Freelancers using Streamlit in India

Freelancers using Streamlit in IndiaFreelancers using Streamlit in India

Projects People

Abu Aasif Ansari

Bhiwandi, India

I build AI agents & internal tools that act on data

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I build AI agents & internal tools that act on data

Cover image for AI-Powered Data Cleaning Tool Development

AI-Powered Data Cleaning Tool Development

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Cover image for AI-Powered Data Cleaning Tool

AI-Powered Data Cleaning Tool

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Cover image for Smart Data Analyst — AI-Powered Data Analysis App

Smart Data Analyst — AI-Powered Data Analysis App

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Cover image for PersonaSkill AI — Career Assessment Tool

PersonaSkill AI — Career Assessment Tool

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Freelance Data Scientist | Python & ML Expert

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Freelance Data Scientist | Python & ML Expert

Cover image for Fraud Transaction Detection System

Fraud Transaction Detection System

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Cover image for Hybrid AI Movie Recommendation System for Pre-2015 Films

Hybrid AI Movie Recommendation System for Pre-2015 Films

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Cover image for Lung Cancer Survival Prediction Model Development

Lung Cancer Survival Prediction Model Development

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I’m an AI & Machine Learning engineer with expertise in deve

I’m an AI & Machine Learning engineer with expertise in deve

Cover image for It all started on a

It all started on a Sunday at the AWS User Group Mumbai meetup. I wasn't expecting to walk away with a new obsession, but then the speaker introduced me to Temporal and everything changed. Temporal is a durable execution engine that solves one of the hardest problems in agentic AI what happens when your LLM workflow crashes mid-run? Normally you lose everything So I went home and built this: an agent that monitors your competitors around the clock tracking pricing changes, product launches, hiring signals, and strategic moves. Every 24 hours it uses Mistral (running fully on-device via Ollama) to analyze the data and synthesize a structured executive briefing delivered straight to your inbox. Sometimes the best projects start with a Sunday conversation. https://github.com/AnuragNagare/Agentic-AI-.git

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Cover image for launched TextGenix Enterprise — an AI-powered intelligent do...

launched TextGenix Enterprise — an AI-powered intelligent document processing system! This platform enhances and transforms documents (PDF, DOCX, TXT, HTML, RTF) with context-aware vocabulary improvements, grammar validation, and industry-specific terminology (legal, medical, financial, technical). It comes with a sleek Gradio-based web interface featuring modern styling, interactive analytics dashboards, and real-time quality metrics like semantic preservation, grammar score, and AI confidence levels. If you’re looking to build your own AI-powered text/document platform, enhance business workflows with custom NLP models, or integrate analytics-driven AI solutions into your enterprise apps I can help.

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Cover image for What every "AI gesture control"

What every "AI gesture control" demo quietly leaves out Everyone films a hand waving at a screen and calls it AI. Almost nobody shows what's underneath that there's usually no gesture model at all. A "grab" is one distance crossing a line. Here's proof. I built a jigsaw puzzle you solve with your bare hands no mouse, no controller, no gesture classifier, no training.

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Cover image for Everyone's building AR filters and

Everyone's building AR filters and calling it "computer vision magic." Almost nobody's asking what's actually happening underneath — that most of these effects are just clever masking, not detection. Here's proof. I built an invisibility cloak that runs entirely in the browser, no green screen, no chroma key, no model training. https://github.com/AnuragNagare/Ghost-frame

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AI Automation & Chatbot Developer | Flutter Developer

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AI Automation & Chatbot Developer | Flutter Developer

Cover image for Built an AI-powered resume analyzer

Built an AI-powered resume analyzer that evaluates resumes and provides instant feedback to improve job success rates. The system analyzes resume content, structure, and keywords using AI to generate a score and actionable suggestions. It helps users optimize their resumes based on industry standards and ATS (Applicant Tracking System) requirements. This tool is designed for job seekers and professionals to enhance their resumes and increase their chances of getting shortlisted. "Open to building similar AI-powered tools for businesses". 🚀

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Cover image for I built an AI chatbot

I built an AI chatbot that can answer questions from PDFs in seconds 🤯 No manual search. Just ask and get instant answers.🤔🤔Companies struggle to search information across documents manually. It wastes time and reduces productivity.👍So I built a RAG-based AI chatbot that understands documents and gives accurate answers instantly. Tach stack :- Python, FastAPI, LangChain, OpenAI API, Vector Database (FAISS) Ask questions from PDFs Context-aware answers Fast semantic search Easy UI chatbot Scalable backend

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Cover image for Raksha - Women Safety & Emergency Alert App

Raksha - Women Safety & Emergency Alert App

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Cover image for This project demonstrates an AI

This project demonstrates an AI chatbot that responds instantly to user queries and automates customer conversations. It is designed to save time, improve response speed, and capture leads without manual effort. The chatbot can be customized for websites, Instagram DMs, and other platforms based on business needs. Perfect for businesses looking to automate customer support and increase conversions.

