.
Users can drag and adjust tasks across days, ensuring flexibility.
Calendars can be exported as ICS files, making them easy to import into Gmail, Outlook, or any calendar application.

How we built it

PADJ BOPR is a full-stack web application:

Frontend:

Built with Vue, Tailwind CSS, and Vite for a fast, modern, and responsive experience.
Users can upload documents, review generated subtasks, and interact with a drag-and-drop calendar.
Deployed on Netlify for reliability and speed.

Backend:

Developed with Flask, handling routing, API logic, and integration with external services.

AI Processing:

Used Groq for LLM inference to quickly and accurately break down project requirements into dependency-based subtasks.

File Parsing:

Supports Word documents, PDFs, plain text, and even handwritten notes using parsing and OCR libraries.

Hosting & Cloud:

Backend deployed on Render, supporting scalable Flask hosting.

Calendar Exporting:

Subtasks are formatted into universal ICS files, compatible with all major calendar platforms.

Challenges we ran into

Dependency logic: Teaching the LLM to not just list subtasks, but capture dependencies and sequence them correctly.
File support: Handling PDFs, Word docs, and handwritten images introduced edge cases.
Calendar integration: Ensuring ICS files import cleanly across platforms.
Deployment: Coordinating frontend (Netlify) and backend (Render) introduced CORS and routing issues.
ML model complexity: Training a custom ML model was too complex, so we pivoted to using Groq.

Accomplishments that we're proud of

Transforming unstructured requirements into structured, actionable subtasks.
Supporting multiple input formats: Word, PDF, TXT, and handwritten images.
Building a beautiful, responsive frontend with Vue + Tailwind.
Seamlessly exporting plans as ICS files.
Overcoming technical hurdles in AI integration and backend deployment under a tight hackathon timeline.

What we learned

Balancing AI-generated structure with user flexibility.
The importance of universal formats (ICS) for interoperability.
Integrating modern frontend stacks (Vue + Vite + Tailwind) with a Python backend.
Critical team communication when dividing frontend, backend, and AI tasks.
Small design touches, like drag-and-drop subtasks, greatly improve UX.

What's next for PADJ BOPR

Collaboration features: Allow multiple users to generate shared project calendars.
Direct calendar syncing: Explore optional integration with Google Calendar and Outlook APIs.
Progress tracking: Add task completion tracking, reminders, and visual progress dashboards.
Mobile support: Build a mobile-friendly interface for managing tasks on the go.