title	PaperPilot
emoji	📄
colorFrom	blue
colorTo	purple
sdk	gradio
sdk_version	6.16.0
python_version	3.10
app_file	app.py
pinned	false
tags	build-small backyard-ai off-brand field-notes

📄 PaperPilot

AI-powered Scholarship & Form Assistant

PaperPilot helps students and applicants understand lengthy application forms instantly by extracting key information, checking eligibility, generating document checklists, and answering questions using AI.

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🚀 Problem Statement

Many students and applicants struggle with:

• Long and complex application forms
• Hidden eligibility requirements
• Missing deadlines
• Missing mandatory documents
• Confusing instructions

PaperPilot solves this by automatically extracting and explaining the most important information from uploaded documents.

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Solution

PaperPilot uses OCR and AI to analyze forms and provide structured, easy-to-understand information.

Users simply upload a PDF and PaperPilot automatically extracts:

• Form Name
• Deadlines
• Eligibility Rules
• Required Documents
• Contact Information
• Form Summary

It also provides an AI assistant for asking questions about the uploaded form.

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✨ Features

📄 Smart PDF Processing

• Supports normal PDFs
• Supports scanned PDFs
• OCR-based text extraction

🤖 AI-Powered Form Understanding

• Automatic form analysis
• Structured information extraction
• Form summarization

✅ Eligibility Analyzer

• Detects eligibility criteria
• Extracts income limits
• Identifies category requirements
• Highlights important conditions

📋 Document Checklist Generator

• Extracts required documents
• Generates actionable checklists

📅 Timeline Extraction

• Detects deadlines
• Highlights important dates

⚠ Risk Detection

• Identifies missing information
• Detects critical deadlines
• Warns users about possible issues

🔍 Document Verification Assistant

• Helps users verify required documents before submission

💬 Ask PaperPilot

Powered by Qwen 2.5 Instruct LLM

Users can ask natural-language questions such as:

• What is the last date to apply?
• Am I eligible?
• What documents are required?
• Summarize this form.
• What happens if I miss the deadline?

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🏗 System Architecture

User Uploads PDF
        │
        ▼
OCR Engine
(Normal + Scanned PDFs)
        │
        ▼
Text Extraction
        │
        ▼
Master JSON Builder
        │
 ┌──────┼──────┐
 ▼      ▼      ▼
Summary Eligibility Checklist
 │        │        │
 ▼        ▼        ▼
Timeline Risk Detection Verification
        │
        ▼
Qwen 2.5 AI Assistant
        │
        ▼
User-Friendly Insights

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🛠 Tech Stack

Frontend

• Gradio

Backend

• Python

OCR

• EasyOCR
• PyMuPDF
• PDFPlumber

AI / NLP

• Hugging Face Inference API
• Qwen 2.5 Instruct

Deployment

• Hugging Face Spaces

DevOps

• GitHub Actions
• CI/CD Pipeline

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🔄 CI/CD Pipeline

GitHub Repository
        │
        ▼
GitHub Actions
        │
        ▼
Hugging Face Spaces
        │
        ▼
Automatic Deployment

Every push to the main branch automatically deploys the latest version of PaperPilot.

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🎯 Use Cases

• Scholarship Applications
• Government Schemes
• Admission Forms
• Internship Applications
• Job Applications
• Grant Applications
• Registration Forms

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Demo Video

https://youtu.be/EalpBFBLPA0?si=UVeDujcsYHECKKSm

Social Media Post

https://x.com/KamranX07/status/2066492830475038881

GitHub Repository

https://github.com/KamranX07/PaperPilot

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📝 Field Notes

Problem

Students often struggle to understand scholarship and application forms due to complex eligibility rules, deadlines, and document requirements.

Solution

PaperPilot is an AI-powered assistant that analyzes forms, extracts important information, verifies eligibility, and answers user questions in natural language.

Technical Architecture

• OCR/Text Extraction
• Structured Form Parser
• Master JSON Generation
• Eligibility Verification Engine
• Qwen 2.5 7B Instruct via Hugging Face Inference
• Gradio Frontend

Challenges Faced

• Hugging Face legacy inference endpoints were deprecated.
• Migrated from the old API route to the new InferenceClient-based routing system.
• Resolved dependency conflicts between Gradio and Pydantic.
• Improved eligibility extraction logic for income limits.

Lessons Learned

• Small language models can solve practical real-world problems effectively.
• Structured JSON extraction greatly improves reliability.
• Good UI/UX significantly improves user adoption.

Future Enhancements

• Multi-language support
• Advanced eligibility reasoning
• Personalized recommendations
• Form autofill assistance
• Voice-based interaction
• Mini-RAG document memory
• Mobile application

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👨‍💻 Developer

Md Kamran Akhter

Built as an AI-powered document intelligence platform for simplifying form understanding and application workflows.

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⭐ PaperPilot

Upload. Understand. Apply with Confidence. Built for the Build Small Hackathon.