Freelancers using Streamlit in IndiaFreelancers using Streamlit in India
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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)
Bulbul Gupta 's avatar
Bulbul Gupta
Indore, India
AI Chatbot Dev | Automate Support & IG DMs
33
Followers
AI Chatbot Dev | Automate Support & IG DMs
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 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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Cover image for AI Chatbot for Customer Support
AI Chatbot for Customer Support Automation This AI chatbot helps businesses automate customer queries and reduce response time by up to 60%. 🔹 Problem: Businesses often struggle with delayed responses, repetitive queries, and high support workload. 🔹 Solution: Developed an AI chatbot using NLP that can understand user intent and provide instant, accurate replies. 🔹 Key Features: • Handles common customer queries automatically • Basic intent recognition using NLP • Fast and real-time responses • Easy integration with web/mobile apps 🔹 Result: • Reduced response time • Improved customer experience • Lower support workload
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Streamlit(2)
Trashu Vashisth's avatar
Trashu Vashisth
Delhi, India
Building Production-Grade AI Agents & RAG Systems
12
Followers
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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Streamlit(2)
Aarsh Vyas's avatar
Aarsh Vyas
proAhmedabad, India
Data Consultant (Data Analytics, Data Science, AI & ML)
New to Contra
Data Consultant (Data Analytics, Data Science, AI & ML)
Cover image for Problem: Olsen Financial Technologies manages vast
Problem: Olsen Financial Technologies manages vast volumes of high-frequency forex data, but clients lacked an intuitive platform to quickly interpret real-time and historical trends, making analysis time-consuming and limiting actionable insights. Solution: Developed a Streamlit-based Forex Currency App that transforms complex datasets into interactive dashboards with real-time updates, dynamic charts, and customizable time filters, enabling faster, data-driven decision-making and improved market trend visibility.
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Cover image for Problem Apex Legal Solutions lacked a
Problem Apex Legal Solutions lacked a modern online presence that reflected its credibility and made it easy for clients to connect. Poor UX and no SEO limited engagement and visibility. Solution I designed and developed a responsive, user-friendly website using HTML, CSS, JavaScript, and TypeScript. Improved UI/UX builds trust, while contact form integration streamlines inquiries. SEO optimization boosts visibility and attracts relevant clients, driving consistent growth.
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Cover image for Problem: Sõl needed an elegant, high-performing
Problem: Sõl needed an elegant, high-performing eCommerce experience that matched its premium brand identity while supporting seamless shopping, digital product delivery, and scalable content management, which was missing in their existing setup. Solution: Developed a custom Shopify experience with refined UI, responsive layouts, and dynamic product logic, including automated post-purchase downloads. Enhanced site performance with optimized assets, analytics integration, A/B testing, and structured data for improved usability and conversions.
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Cover image for Problem: The health insurance provider struggled
Problem: The health insurance provider struggled with fragmented data across multiple platforms, making it difficult to track campaign performance, user behavior, and ROI in real time, leading to delayed insights and inefficient decision-making. Solution: Built centralized Looker Studio dashboards integrating multiple data sources via automated ETL, delivering real-time insights through intuitive visuals. Enabled teams to monitor performance, optimize campaigns, and make faster, data-driven decisions with complete visibility.
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Streamlit(1)
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 Everyone's talking about quantum computing.
Everyone's talking about quantum computing. Nobody's using it to feed farmers. India loses 20–30% of its crop yield every year to diseases and pests. Not because farmers don't care — but because early detection is hard, expensive, and inaccessible to the people who need it most. The existing solutions? Either a basic image classifier trained on lab-perfect photos that fail in real field conditions, or an agronomist visit that costs time and money most small farmers don't have. So I built QuantumEdge AgriGuard — a hybrid Quantum Neural Network app where a farmer can photograph a diseased leaf on their phone and get an instant diagnosis in under 5 seconds. Here's what makes it different from just another plant disease detector: Instead of a pure classical CNN, I built a hybrid architecture — a ResNet/EfficientNet backbone extracts visual features, then passes them into a Variational Quantum Circuit (VQC) for the final classification. The quantum layer uses angle embedding + StronglyEntanglingLayers, which gives it a measurable edge on small, noisy datasets — exactly the kind of data you get from Indian field conditions. The app doesn't just tell you what disease it is. It gives you: → Confidence score → Organic + chemical remedies (India-specific) → Yield impact estimate → A live classical vs quantum accuracy comparison so you can see the difference yourself I tested the quantum advantage claim honestly — ran both models on the same downsampled PlantVillage dataset and tracked accuracy, F1-score, and inference time side by side. The results are on the dashboard. No hand-waving. Built with PennyLane + PyTorch + Plotly Dash. Designed to run on simulators today and on QpiAI-Indus 25-qubit hardware tomorrow.
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Cover image for Most AI research tools are
Most AI research tools are just a chatbot with a search button. I built something different. Every time you ask an AI to research something, you're getting one model, one pass, no quality check. It writes confidently, cites poorly, and you have no idea if what it produced is actually accurate. For anyone making real decisions from AI-generated research, that's a silent risk most people ignore. The problem gets worse at scale the longer and more complex the question, the more a single model hallucinates, misses sources, and loses structure. There's no one checking its work. So I built ResearchOS a 5-agent pipeline where each agent has one job. A Supervisor breaks down your question. A Researcher runs parallel searches across 22+ sources. An Analyst extracts data and auto-generates charts. A Writer synthesises a cited report. A Critic fact-checks it and sends it back for revision if anything is wrong. The loop runs up to 3 times before the report is approved. One question in. A full cited report with charts and PDF export in under 10 minutes. I tested it live by watching the Critic catch a missing citation mid-run and send the Writer back to fix it before approval. That's the part that makes this actually usable for real work. Built on LangGraph, Groq, Tavily, ChromaDB and runs entirely on free tiers.
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Streamlit(2)
Karan Singh's avatar
Karan Singh
Kangra, India
AI/ML Engineer crafting intelligent systems & AI solutions.
10
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 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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Cover image for I recently fine-tuned the Mistral 7B Instruct model on a dat...
I recently fine-tuned the Mistral 7B Instruct model on a dataset of NDA (Non-Disclosure Agreement) documents — building an AI reviewer capable of identifying compliance issues and clause inconsistencies. To make the model more accessible, I converted the trained weights to CPU-compatible files, allowing efficient inference without GPU requirements. Model: Mistral 7B Instruct v0.1 Focus: Legal text review & semantic understanding Tech: PyTorch, Transformers, Kaggle Check out the full notebook here → [Kaggle Project Link (https://www.kaggle.com/code/karansingh123456/nda-reviewer-model-training)] My Kaggle account here → Profile (https://www.kaggle.com/curiouscyborgs) #AI #NLP #SentimentAnalysis #DeepLearning #CNN #LSTM #DataScience #GitHub
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Streamlit(1)
Varun Walekar's avatar
Varun Walekar
Bengaluru, India
AI Data Analyst | Power BI & Python | Automated Reports
New to Contra
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 From Chaos to Clarity: Healthcare
From Chaos to Clarity: Healthcare Dashboard in Power BI 1. Clean and interactive Power BI dashboard for tracking patient trends, hospital performance, and key healthcare metrics in one place. 2. A modern healthcare analytics dashboard built in Power BI to monitor admissions, diseases, and operational efficiency. 3. Turn raw healthcare data into meaningful insights with this intuitive and fully interactive Power BI dashboard.
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Streamlit(3)