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Umaima Iqbal's avatar
Umaima Iqbal
Lahore, Pakistan
I build offline AI tools that make documents talk.
New to Contra
I build offline AI tools that make documents talk.
Cover image for AuraExtract — Intelligent Invoice &
AuraExtract — Intelligent Invoice & Receipt Data Extractor The extraction engine uses intelligent regex pattern matching that handles real-world invoice layouts — column-per-line PDF formats, inline tabular formats, and plain text documents. It detects 10 fields automatically and parses up to 20 line items per invoice. Supports PDF, TXT, and DOCX formats. Includes a raw text preview panel so users can verify exactly what the engine is reading. CSV export includes both the summary fields and full line items table — ready to open directly in Excel. Pure Python. Zero external dependencies beyond pypdf for PDF reading.
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Cover image for AuraSort scans any folder and
AuraSort scans any folder and automatically sorts files into named subfolders by type — Documents, Images, Videos, Audio, Code, Archives, and more. Files are renamed to clean, consistent lowercase format. Every operation is logged live on screen as it happens. Built with a Dry Run mode so users can preview exactly what will move before anything is touched. Full undo restores every file to its original location with one click. An HTML report is generated after each sort showing every file moved, every category created, and total time taken. Pure Python. Zero external libraries. Works on any machine without installation.
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Cover image for A fully offline document summarizer
A fully offline document summarizer built in pure Python. Uses TF-IDF scoring, position weighting, and Jaccard deduplication to extract the most important sentences from any PDF, DOCX, or TXT file — each labeled with a relevance percentage. The result looks like this: [1] [100% relevance] The algorithm achieved 94% accuracy on benchmark tests. [2] [81% relevance] Training was performed on 50,000 labeled samples. [3] [67% relevance] Results were validated using 5-fold cross validation. Supports PDF, Word, and TXT files. Saves summaries to your computer. Runs completely offline. No subscriptions, no API keys, no internet required.
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Cover image for Built AuraChat v3.0 — a
Built AuraChat v3.0 — a fully offline Document Intelligence desktop app in pure Python. Users upload any PDF, Word, or TXT file and ask questions in plain English. The system returns cited answers with confidence scores instantly. Technical highlights: — Custom NLP engine using TF-IDF scoring + hybrid token overlap analysis — 1,700× faster indexing than baseline on 500-page documents — Multi-threaded processing — UI never freezes during heavy indexing — Supports PDF, DOCX, and TXT file formats — Zero external APIs — runs completely offline on the user's machine — 23 production-grade bugs identified and resolved before delivery This is not a demo. This is production-ready software built with clean architecture, full error handling, keyboard shortcuts, chat export, source citations, and confidence indicators
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Tkinter(4)
Alessandro Federici's avatar
Alessandro Federici
proBologna, Italy
AI & Automation Developer, I'll build your App / Automation
2x
Hired
5.0
Rating
13
Followers
AI & Automation Developer, I'll build your App / Automation
Cover image for AF Speech: Offline AI Desktop App Development
AF Speech: Offline AI Desktop App Development
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16
Cover image for AI Voice Booking Agent
AI Voice Booking Agent
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14
Cover image for AI Cold Outreach Platform
AI Cold Outreach Platform
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5
Cover image for Business Analyzer (AI Report)
Business Analyzer (AI Report)
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3
Tkinter(1)
Kseniya Mikhno's avatar
Kseniya Mikhno
Brisbane City QLD 4000, Australia
Python Developer | IoT, ML & Full Stack
New to Contra
Python Developer | IoT, ML & Full Stack
Cover image for Built a desktop application for
Built a desktop application for managing repair jobs, designed for small businesses that need to track incoming repairs, parts, costs, and customer information in one place. The application features a clean interface for logging new repair jobs, tracking their status through completion, managing customer records, and generating reports. All data is stored in a SQL database with full CRUD operations. Key features include job intake and status tracking, customer and parts management, cost calculation and invoicing, and reporting for business insights. Built with Python for the application logic and UI, and SQL for persistent data storage.
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Cover image for Developed and integrated a machine
Developed and integrated a machine learning system for geospatial mineral exploration analysis in collaboration with an industry partner. The system processes terrain and geological data to identify potential mineral deposits. The project involved building data pipelines to ingest and clean geospatial datasets, training machine learning models to recognize patterns associated with mineral deposits, and creating visualizations of the analysis results. Key features include automated geospatial data processing, machine learning model training and evaluation, integration with industry partner workflows, and visual output of exploration analysis results. The tech stack included Python for data processing and model development, SQL for data management, and machine learning libraries for predictive analysis.
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Cover image for Developed an IoT-based communication system
Developed an IoT-based communication system that converts web-submitted text messages into Morse code signals transmitted via LED using Raspberry Pi GPIO controls. The goal was to build a system that bridges web-based text input with physical hardware output, demonstrating IoT communication principles through a practical Morse code implementation. I built a web interface where users submit text messages. The backend processes the text, converts each character into its Morse code equivalent, and sends the signal pattern to Raspberry Pi GPIO pins. An LED physically blinks the Morse code in real time. Key features include a web-based text input interface, real-time text-to-Morse-code conversion, Raspberry Pi GPIO integration for LED signal output, and a full-stack architecture connecting the web layer to the hardware layer. The tech stack included Python for backend logic and GPIO control, Raspberry Pi for hardware interfacing, and web technologies for the user-facing input form.
