Freelancers using Streamlit in IndiaFreelancers using Streamlit in India
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Abu Aasif Ansari's avatar
Abu Aasif Ansari
Bhiwandi, India
I build AI agents & internal tools that act on data
13
Followers
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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8
Cover image for Smart Data Analyst — AI-Powered Data Analysis App
Smart Data Analyst — AI-Powered Data Analysis App
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9
Cover image for PersonaSkill AI — Career Assessment Tool
PersonaSkill AI — Career Assessment Tool
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16
Streamlit(5)
Mohammad Umar's avatar
Mohammad Umar
India
Freelance Data Scientist | Python & ML Expert
10
Followers
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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Streamlit(3)
Anurag Nagare's avatar
Anurag Nagare
Mumbai, India
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 CryptoHorizon an interactive app that
CryptoHorizon an interactive app that makes the quantum threat to encryption real instead of theoretical: a live Shor's-algorithm demo, a classical-vs-post-quantum crypto benchmark, a personal "harvest now, decrypt later" risk calculator, and a sourced Q-Day timeline, all in one dashboard. Defining feature is honesty by design: every number on screen is computed live at request time, never hardcoded RSA-2048 and ECC P-256 keys get generated, benchmarked, and compared against NIST's ML-KEM and ML-DSA in real time, and the quantum demo runs an actual Qiskit circuit against small, pre-validated semiprimes rather than a lookup table dressed up as a result. Only one thing is allowed to leak into the UI's language: a rule baked in from commit one that nothing may imply real-world RSA is broken or claim quantum advantage. Under the hood, Shor's algorithm runs for real superposition over a counting register, controlled modular exponentiation, inverse QFT, continued-fraction extraction, then a classical gcd step to pull out the factors on a FastAPI backend with no shortcuts. The risk calculator checks your required confidentiality window against five independently sourced Q-Day estimates from NIST, Google, and the Global Risk Institute, so "safe until 2035" is a real, cited comparison, not a guess. medium.com/@anuragnagare77/cryptohorizon-i-built-a-quantum-computer-that-factors-91-and-thats-the-whole-point-2c156a00d0da
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Cover image for One Gate: Voice-controlled Gmail, Calendar,
One Gate: Voice-controlled Gmail, Calendar, and Drive that can't fire the wrong thing Every "AI agent" demo shows a voice command turning into a sent email like magic, but almost nobody shows the part that actually matters: what stops it from sending the wrong thing. I built proof: an agent that runs your Gmail, Calendar, Contacts, and Drive by voice reply to a thread, schedule a meeting, archive an email, find a file hands-free end to end, but never fires anything irreversible without you saying so. The honest hard part isn't getting an LLM to sound smart, it's this: a transcript goes to LLM against a strict JSON schema and comes back as an ordered plan, every step tagged reversible or not and exactly one thing in the whole system is allowed to check that flag. Finding a thread, drafting a reply, creating a calendar event: those just run. The result covers real ground without ever feeling like it's guessing: forward or reply to email with the original quoted underneath, archive/label/trash, resolve a name to a real address through your Contacts first and your mail history as fallback, schedule an event that creates quietly and only emails the invite after a second confirmation, answer "what did John say" or "what's on my calendar Thursday" grounded in content actually fetched from your account not hallucinated. No backend, no server anywhere in the loop
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Streamlit(2)
Bulbul Gupta 's avatar
Bulbul Gupta
Indore, India
AI Automation & Chatbot Developer | Flutter Developer
49
Followers
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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414
Streamlit(2)
Karan Singh's avatar
Karan Singh
Kangra, India
AI/ML Engineer crafting intelligent systems & AI solutions.
11
Followers
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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Streamlit(1)
DHANRAJ SHARMA's avatar
DHANRAJ SHARMA
Mehsana, India
AI Automation Specialist — I build AI workflows that ship
New to Contra
AI Automation Specialist — I build AI workflows that ship
Cover image for Built during my Generative AI
Built during my Generative AI internship at SmartBridge. An AI assistant that combines computer vision with conversational LLMs — upload any vehicle image and get structured, detailed insights through a chatbot interface. Designed an image-to-insight pipeline using Gemini API vision models and prompt engineering, wrapped in an interactive Streamlit application that supports dynamic follow-up queries. Demonstrates multimodal AI, RAG-style retrieval, and real-time LLM interaction in a single product.
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Cover image for I build custom AI-powered contract
I build custom AI-powered contract analysis web applications that help users review contracts, identify risks, and generate clear recommendations in minutes. Using LangChain, RAG, Llama models, Hugging Face embeddings, Flask, and Docker, I create production-ready solutions that can analyze PDFs, DOCX files, and text documents, detect risky clauses, generate plain-English summaries, and provide structured risk scores. Perfect for LegalTech startups, law firms, procurement teams, HR departments, and founders looking to automate contract review workflows. Includes source code, deployment, customization, and post-launch support.
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Cover image for Built SiteScope AI, an AI-powered
Built SiteScope AI, an AI-powered client intake and discovery call automation system using Make.com (http://Make.com), Google Gemini AI, Jina AI Reader, Tally, Google Sheets, Google Calendar, Gmail, and Google Meet. The system automatically processes client submissions, extracts and analyzes website content, generates business insights, creates discovery call strategy briefs, produces personalized recommendations, schedules meetings with automatic calendar invites, sends branded confirmation emails, and logs all data into a structured CRM. The automation eliminates manual pre-call research, scheduling, and follow-ups while enabling faster lead response times and fully prepared discovery calls within minutes of form submission.
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Cover image for Developed ReviewRadar, an AI-powered customer
Developed ReviewRadar, an AI-powered customer review monitoring and business intelligence system built with n8n, Google Gemini AI, SERPAPI, Google Sheets, Gmail, Slack, Twilio WhatsApp, and Looker Studio. The platform continuously collects Google Reviews, classifies sentiment, identifies complaint categories, assigns urgency scores, generates AI-powered response drafts, and instantly notifies stakeholders of critical feedback. Weekly executive summaries and interactive dashboards provide actionable insights into customer sentiment, review trends, top complaints, top praise, and business performance. The automation eliminates manual review monitoring while enabling proactive reputation management and faster customer response times.
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Streamlit(1)