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AI/ML Engineer crafting intelligent systems & AI solutions.

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AI/ML Engineer crafting intelligent systems & AI solutions.

Cover image for In this project, I developed a Sentiment Analysis Web App us...

In this project, I developed a Sentiment Analysis Web App using deep learning (CNN) and traditional models to classify text sentiment with high accuracy. The system includes a complete evaluation pipeline comparing CNN, LSTM, Logistic Regression, Random Forest, and Naive Bayes — analyzing performance across multiple iterations and datasets. Key Highlights: Built a Streamlit-based web app for real-time sentiment classification Developed and evaluated multiple models for accuracy and F1-score Created detailed analysis reports and prototype schematics Project here → GitHub Repository (https://github.com/Imkaran04/Sentiment_Analysis_Web_App/tree/main) Reports: Sentiment Analysis Report (PDF), Product Prototype Diagram Tech Stack: Python, Streamlit, TensorFlow/Keras, Scikit-learn, Matplotlib

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Cover image for 1. What it is: An

1. What it is: An advanced e-commerce web app that combines Graph Neural Networks (GNNs) and Generative AI to identify "toxic inventory"—products, sizes, or suppliers driving high customer returns. 2. GNN Predictive Modeling: Built a heterogeneous GNN using PyTorch Geometric to model relationships between customer demographics and product features to predict future return risks. 3. AI Attribute Enrichment: Automated a metadata extraction pipeline using the Gemini API with local caching to parse raw HTML product descriptions into structured product features (fabric, fit, pattern). 4. Interactive Dashboard & Reporting: Developed a Streamlit dashboard with Supabase Auth and integrated ReportLab to generate boardroom-ready PDF return audit reports. 5. Tech Stack: Python, PyTorch Geometric, Streamlit, Gemini API, Supabase, ReportLab.

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Cover image for Scientific Image Forgery Detection — Kaggle Competition Part...

Scientific Image Forgery Detection — Kaggle Competition Participated in the ongoing Kaggle competition on Copy-Move Forgery Detection in Scientific Images, aimed at identifying manipulated biomedical figures that can compromise research integrity. For this challenge, I developed a ResNet50 + U-Net hybrid segmentation model using PyTorch, designed to detect and segment forged regions at the pixel level. My approach combines Dice and Focal losses for balanced training, WeightedRandomSampling to oversample forged images, and Test-Time Augmentation (TTA) to improve prediction robustness. Achieved an initial score of 0.303 on the public leaderboard. I’m continuing to experiment with architecture tuning, learning rate schedules, and other loss functions to further enhance performance and generalization.

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Cover image for Introducing QuickSynopsis, a fully-featured AI-based summari...

Introducing QuickSynopsis, a fully-featured AI-based summarization and text comparison web app designed for speed, simplicity, and scalability. This project lets users: Generate efficient, context-aware summaries for any text. Compare multiple Summaries to highlight key differences. Enjoy a responsive UI with user authentication. Built using Python (Flask), HTML/CSS/JS, and SQLite/MySQL, QuickSynopsis can easily be customized or deployed to your preferred cloud platform. Key Features: AI-powered summarization & text comparison Signup/login authentication Integrated payment gateway (customizable) Responsive, modern UI/UX Ready-to-deploy setup for Heroku, AWS, or local hosting Explore the repo: GitHub – QuickSynopsis-Version-Control (https://github.com/Imkaran04/QuickSynopsis-Version-control)

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Trashu Vashisth

Building Production-Grade AI Agents & RAG Systems

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Building Production-Grade AI Agents & RAG Systems

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.

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Cover image for An autonomous AI system that

An autonomous AI system that turns a simple voice command into a deep-dive research report in seconds. No typing, no manual searching. Key Highlights: Voice Control: Uses Speech-to-Text for hands-free research triggers. Multi-Agent Intelligence: Powered by CrewAI & Llama 3.3 (Groq) to find, verify, and summarize live web data. Voice Synthesis: Delivers an instant audio summary via ElevenLabs. Automated Export: Generates a professional PDF report automatically. Tech Stack: CrewAI, Groq, ElevenLabs, Streamlit, DuckDuckGo API.

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Cover image for I built a professional, end-to-end

I built a professional, end-to-end AI Receptionist system designed to automate clinic appointment management. This isn't just a chatbot; it's an AI Agent that can reason, use tools, and manage a live database autonomously. Key Contributions: Agentic Reasoning: Integrated CrewAI with Llama 3.3 (Groq) to enable the agent to understand complex user intents (Booking vs. Cancellation) and relative time (e.g., "next Tuesday at 3pm"). Autonomous Tool Use: Developed custom Python tools that allow the agent to verify real-time availability in a SQLite database and execute atomic transactions without human intervention. High-Performance Backend: Built a robust API using FastAPI to handle asynchronous requests between the AI agent and the database. Premium Dashboard: Designed a modern, Glassmorphic UI using Tailwind CSS that provides a real-time sync of the clinic’s schedule. The Result: A seamless, hands-free system that reduces administrative overhead by 100%, allowing clinic staff to focus on patients while the AI handles the entire scheduling lifecycle. Tech Stack: Python, CrewAI, Groq API, FastAPI, SQLite, Tailwind CSS

