Projects using Python in GurugramProjects using Python in Gurugram
Projects
People
Cover image for That makes total sense. A
That makes total sense. A lot of portfolio platforms like Contra don't support LaTeX rendering, so those equations just end up looking like broken code. Here is the revised version with the math translated into clean, readable plain text so it formats perfectly on the site. AuraOps: The Autonomous Unified Release Authority 🚀🧠 Moving AI from "Assisting Developers" to "Making Production Decisions" Modern CI/CD pipelines are reactive. They lint, test, and warn—but the final decision still depends on a human. As DevOps complexity grows with security risks, compliance requirements, and sustainability concerns, developers are overwhelmed. We asked a simple question: What if the pipeline itself could decide whether code is safe to ship? That idea led to AuraOps—a multi-agent AI system integrated directly into GitLab Merge Requests that doesn’t just analyze code, but actively fixes it, verifies it, and makes the final release decision. 💡 How It Works: The Autonomous Pipeline AuraOps intercepts GitLab webhooks when a Merge Request is opened, extracts the code diff, and triggers a 3-phase autonomous pipeline powered by specialized AI agents. The Execution Flow: Phase 1 (Parallel): Security & Sustainability Analysis Phase 2 (Sequential): Validation & Risk Decision Phase 3 (Parallel): Compliance Checks & Deployment 🤖 The Multi-Agent AI System AuraOps orchestrates several distinct agents to handle the entire lifecycle: SecurityAgent: Detects vulnerabilities (SQLi, XSS, secrets), auto-remediates them by writing and committing patches, and re-validates its own fixes. GreenOpsAgent: Optimizes infrastructure using real-time carbon data and suggests lower-emission deployment regions. ValidationAgent: Runs the GitLab CI/CD pipeline to ensure AI-generated fixes didn’t break functionality, with graceful fallbacks if CI is down. ComplianceAgent: Audits code and deployment against SOC2, GDPR, and HIPAA requirements. DeployAgent: Builds and deploys the application to Google Cloud Run, selecting the greenest, most optimal region. RiskEngine (The Brain): The decision-making core that aggregates all signals into a single release scorecard and outputs a definitive APPROVE or BLOCK. 🧮 The RiskEngine Decision Model To confidently block or approve a release without human input, we engineered a weighted decision model to generate a final confidence score. The core confidence score combines three key weighted metrics: Security Score * Eco (Sustainability) Score * Validation Result The final AI decision function evaluates this combined score against a strict threshold. For example, if the total confidence score is 75% or higher, the release is securely shipped and marked as APPROVE. If it falls below that mark, the system automatically outputs a BLOCK decision to prevent risky deployments. 🧗‍♂️ Engineering Challenges Reliable Auto-Remediation: Detecting issues is easy; safely fixing them is not. We implemented multi-pass revalidation (up to 3 cycles) and auto-commits directly to GitLab. Failure Resilience: Real-world systems fail. We built AuraOps with graceful degradation—handling CI failures by safely skipping them, retrying API rate limits with exponential backoff, and bypassing missing configs without crashing. Sustainability as a Metric: Mapping cloud infrastructure to real-time carbon intensity APIs to quantify exact CO₂ savings. 🚀 The Final Output: The Release Scorecard Instead of overwhelming the developer with logs, AuraOps outputs a clean, aggregated scorecard directly in the MR containing: Security score & vulnerabilities auto-fixed Sustainability index & CO₂ emissions avoided Time saved via automation Final AI Decision + Confidence Percentage 💻 Built With AI Models: Gemini 2.5 Flash | Gemini 3.5 Pro | Claude 3.5 Sonnet Backend & Orchestration: Python | FastAPI | Uvicorn | Node.js | TypeScript Frontend & 3D Vis: React | Three.js (React Three Fiber) | CSS DevOps & Cloud: Docker | GitLab API & Webhooks | GitLab CI | Google Cloud Build | Google Cloud Run Try AuraOps : https://auraops-735853806237.europe-north1.run.app/dashboard
1
119
Cover image for Hi Contra community! Just wrapped a
Hi Contra community! Just wrapped a rewarding project for Nauvyashree—delivered deep-dive EDA, custom visualizations, and rich survey sentiment analysis across four datasets. Each milestone included well-designed PDFs packed with clear charts and approachable explanations—making insights accessible for all audiences. I went the extra mile with collaborative Google Meet sessions to walk the client through every analysis, helping connect the findings to big-picture goals. Feedback was fantastic, and milestone-based payments made the process smooth and transparent. This project really strengthened my ability to turn complex results into stories that empower client decision-making. If you want visually engaging, client-focused analytics—or need easy-to-follow reports and hands-on walkthroughs for your next project—let’s connect!
