Freelancers using Python in Lahore

Freelancers using Python in LahoreFreelancers using Python in Lahore

Projects People

proLahore, Pakistan

AI Integration & Automation Engineer | Full-Stack Web Apps

$50k+: Earned

65x: Hired

4.9: Rating

127: Followers

Expert

Expert

+2

AI Integration & Automation Engineer | Full-Stack Web Apps

Cover image for Autonomous Multi-Agent Market Research System Development

Autonomous Multi-Agent Market Research System Development

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12

Cover image for Magnai | UK Public Affairs

Magnai | UK Public Affairs

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68

Cover image for Humoni - secure housing in under 72 hours

Humoni - secure housing in under 72 hours

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117

Cover image for Wellbeing Wizard AI

Wellbeing Wizard AI

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180

Lahore, Pakistan

AI/ML & Data Solutions Engineer

New to Contra

AI/ML & Data Solutions Engineer

Cover image for Developed a full-stack language learning

Developed a full-stack language learning application tailored for Luxembourgish, combining speech recognition, natural language understanding, and generative AI. Fine-tuned OpenAI’s Whisper model for accurate Luxembourgish transcription and built a custom text-to-speech (TTS) engine for realistic audio feedback. A RAG-based architecture enables the app to answer user queries contextually, making learning highly interactive. The frontend is built with React, while Flask powers the backend. Designed to deliver an immersive, conversation-driven auditory learning experience.

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Cover image for Hey Contra!
Excited to finally

Hey Contra! Excited to finally be here and connect with builders, founders, and innovators from around the world. I'm Osman, an AI Engineer specializing in AI Agents, LLM Applications, Automation Systems, and Full-Stack Development. Over the past 3+ years, I've helped businesses transform ideas into intelligent products using OpenAI, Gemini, LangChain, LangGraph, FastAPI, Django, and modern cloud technologies. Some of the solutions I've built include: • AI Agents and Multi-Agent Systems • RAG Applications with Private Knowledge Bases • AI-Powered SaaS Products • Automation Workflows with n8n, Zapier, and Make • Voice AI Agents with Twilio and ElevenLabs • Custom Chatbots and Internal AI Tools • Data Automation and Web Scraping Systems I enjoy solving complex problems and building products that create real business value—not just demos. Looking forward to connecting with founders, startups, agencies, and teams working on exciting AI projects. If you're building something ambitious with AI, let's talk.

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Cover image for Worked on an RLHF (Reinforcement

Worked on an RLHF (Reinforcement Learning from Human Feedback) pipeline focused on dataset creation, data annotation, and model evaluation. My role involved designing and curating high-quality prompt datasets, reviewing AI-generated responses, and providing structured feedback based on accuracy, relevance, safety, and helpfulness. Contributed to improving model performance by ensuring consistent evaluation standards and high-quality human feedback for training alignment and refinement.

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Cover image for Trained a DreamBooth LoRA model

Trained a DreamBooth LoRA model to generate high-quality, personalized image outputs with consistent subject identity across different prompts and styles. The project involved dataset preparation, image captioning, and fine-tuning diffusion models using LoRA for efficient training and deployment. The solution enables fast generation of customized visuals while preserving subject consistency, style control, and high fidelity, suitable for creative, branding, and content generation use cases.

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Muhammad Ahmad Mansoor

Lahore, Pakistan

Engineering intelligent solutions with AI.

5.0: Rating

2: Followers

Engineering intelligent solutions with AI.