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Cover image for Developed a web-based job tracking
Developed a web-based job tracking system for a waterproofing business, allowing clients to monitor project progress through a secure web portal accessed via unique QR codes. The waterproofing business needed a way for their clients to track the status of ongoing projects without constant phone calls or emails. They wanted something simple, accessible, and secure. Built a full-stack web application where each job gets a unique QR code. Clients scan the code and are taken to a secure portal showing real-time progress updates on their project. The backend handles job management, status updates, and client authentication. Key features include QR code generation linked to individual jobs, a secure client portal for real-time project tracking, an admin dashboard for managing jobs and updating statuses, and a web-based interface accessible from any device. Python for backend logic, SQL for data management, and web technologies for the client-facing portal.
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Tkinter(1)
Tannu Antil's avatar
Tannu Antil
Delhi, India
developer
New to Contra
developer
Cover image for Shoplifting Detection System (Computer Vision) The
Shoplifting Detection System (Computer Vision) The Shoplifting Detection System is an AI-powered surveillance solution designed to identify suspicious behavior in retail environments using computer vision and deep learning. The system analyzes real-time video from CCTV cameras to detect potential theft activities. It was trained on a dataset of 70,000+ images and uses a YOLO-based object detection model to recognize suspicious actions. When abnormal behavior is detected, the system triggers automated alerts and saves video clips for further review, improving retail security and loss prevention.
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Cover image for Signify - ISL Signify is a
Signify - ISL Signify is a web-based AI system designed to translate Indian Sign Language (ISL) gestures into text and audio in real time, helping bridge communication gaps for people with hearing and speech impairments. The project uses computer vision and machine learning to detect hand gestures through a camera and convert them into understandable language. The system was trained on a dataset of over 260,000 gesture images, enabling accurate recognition of different ISL signs. The model processes live video input, identifies hand gestures, and instantly generates corresponding text and speech output. The platform also includes learning features such as quizzes and a sign dictionary, making it useful for both communication and educational purposes. The project focuses on real-time image processing, gesture recognition, and accessibility-driven AI solutions.
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Cover image for ✨ Welcome to Aurelia Pixie
✨ Welcome to Aurelia Pixie 🌸🖤 A creative fashion and aesthetic inspiration space where every post is designed with elegance, style, and personality. This project focuses on creating high-quality posters, fashion edits, moodboards, and trendy visual content that blends soft aesthetics with bold modern vibes. From dreamy outfits and Pinterest-inspired designs to aesthetic lifestyle visuals, every post is carefully crafted to inspire creativity and style lovers worldwide. ✨ Follow the journey of aesthetic storytelling through visuals, fashion inspiration, and beautifully designed content. Aurelia Pixie Website Pinterest: @aureliapixie01
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Cover image for Aureli Apixie (https://aureli-apixie.lovable.app) is a modern
Aureli Apixie (https://aureli-apixie.lovable.app) is a modern AI-powered web experience built using the Lovable platform, focused on sleek design, interactive user experience, and smart digital functionality. The site reflects a clean aesthetic with a futuristic and minimal interface, making it feel premium and visually engaging. It appears to combine: Elegant UI/UX styling Responsive web design AI-integrated workflow or automation features Smooth navigation and lightweight performance Startup-style branding with a modern creative feel The platform is likely built using Lovable AI, an AI website and app builder that allows creators to generate websites and apps through prompts and natural language. Lovable is known for helping users quickly build full-stack apps, landing pages, dashboards, and interactive tools without heavy coding knowledge.  Overall, Aureli Apixie gives the vibe of: an AI startup landing page, a digital product showcase, or a futuristic creative-tech brand website. It uses a polished, soft-modern visual identity that fits current 2026 web design trends such as: glassmorphism, minimal luxury layouts, neutral palettes, AI-inspired branding, and immersive scrolling experiences.
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Tkinter(1)
Vaibhav Mishra's avatar
Vaibhav Mishra
Faridabad, India
A Professional Full Stack Developer
A Professional Full Stack Developer
Cover image for Face Recognition and Verification System Development
Face Recognition and Verification System Development
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1
Cover image for OCR Python Script Development
OCR Python Script Development
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2
Cover image for Rice Grain Species Classification using Machine Learning
Rice Grain Species Classification using Machine Learning
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2
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Prajjwal Barmaiya's avatar
Prajjwal Barmaiya
Delhi, India
punctual, motivated, hardworker, Quick Learner
punctual, motivated, hardworker, Quick Learner
Cover image for Automatic Attendance System with Face Recognition
Automatic Attendance System with Face Recognition
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3
Cover image for RISC-V Based Single-Cycle Core Design
RISC-V Based Single-Cycle Core Design
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3
Cover image for Music-Player-Using-HTML-And-CSS
Music-Player-Using-HTML-And-CSS
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5
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Aaron Segiel's avatar
Aaron Segiel
Boca Raton, USA
AI engineer and Web Developer
AI engineer and Web Developer
Cover image for AI Code Reviewer Application Development
AI Code Reviewer Application Development
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20
Cover image for Web Scraping for News Titles
Web Scraping for News Titles
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14
Cover image for CIFAR-10 Image Classification with CNN
CIFAR-10 Image Classification with CNN
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12
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