1
88
Cover image for The Problem: Sales teams waste 60%
The Problem: Sales teams waste 60% of their time researching leads instead of closing them. The Solution: I built a custom Agentic AI Pipeline that automates deep-dive business intelligence and lead scoring. Key Technical Highlights: Multi-Agent Architecture: Built using CrewAI, featuring a 'Business Intelligence Specialist' (for real-time research) and a 'Senior Sales Director' (for strategic scoring). High-Speed Intelligence: Powered by Llama 3.3-70B  for near-instant reasoning and decision-making. Real-time Web Scoping: Integrated Tavily AI to fetch live revenue data, employee counts, and market positioning. Enterprise Storage: A robust SQLite backend to manage lead pipelines with a sleek Streamlit dashboard. Smart Throttling: Engineered custom rate-limiting and token-trimming logic to ensure 99.9% uptime even under heavy API constraints. How it works: Simply enter a company name and URL. The AI agents scour the web, analyze the company's "AI potential," calculate a priority score (0-100), and even write a personalized sales pitch—all in under 30 seconds.
0
78
Cover image for RetailEye Insights – AI-Powered Retail
RetailEye Insights – AI-Powered Retail Analytics & Live Monitoring Platform Overview RetailEye Insights is a real-time retail intelligence platform developed for GMR Hyderabad International Airport to help retailers understand customer behaviour, monitor store activity, and make data-driven business decisions using AI-powered video analytics. The platform aggregates data from multiple cameras across retail stores and presents actionable insights through an interactive dashboard, enabling store managers and administrators to monitor footfall, customer demographics, occupancy, and live activity from a centralized interface. My Role Frontend Developer (React.js) I was responsible for designing and developing the complete frontend application, integrating real-time APIs, building reusable UI components, optimizing performance, and creating interactive analytics dashboards for enterprise users. Key Features 📊 Real-Time Analytics Dashboard Developed an interactive analytics dashboard that provides: Live footfall tracking Customer entry and exit statistics Hourly traffic trends Store-wise analytics Peak business hours Occupancy monitoring Real-time KPI cards Historical trend analysis 👥 Customer Demographics Implemented AI-powered demographic visualizations including Gender distribution Age group classification Customer segmentation Hourly demographic trends Comparative analytics These insights help retailers understand customer behaviour and optimize staffing and marketing strategies. 🎥 Live Camera Monitoring Built live monitoring interfaces allowing administrators to: View multiple camera feeds Monitor stores in real time Switch between camera locations Observe customer activity instantly Access centralized surveillance dashboards 📈 Interactive Data Visualization Created responsive and interactive charts for: Footfall over time Gender trends Age distribution Customer activity Historical reports Features include: Dynamic filtering Hover tooltips Responsive layouts Smooth chart animations 🏬 Multi-Store Management Implemented support for multiple retail locations with: Store selection Centralized monitoring Individual store analytics Cross-store comparison Unified management dashboard 🔍 AI Person Tracking & Debug Console Developed an advanced debugging interface for AI detection pipelines that displays: Active tracking sessions Identified and unidentified persons Face detection quality Re-identification confidence Tracking lifecycle Camera pipeline status Frame statistics Live processing information This interface significantly improved monitoring and debugging of the AI vision system during development. 👤 User & Access Management Implemented secure administrative modules for: User management Role-based access Authentication Administrative controls Secure dashboard access Technical Highlights Developed a scalable component-based architecture Integrated REST APIs with real-time polling Optimized rendering performance for large datasets Built reusable UI components Implemented responsive layouts for enterprise users Managed application state efficiently Created modular analytics widgets Improved dashboard loading and rendering performance Frontend Tech Stack React.js TypeScript Tailwind CSS React Query REST APIs Chart.js / Recharts (Analytics Visualizations) Material UI Responsive UI Design Backend Tech Stack Python FastAPI PostgreSQL SQLAlchemy JWT YOLOv8 InsightFace TorchReID OpenCV ONNX Runtime MinIO Outcome RetailEye Insights provides retailers with real-time visibility into customer behaviour through AI-powered video analytics, helping improve operational efficiency, optimize staffing, understand shopper demographics, and make informed business decisions across multiple retail locations.