Cover image for 🚀 Built an AI Receptionist

🚀 Built an AI Receptionist for Healthcare — Here's What It Can Do Managing a healthcare front desk is challenging. Calls, appointment scheduling, patient questions, and administrative tasks often overwhelm staff, leading to long wait times and missed opportunities to deliver a great patient experience. To address this, I built an AI Receptionist MVP designed specifically for healthcare providers. It can answer calls, schedule appointments, manage inbound and outbound communication, and respond to patient inquiries—all while staying within appropriate clinical boundaries. Here's what it demonstrated during a live test with our demo clinic, Evergreen Community Health Center: 🧠 Intelligent Patient Conversations Rather than following a fixed script, the AI understands context and knows its limitations. ✅ Recognized that dermatology wasn't offered and suggested available services instead: General Practice Pediatrics Physical Therapy Dental Care ✅ Clearly avoided giving medical advice by responding: "As a receptionist, I'm not qualified to provide medical advice." ✅ Offered the appropriate next steps by either: Booking a GP appointment for an initial assessment, or Transferring the caller to clinical staff for medical questions. 📅 Smart Appointment Scheduling The AI schedules appointments based on both clinic policies and patient preferences. During the demo, it: Collected the patient's information. Suggested the next available appointment. Adjusted the booking when the patient requested a later time. Checked clinic operating hours automatically and successfully booked a 5:00 PM appointment before closing. 💳 Administrative Automation Beyond booking appointments, the AI also handled routine administrative tasks by: Explaining consultation fees. Answering policy-related questions. Confirming referral procedures. Collecting the patient's email for appointment confirmation. ⚡ Built for Scale Unlike a traditional reception desk, the AI can handle thousands of conversations simultaneously. That means: No busy signals No waiting queues No missed calls Better patient accessibility 24/7 ⚙️ Easily Customizable Every healthcare provider operates differently. The AI can be configured with: Clinic-specific services Staff availability Business hours Appointment rules FAQs Internal workflows making it adaptable to virtually any healthcare organization. This project demonstrates how conversational AI can streamline healthcare operations while allowing staff to spend more time focusing on patient care. I'm excited to continue expanding its capabilities with integrations such as EMR/EHR systems, multilingual support, voice biometrics, and intelligent call routing. 💬 If you're exploring AI solutions for healthcare, I'd love to connect and discuss how conversational AI can modernize patient engagement. #AI #ArtificialIntelligence #HealthcareAI #HealthTech #VoiceAI #AIReceptionist #Automation #ConversationalAI #PatientExperience #GenerativeAI #LLM #Innovation

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Cover image for I just completed a production-ready

I just completed a production-ready AI Lead Generation Agent designed to automate how businesses discover, verify, and export qualified leads across multiple data sources. This system replaces manual lead hunting with an intelligent, filter-driven pipeline that aggregates business data, verifies contact information, and delivers CRM-ready outputs in real time. 🔑 What this agent does: • Searches businesses by location, industry, and keywords • Aggregates data from Yellow Pages, Google Maps (API-ready), and extensible sources • Applies smart filters (company size, founding date, industry relevance) • Automatically verifies emails, phones, and websites • Deduplicates leads for clean datasets • Exports structured CSVs for sales & marketing teams • Supports real-time queries via a FastAPI backend • Schedules daily exports and weekly reports 🧠 Tech Stack Highlights: • Python + FastAPI (async, high-performance backend) • Selenium-based scraping with anti-bot handling • Modular lead source orchestration • Glassmorphism UI with real-time search • CSV-based persistence (lightweight & scalable) This project was built with real business use cases in mind — sales pipelines, outreach automation, and scalable lead discovery — not just experimentation. 🎥 Full walkthrough video: 👉 I’m actively building and sharing end-to-end AI systems focused on automation, data intelligence, and real-world impact. hashtag#AI (https://www.linkedin.com/search/results/all/?keywords=%23ai&origin=HASH_TAG_FROM_FEED) hashtag#ArtificialIntelligence (https://www.linkedin.com/search/results/all/?keywords=%23artificialintelligence&origin=HASH_TAG_FROM_FEED) hashtag#LeadGeneration (https://www.linkedin.com/search/results/all/?keywords=%23leadgeneration&origin=HASH_TAG_FROM_FEED) hashtag#Automation (https://www.linkedin.com/search/results/all/?keywords=%23automation&origin=HASH_TAG_FROM_FEED)hashtag#SoftwareEngineering (https://www.linkedin.com/search/results/all/?keywords=%23softwareengineering&origin=HASH_TAG_FROM_FEED) hashtag#FastAPI (https://www.linkedin.com/search/results/all/?keywords=%23fastapi&origin=HASH_TAG_FROM_FEED) hashtag#WebScraping (https://www.linkedin.com/search/results/all/?keywords=%23webscraping&origin=HASH_TAG_FROM_FEED)hashtag#SaaS (https://www.linkedin.com/search/results/all/?keywords=%23saas&origin=HASH_TAG_FROM_FEED) hashtag#Startup (https://www.linkedin.com/search/results/all/?keywords=%23startup&origin=HASH_TAG_FROM_FEED) hashtag#Entrepreneurship (https://www.linkedin.com/search/results/all/?keywords=%23entrepreneurship&origin=HASH_TAG_FROM_FEED)hashtag#TechProjects (https://www.linkedin.com/search/results/all/?keywords=%23techprojects&origin=HASH_TAG_FROM_FEED) hashtag#AIProjects (https://www.linkedin.com/search/results/all/?keywords=%23aiprojects&origin=HASH_TAG_FROM_FEED) hashtag#BuildInPublic (https://www.linkedin.com/search/results/all/?keywords=%23buildinpublic&origin=HASH_TAG_FROM_FEED)hashtag#BusinessGrowth (https://www.linkedin.com/search/results/all/?keywords=%23businessgrowth&origin=HASH_TAG_FROM_FEED) hashtag#SalesTech (https://www.linkedin.com/search/results/all/?keywords=%23salestech&origin=HASH_TAG_FROM_FEED) hashtag#B2B (https://www.linkedin.com/search/results/all/?keywords=%23b2b&origin=HASH_TAG_FROM_FEED)