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Cover image for Developed a highly responsive AI

Developed a highly responsive AI Voice Agent using Vapi that handles real-time conversations with exceptional clarity. The agent is designed to engage users naturally, gather specific information during the call, and accurately extract that data for further use. The voice quality for both the user and the bot is seamless, making the interaction feel professional and human-like

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Bengaluru, India

AI Data Analyst | Power BI & Python | Automated Reports

AI Data Analyst | Power BI & Python | Automated Reports

Cover image for ProData AI — Automated Data

ProData AI — Automated Data Science Platform Built ProData AI, an automated data science platform developed entirely with Streamlit. It is designed to help users transform raw datasets into actionable business insights in seconds. 🚀 What it does One-Click Mode Upload any CSV or Excel file and the full pipeline runs automatically in under 30 seconds: Data cleaning & preprocessing AutoML (6 models trained simultaneously) 30-day forecasting using Prophet Business driver analysis with Explainable AI (XAI) AI-generated insights using Anthropic Claude PDF report generation Manual Mode Provides full control over each stage of the data science workflow for advanced users. 🛠 Tech Stack Streamlit — complete UI and app framework scikit-learn — AutoML pipeline Prophet — time-series forecasting Anthropic Claude API — AI insights & chat Plotly — interactive visualizations fpdf2 — PDF report generation Ideal for: business analysts startups small businesses automated reporting workflows freelance analytics projects Open to freelance collaborations and custom dashboard / AI reporting solutions.

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Cover image for AI Data Scientist

AI Data Scientist

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Cover image for I built an AI-powered Data Analyst that turns raw CSV, Excel...

I built an AI-powered Data Analyst that turns raw CSV, Excel, or SQL data into executive-ready insights. ✅ Automatic EDA & outlier detection ✅ Visual charts & insights ✅ Executive PDF report ✅ Business recommendations ✅ Voice-based summaries This isn’t a dashboard — it’s an AI analyst for founders and teams who want fast decisions. 🎥 Watch the demo video 📩 Message me “AI Analyst” for access or a custom build #AI #DataAnalytics #Automation #BusinessIntelligence #Streamlit

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Cover image for Built something I'm pretty proud

Built something I'm pretty proud of — Data Detective An AI-powered anomaly investigation tool that turns dry data analysis into a noir detective case. Under the hood: → Claude + 14 custom MCP tools → Isolation Forest for anomaly detection → Pearson correlation analysis → Results presented as an actual investigation report No spreadsheets. No jargon. Just: "The suspect is outlier #47. Here's the evidence." Live here → [https://claude.ai/public/artifacts/f8117dff-b531-4f31-8862-2a86a77137c2] Full MCP server → https://github.com/Varu4/prodata-ai-mcp #contralabs #AI #dataanalysis

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Python & Django developer for web apps and APIs

New to Contra

Python & Django developer for web apps and APIs

Cover image for Built a Python-powered automated trading

Built a Python-powered automated trading tool that pulls live market data, runs technical analysis, and generates real-time trade signals. Features normalized comparison charts across multiple assets (Nifty, USD Index, Brent Crude, US 10Y Yield), a macro pressure table, correlation analysis, and a portfolio helper. Backend built in Python with automated data pipelines, live market feeds, and signal interpretation logic that flags bullish/bearish pressure. Clean, data-dense dashboard for fast decision-making. Tech: Python, Pandas, REST APIs, data automation, real-time charting.

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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?

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Cover image for An AI-driven trading engine that

An AI-driven trading engine that analyzes 200-DMA breakouts and real-time market sentiment to generate long/short recommendations. It scans 1,500+ NSE stocks in under 2 seconds and uses generative AI to build option strategies, delivering event-driven trade signals end-to-end. I built the full Django backend, async APIs, and the signal-generation logic. Accomplishments and responsibilities: Built an AI-driven trading engine analyzing 200-DMA breakouts and market sentiment, generating long/short signals with ~70% directional accuracy — outperforming baseline strategies by 35%; Integrated generative-AI insights for automated option-strategy creation (spreads, straddles, condors), improving Sharpe ratio by 1.6× and cutting manual analysis time by 60%; Developed a Django backend with async APIs scanning 1,500+ NSE stocks in under 2 seconds, achieving 40% lower latency with event-driven alerts.

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Cover image for An AI-powered news platform that

An AI-powered news platform that delivers concise, real-time AI-industry updates to 2,500+ active users. It scrapes and aggregates 500+ sources daily, removes duplicates, and uses generative-AI summarization to cut reading time significantly while surfacing the most relevant stories. I built the backend responsible for scraping, deduplication, and the LLM summarization pipeline.

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