0
10
Cover image for Now, meet the one I
Now, meet the one I already told you all my project jarvis which is complete now. Here's the details - JARVIS is a production-grade, privacy-first AI desktop assistant designed to operate fully locally on standard hardware. Built to rival modern desktop AI systems, JARVIS seamlessly integrates natural voice interaction, real-time screen vision comprehension, deep Windows OS automation, and intelligent document/presentation generation. Unlike basic wrappers around cloud APIs, JARVIS features a hybrid architecture combining zero-latency regex intent routing, local LLM fallbacks, Win32 API shell controls, and active VLM screen verification. Key Capabilities of Jarvis - 🎙️ Multimodal Voice & Audio Intelligence Bilingual STT & Dynamic Query Cleaning: Real-time speech recognition tuned for Hinglish, Hindi, and English with automatic phonetic filler word stripping. Expressive Local TTS & Emotion Effects: Low-latency neural speech synthesis powered by Piper ONNX and Edge TTS with adaptive prosody and emotional modulation. Hands-Free Media & Non-API Automation: Full hardware media key automation for Spotify and browser video playback without requiring paid API tokens. 👁️ Vision AI & Live Screen Comprehension VLM Window & Screen Verification: Captures active window frames using OpenCV and local vision models (Moondream / Qwen-VL/Mistral) to verify OS tasks (e.g., verifying opened folders, app states, or UI elements). Camera Emergency Sentinel: Real-time visual distress sentinel using multimodal vision checks before initiating priority emergency calls. 🖥️ Deep OS & File System Automation Subfolder Inspection & Bulk Purging: Inspects complex nested folder structures (e.g., Pictures/Screenshots), calculates storage footprints, and executes secure file/folder purges via Win32 shell calls. OneDrive-Aware Name-Based Resolution: Intelligent 3-tier lookup engine resolving standard paths (Desktop, Downloads, Pictures) across native paths and OneDrive redirects without needing absolute user paths. Silent Recycle Bin Clean & Disk Optimization: Win32 API integration (SHEmptyRecycleBinW) for 100% silent, error-free disk maintenance. 📄 Productivity & AI Document Generation Automated Presentation Engine: Generates styled PowerPoint presentations (.pptx) with custom slide themes, topic summaries, and automated asset downloads. Markdown & PDF Document Compiler: Built-in Marp compilation engine converting voice notes to polished PDF slides and documents. And not only that I have open-sourced the entire github repo you can install it, check it and run it to your laptop as your assistant too, and don't forget to give the star, and if you face any issue kindly dm me or message in github too. Here's the link - "https://github.com/darshitp091/Jarvis "
2
150
Cover image for Hit a classic Django trap
Hit a classic Django trap this week and figured it's worth sharing since so many people are running into it now. I was adding an LLM feature to a Django app. The AI call takes a few seconds, so naturally I made the view async so it doesn't block a worker while waiting. Wrote the async view, called the ORM like I always do, and boom: SynchronousOnlyOperation: You cannot call this from an async context. Turns out Django's ORM can't just be called normally inside async code. The classic sync API isn't safe in an event loop, so Django protects it and throws this error instead. The fix is simpler than most people think. Since Django 4.1 the ORM has async versions of everything, same names with an "a" prefix. So objects.get() becomes await objects.aget(), create() becomes acreate(), save() becomes asave(). For loops over querysets, async for works directly. And for old sync code or third party libraries you can't change, wrap them with sync_to_async(). Why this matters right now: everyone is bolting AI features onto Django apps, and LLM calls are exactly the slow I/O that async is made for. Which means a lot of devs who never touched async Django are suddenly hitting this error for the first time. One honest caveat: transactions still don't fully work in async mode, so if you need atomic blocks, keep that path sync and wrap it. Anyone else made the jump to async views yet, or still happily on WSGI?
0
81