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Cover image for Building the Future of Hiring:

Building the Future of Hiring: Real-Time AI Interviews I’m excited to share a demo of our AI-based recruiter automation tool, designed to streamline recruitment funnels with real-time video interviews, deep skill analytics, and cheating detection. In this video, I demonstrate the full candidate journey: 🔹 Backend Automation: The system extracts skills from my resume and matches them against the job description automatically. 🔹 Interactive Interview: I speak directly with "Higher Vision AI" about my experience in Computer Vision, GenAI, and Agentic AI across industries like security surveillance and e-commerce. 🔹 Technical Deep Dives: We discuss real-world challenges, such as implementing voice-to-voice interaction using LiveKit and how I adapted to new integration hurdles. 🔹 Instant Feedback: The session wraps up with a comprehensive dashboard displaying my interview score, resume score, summary, and skills audit. I also discuss how I stay ahead of trends using resources like daily.dev (http://daily.dev). 🎧 Note: Please excuse the slight echo on the agent's voice in this screen recording; it is a result of the recording setup, not the live system! #AgenticAI (https://www.linkedin.com/search/results/all/?keywords=%23agenticai&origin=HASH_TAG_FROM_FEED) #TalentAcquisition (https://www.linkedin.com/search/results/all/?keywords=%23talentacquisition&origin=HASH_TAG_FROM_FEED) #ComputerVision (https://www.linkedin.com/search/results/all/?keywords=%23computervision&origin=HASH_TAG_FROM_FEED) #PeopleAnalytics (https://www.linkedin.com/search/results/all/?keywords=%23peopleanalytics&origin=HASH_TAG_FROM_FEED) #VoiceAI (https://www.linkedin.com/search/results/all/?keywords=%23voiceai&origin=HASH_TAG_FROM_FEED) #FutureOfWork (https://www.linkedin.com/search/results/all/?keywords=%23futureofwork&origin=HASH_TAG_FROM_FEED) #SkillAssessment (https://www.linkedin.com/search/results/all/?keywords=%23skillassessment&origin=HASH_TAG_FROM_FEED) #InterviewIntelligence (https://www.linkedin.com/search/results/all/?keywords=%23interviewintelligence&origin=HASH_TAG_FROM_FEED) #LLM (https://www.linkedin.com/search/results/all/?keywords=%23llm&origin=HASH_TAG_FROM_FEED) #SecureHiring (https://www.linkedin.com/search/results/all/?keywords=%23securehiring&origin=HASH_TAG_FROM_FEED) #TechRecruitment (https://www.linkedin.com/search/results/all/?keywords=%23techrecruitment&origin=HASH_TAG_FROM_FEED)

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Cover image for I wanted to make an

I wanted to make an AI talk to me like a real human. Not a chatbot that waits for you to finish speaking. Not a voice note exchange. I wanted an AI that could listen while I spoke, interrupt naturally, and respond instantly — like a personal assistant or customer-care agent. Something that didn’t just reply, but could actually do things — send emails, check dashboards, automate tasks. So I started small. I used a basic text model with Kokui for speech-to-text. The first time it responded, it felt magical — until it started ignoring me mid-sentence, replying late, or not speaking at all. Latency was awful. It wasn’t a conversation; it was waiting for a robot to remember it existed. I upgraded to DeepGram, tuned the audio, and still, it felt disconnected. I wanted real-time. I wanted the AI to exist in time with me, not after me. So I went deep into research. GitHub, StackOverflow, documentation black holes, even different AI platforms — but nothing gave me the real-time link I needed. Then I found one line buried in a doc: “OpenAI uses LiveKit for real-time voice.” That changed everything. LiveKit works like Zoom — a room where participants join and talk. I built my agent to join a LiveKit room, then I joined as well. And for the first time, I wasn’t sending messages to a server — I was talking to an AI inside the same space. The first test stunned me. Latency dropped to about 50ms. The AI listened while I spoke, and responded instantly. For the first time, it felt alive. Then came a strange bug. The AI joined once, but couldn’t reconnect after leaving. I rewrote code, regenerated tokens, nothing worked — until I realized LiveKit doesn’t let you rejoin a room with the same token. I changed the room name and boom — it worked flawlessly. Now it runs inside LiveKit Sandbox, talking and listening in real-time. It can send emails, check dashboards, handle automation — all with almost zero delay. I started out trying to make a talking AI. What I built feels more like a digital employee — one that works, listens, and speaks in real time. And the best part? It doesn’t feel like the future anymore. It feels like now. #AI (https://www.linkedin.com/search/results/all/?keywords=%23ai&origin=HASH_TAG_FROM_FEED) #ArtificialIntelligence (https://www.linkedin.com/search/results/all/?keywords=%23artificialintelligence&origin=HASH_TAG_FROM_FEED) #LiveKit (https://www.linkedin.com/search/results/all/?keywords=%23livekit&origin=HASH_TAG_FROM_FEED) #VoiceAI (https://www.linkedin.com/search/results/all/?keywords=%23voiceai&origin=HASH_TAG_FROM_FEED) #AITools (https://www.linkedin.com/search/results/all/?keywords=%23aitools&origin=HASH_TAG_FROM_FEED) #MachineLearning (https://www.linkedin.com/search/results/all/?keywords=%23machinelearning&origin=HASH_TAG_FROM_FEED) #AIAssistant (https://www.linkedin.com/search/results/all/?keywords=%23aiassistant&origin=HASH_TAG_FROM_FEED) #AIInnovation (https://www.linkedin.com/search/results/all/?keywords=%23aiinnovation&origin=HASH_TAG_FROM_FEED) #TechDevelopment 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Lahore, Pakistan

ML AI | Backend | Computer Vision | GenAI | LLM Agents

New to Contra

ML AI | Backend | Computer Vision | GenAI | LLM Agents

Cover image for LakeShield - AI-Powered Video Monitoring

LakeShield - AI-Powered Video Monitoring and Vessel Intelligence Platform I led the development of LakeShield as the Senior AI/ML Engineer and Lead Developer, taking the platform from initial research and experimentation to a scalable production system. My responsibilities included: 🔹 Designing the end-to-end AI and video-processing architecture 🔹 Building YOLO-based boat and vehicle detection pipelines 🔹 Developing object tracking and movement-analysis workflows 🔹 Implementing OCR for extracting boat registration information 🔹 Creating scalable pipelines for processing thousands of surveillance videos 🔹 Developing FastAPI backend services and automated data workflows 🔹 Building a Next.js analytics dashboard integrated with Supabase 🔹 Deploying and operating the AI pipeline on cloud GPU infrastructure 🔹 Optimizing model accuracy, inference speed, infrastructure costs, and reliability 🔹 Managing production monitoring, troubleshooting, maintenance, and continuous improvements The platform transforms raw surveillance footage into structured operational insights, enabling automated vessel monitoring, vehicle activity analysis, registration extraction, and reporting. This project involved complete technical ownership across Computer Vision, AI/ML, backend development, cloud infrastructure, data engineering, MLOps, and production operations. #ComputerVision #VideoAnalytics #ArtificialIntelligence #ObjectDetection #OCR #MLOps #FastAPI #NextJS #Supabase #CloudEngineering

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Cover image for Shelfr - AI-Powered Retail Shelf

Shelfr - AI-Powered Retail Shelf Intelligence Platform I led the development of Shelfr as the Senior Computer Vision Engineer and Lead Developer, taking the platform from the initial idea and system architecture through development, deployment, and production operations. My work included: 🔹 Designing the complete computer vision and backend architecture 🔹 Building product detection, shelf analysis, OCR, and image-processing pipelines 🔹 Developing APIs and scalable data-processing workflows 🔹 Deploying and managing production systems on GCP cloud servers 🔹 Optimizing model accuracy, processing speed, and infrastructure performance 🔹 Managing production monitoring, reliability, troubleshooting, and ongoing improvements 🔹 Leading technical decisions across AI, backend, cloud infrastructure, and DevOps The platform converts real-world retail shelf images into structured product and shelf-level insights, helping automate retail auditing, product visibility analysis, and inventory workflows. #ComputerVision #RetailAI #LeadDeveloper #AIEngineering #GCP #MLOps #Python #CloudEngineering

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Cover image for ⚖️ Built a French Legal

⚖️ Built a French Legal AI Assistant powered by advanced RAG and LLM technology. The system enables users to ask complex legal questions and receive accurate, context-aware answers grounded in French legal documents. Key features include: 🔹 Custom legal document ingestion and chunking 🔹 Metadata-based vector search 🔹 Hybrid retrieval and reranking 🔹 Agentic RAG workflows using LangGraph 🔹 Source-grounded answers with legal references 🔹 Private deployment on an Azure VM using locally hosted LLMs The main focus was improving retrieval accuracy, reducing hallucinations, and making large collections of legal documents easier to search and understand. #LegalAI #RAG #LLM #ArtificialIntelligence #LangGraph #Azure #GenerativeAI #MachineLearning

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Cover image for AI-Powered PDF Data Extraction
My role:

AI-Powered PDF Data Extraction My role: AI Data Processing and Extracton Engineer Organizations often struggle to extract structured and useful information from large volumes of unstructured PDF documents. I developed a flexible AI-powered data extraction solution that allows users to define the specific entities and fields they want to retrieve. The system processes different PDF formats, identifies relevant information, and converts it into structured, usable data. The solution reduces manual document processing, improves retrieval accuracy, and can be adapted to different document types and business requirements. A working demo link is attached.

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Lahore, Pakistan

Scaling B2B revenue via advanced agentic automation.

New to Contra

Scaling B2B revenue via advanced agentic automation.

Cover image for Autonomous AI Voice Agent: 24/7

Autonomous AI Voice Agent: 24/7 Inbound Call & Appointment Scheduler Built an end-to-end autonomous AI Voice Agent designed to handle inbound business calls, answer FAQs with ultra-low latency, and book client appointments directly into calendar systems. 🛠️ Key Features & Technical Architecture: • Natural Conversational AI: Integrated Vapi AI for lifelike, natural-sounding voice interactions with zero awkward delays. • Custom Python Backend: Engineered robust backend logic using Python to manage dynamic call flow states, session context, and lead data. • Automated Scheduling Sync: Connected real-time webhook integrations to check live availability and instantly lock in calendar bookings during the call. • 24/7 Business Coverage: Eliminates missed inquiries and converts cold inbound calls into confirmed client appointments automatically. 💻 Tech Stack: Vapi AI, Python, Webhooks, REST APIs, Calendar Integration

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Cover image for Premium 3D Dental Infrastructure: Dynamic

Premium 3D Dental Infrastructure: Dynamic AI-Driven Conversion Engine The Objective: To modernize patient acquisition for high-end clinical practices by replacing outdated, static web layouts with an immersive, high-converting digital ecosystem. The Solution: Engineered a premium, fully responsive dental web infrastructure designed to capture high-value leads automatically. Moving completely away from traditional static pages, this platform utilizes high-performance dynamic components and fluid visual animations to build instant brand authority. The ecosystem features seamless lead generation architectures paired with a fully integrated, highly autonomous Agentic AI Chatbot to guide users from initial discovery to booking. Core Capabilities & Tech Highlights: Dynamic 3D Web Experience: Implements cutting-edge animations and interactive UI layers that break the mold of standard medical websites, significantly maximizing user session duration. Agentic AI Integration: Features an intelligent conversational agent—not a basic keyword-matching chatbot—capable of interpreting complex patient inquiries, qualifying leads, and handling advanced intent workflows autonomously. High-Conversion Lead Capture: Strategically positioned, interactive lead generation forms mapped to user behavior to maximize data capture while minimizing friction. Automated Intake Pipelines: Built to capture user interactions and route pre-qualified inquiry data directly to back-end administrative systems, removing operational drag. The Impact: Developed a scalable, high-retention blueprint for the healthcare sector that transforms passive web traffic into automated, pre-qualified customer pipelines.

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Cover image for AI Commerce Agent: Dynamic Product

AI Commerce Agent: Dynamic Product Retrieval System The Objective: To bridge the gap between inspiration and purchase by creating a seamless, agentic shopping experience for an e-commerce brand (Home Vibe Club). The Solution: Standard chatbots only return text, creating friction for the user. To solve this, I architected an advanced AI Commerce Agent capable of executing complex retrieval tasks. Instead of just answering questions, this agent actively queries the product database and renders interactive, highly visual product cards directly within the chat interface. Core Capabilities & Tech Highlights: Agentic Workflows: The AI autonomously decides when to trigger a product search based on user intent. Dynamic UI Rendering: Automatically injects clickable, aesthetic product cards into the conversation stream. Conversational Commerce: Reduces the barrier to entry, allowing users to browse, discover, and access products without ever leaving the chat window. High-Retention Design: Built with a dark-mode, premium aesthetic to keep users engaged and increase session duration. The Impact: Transformed a static browsing experience into an interactive, high-conversion sales pipeline.

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Cover image for Enterprise AI Outbound Engine: Programmatic

Enterprise AI Outbound Engine: Programmatic Domain Auditing & Hyper-Personalized Lead Acquisition The Objective To eliminate the extremely low conversion rates associated with generic, high-volume cold email strategies. This asset was engineered to build an automated, zero-latency outbound prospecting infrastructure that conducts programmatic technical audits on target company websites in real time. By delivering immediate, hyper-personalized value to a prospect’s inbox, the system dramatically increases cold response rates and shortens sales pipeline velocity. System Architecture & Workflow Breakdown The underlying logic is built as an asynchronous, multi-stage agentic data pipeline that seamlessly processes targets from raw data cells to a finalized outbound delivery: Stage 1: Automated Data Ingestion & Polling: The system monitors a centralized data core (Google Sheets) via automated webhooks, continuously scanning for newly appended prospect leads and tracking operational states to prevent duplicate processing. Stage 2: Programmatic Web Scraping: The workflow executes custom HTTP GET requests to isolate, scrape, and ingest live front-end code, metadata, and structural configurations directly from the target company's domain. Stage 3: Multi-Layered LLM Synthesis: The raw web payload is dynamically passed through dual-stage Large Language Model (LLM) API completion nodes. The first node isolates optimization bottlenecks on the prospect's site. The second node acts as a context-aware copywriter, translating those raw gaps into a polished, bespoke audit tailored specifically to the business owner. Stage 4: Automated SMTP Deployment: The finalized custom audit and strategic pitch are formatted into an email payload and routed through an automated mail-server node to deliver the value statement directly to the decision-maker. Stage 5: Closed-Loop State Sync: Upon successful delivery, a final write-back module updates the primary database row with full timestamp logs and execution states, establishing a clean audit trail. Core Capabilities & Tech Highlights Context-Driven Hyper-Personalization: Completely bypasses basic name-merge fields by extracting live, domain-specific text to generate authentic, high-impact value statements unique to every recipient. Token-Optimized Payload Ingestion: Employs advanced parsing to strip out irrelevant source code before sending data to the LLM core, preserving maximum contextual relevance while drastically lowering API computational costs. Scale-Ready Asynchronous Architecture: Engineered to execute complex web audits and custom draft writing sequentially across hundreds of target leads simultaneously without causing operational bottlenecks or memory overloads. Inbound Funnel Multiplier: Directly interfaces with modern outbound deliverability systems, protecting domain authority by replacing blind spam with highly targeted, consultative tech audits.

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Irtaza Ahmed Khan

Lahore, Pakistan

Machine Learning Engineer

Machine Learning Engineer

Cover image for Wasail: Demand Forecasting System

Wasail: Demand Forecasting System

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7

Cover image for Natural Language Processing

Natural Language Processing

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Time Series Forecasting

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Lahore, Pakistan

CS student & designer automating the future with AI.

New to Contra

CS student & designer automating the future with AI.

Cover image for rule-based chatbot built in Python

rule-based chatbot built in Python that replies to greetings, questions, and casual conversation using keyword matching — no AI/ML involved. Includes an advanced version with jokes, time/date, and emotion responses, plus a bonus HTML/CSS/JS chat UI. CodeAlpha Python Internship Task 4.

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Cover image for 📈 Sharing Task 2 of

📈 Sharing Task 2 of my Python Programming Internship at CodeAlpha (https://www.linkedin.com/company/codealpha/)! This project is a Stock Portfolio Tracker built in Python. The user enters stock names and quantities, and the program calculates the total investment value using a hardcoded dictionary of stock prices (like AAPL, TSLA, GOOGL, and more). It validates input, lets you add multiple stocks, displays a clean summary table and optionally saves the results to a .txt or .csv file. I also built a bonus browser-based version using HTML, CSS, and JavaScript with a live-updating portfolio table, a dropdown to select stocks and a one-click CSV download feature. Through this task, I got hands-on practice with: ✅ Dictionaries for structured data storage ✅ Input/output handling and validation ✅ Basic arithmetic for real-world calculations ✅ File handling (.txt and .csv export) ✅ Translating backend logic into an interactive front-end Thankful to CodeAlpha for this practical learning experience on to the next task! 🔗 GitHub repo link: https://lnkd.in/dswwqvYU (https://lnkd.in/dswwqvYU)hashtag#CodeAlpha (https://www.linkedin.com/search/results/all/?keywords=%23codealpha&origin=HASH_TAG_FROM_FEED) hashtag#PythonProgramming (https://www.linkedin.com/search/results/all/?keywords=%23pythonprogramming&origin=HASH_TAG_FROM_FEED) hashtag#Internship (https://www.linkedin.com/search/results/all/?keywords=%23internship&origin=HASH_TAG_FROM_FEED) hashtag#LearningByDoing (https://www.linkedin.com/search/results/all/?keywords=%23learningbydoing&origin=HASH_TAG_FROM_FEED) hashtag#SoftwareDevelopment (https://www.linkedin.com/search/results/all/?keywords=%23softwaredevelopment&origin=HASH_TAG_FROM_FEED) hashtag#StudentDeveloper (https://www.linkedin.com/search/results/all/?keywords=%23studentdeveloper&origin=HASH_TAG_FROM_FEED) hashtag#BSCS (https://www.linkedin.com/search/results/all/?keywords=%23bscs&origin=HASH_TAG_FROM_FEED)

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I'm excited to share my latest project an Email Extractor built as part of my virtual internship with CodeAlpha (https://www.linkedin.com/company/codealpha/)! Manually scouring through large blocks of text, documents, or data dumps to find contact information is incredibly time-consuming. To solve this, I built a utility tool that leverages Python and regular expressions (Regex) to instantly scan, parse and extract all email addresses from raw text data. Key Highlights: Efficient pattern matching using Regex. Saves hours of manual administrative work. Clean and modular Python codebase. This project helped me sharpen my skills in string manipulation, data parsing, and building practical automation scripts that solve real-world efficiency challenges. Check out the source code here: https://lnkd.in/er7aph2M (https://lnkd.in/er7aph2M)I would love to hear your feedback or thoughts on how to expand its functionality! hashtag#Python (https://www.linkedin.com/search/results/all/?keywords=%23python&origin=HASH_TAG_FROM_FEED) hashtag#Automation (https://www.linkedin.com/search/results/all/?keywords=%23automation&origin=HASH_TAG_FROM_FEED) hashtag#DataParsing (https://www.linkedin.com/search/results/all/?keywords=%23dataparsing&origin=HASH_TAG_FROM_FEED) hashtag#SoftwareDevelopment (https://www.linkedin.com/search/results/all/?keywords=%23softwaredevelopment&origin=HASH_TAG_FROM_FEED) hashtag#Coding (https://www.linkedin.com/search/results/all/?keywords=%23coding&origin=HASH_TAG_FROM_FEED) hashtag#Regex (https://www.linkedin.com/search/results/all/?keywords=%23regex&origin=HASH_TAG_FROM_FEED) hashtag#CodeAlpha (https://www.linkedin.com/search/results/all/?keywords=%23codealpha&origin=HASH_TAG_FROM_FEED) hashtag#Internship (https://www.linkedin.com/search/results/all/?keywords=%23internship&origin=HASH_TAG_FROM_FEED) hashtag#OpenSource (https://www.linkedin.com/search/results/all/?keywords=%23opensource&origin=HASH_TAG_FROM_FEED) hashtag#TechInnovation (https://www.linkedin.com/search/results/all/?keywords=%23techinnovation&origin=HASH_TAG_FROM_FEED)

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Cover image for 🚀 Excited to share Task

🚀 Excited to share Task 1 of my Python Programming Internship at CodeAlpha (https://www.linkedin.com/company/codealpha/)! I built a classic Hangman Game from scratch in Python the player guesses a hidden word one letter at a time with a limited number of wrong attempts before the game ends. Along the way, I worked with core programming concepts like loops, conditional logic, string manipulation and Python's random module to pick a different word each round. To take it a step further, I also built a bonus interactive version using HTML, CSS, and JavaScript complete with an animated SVG gallows that builds up with every wrong guess, a clickable on-screen keyboard, and a clean dark-themed UI. This task helped me strengthen my understanding of: ✅ Control flow (if-else, while loops) ✅ String and list handling ✅ Input validation ✅ Translating console logic into an interactive front-end Grateful to CodeAlpha (https://www.linkedin.com/company/codealpha/) for this hands-on learning opportunity! Looking forward to tackling the next tasks in this internship. 🔗 GitHub repo link: https://lnkd.in/dpN93hRV (https://lnkd.in/dpN93hRV)hashtag#CodeAlpha (https://www.linkedin.com/search/results/all/?keywords=%23codealpha&origin=HASH_TAG_FROM_FEED) hashtag#PythonProgramming (https://www.linkedin.com/search/results/all/?keywords=%23pythonprogramming&origin=HASH_TAG_FROM_FEED) hashtag#Internship (https://www.linkedin.com/search/results/all/?keywords=%23internship&origin=HASH_TAG_FROM_FEED) hashtag#LearningByDoing (https://www.linkedin.com/search/results/all/?keywords=%23learningbydoing&origin=HASH_TAG_FROM_FEED) hashtag#SoftwareDevelopment (https://www.linkedin.com/search/results/all/?keywords=%23softwaredevelopment&origin=HASH_TAG_FROM_FEED) hashtag#StudentDeveloper (https://www.linkedin.com/search/results/all/?keywords=%23studentdeveloper&origin=HASH_TAG_FROM_FEED) hashtag#BSCS (https://www.linkedin.com/search/results/all/?keywords=%23bscs&origin=HASH_TAG_FROM_FEED)

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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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