You will get Advanced AI Virtual Painter
Top Rated
Project details
I built this tool for you from the ground up using MediaPipe, OpenCV, and Google Speech API to deliver a genuinely hands-free creative experience through your webcam.
Draw with finger gestures, autocomplete shapes with AI, and control everything with simple voice commands instantly.
For educators it is a compelling classroom demo. For developers it is a production-ready computer vision foundation to build on. For creatives it removes every barrier between idea and execution.
Draw with finger gestures, autocomplete shapes with AI, and control everything with simple voice commands instantly.
For educators it is a compelling classroom demo. For developers it is a production-ready computer vision foundation to build on. For creatives it removes every barrier between idea and execution.
AI Development Type
Deep Learning, Model TuningAI Tools
PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$150
|
Standard
$300
|
Advanced
$500
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Detailed Code Comments | |||
Knowledge Graph | - | ||
Model Documentation | |||
Ontology | - | - | |
Source Code | |||
Taxonomy | - | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$100 - $200
15 reviews
(12)
(2)
(0)
(1)
(0)
This project doesn't have any reviews.
OT
Oleksandr T.
Aug 19, 2026
Excel Certificate Template Design
HM
Hassan M.
Jun 17, 2026
Consultation for RAG and AI
RG
Rudik G.
May 29, 2026
Building an LSTM model in Python for Armenian text to classify brand names and ADGCode accurately.
HT
Hans T.
Apr 20, 2026
Style class creation.
HB
Hieu B.
Mar 3, 2026
Website management, development, maintenance
About Faizan
AI Engineer | LLM, RAG & LangChain | Healthcare Full-Stack Developer
100%
Job Success
Rawalpindi, Pakistan - 6:49 am local time
I build retrieval augmented generation systems for searching clinical documents in plain language, machine learning prediction models, clinical NLP pipelines, and analytics dashboards. My core stack is GPT-4, Claude, LangChain, and LangGraph for the AI layer, and Python, FastAPI, React, Node.js, and PostgreSQL for the application layer. I handle the full project myself, from data pipeline through deployment, including web application, API, and payment integration with Stripe when needed.
Recent Work:
HealthDataVitals: A live healthcare analytics platform I built and launched that tracks cost, quality, and performance data across providers, with the dashboard, data pipeline, and payment integration handled end to end.
Agentic Medical Document RAG System: Lets clinical teams upload medical documents and ask questions in plain language, built on LangChain with Pinecone and ChromaDB for vector search. It automatically detects and removes protected health information before storage, so documents stay searchable without exposing patient data.
AssistMedica, AI Clinic Scheduling Agent: A clinic administration dashboard with an agentic scheduling assistant powered by the Claude API, using tool calling for live schedule reads and updates, plus voice input and speech output for hands free appointment management.
Diabetes Prediction System: A machine learning prediction model built with Random Forest, XGBoost, and LightGBM in Python, with SHAP explainability so users can see which factors drove each prediction. Deployed through a web app, a REST API, and a Discord bot.
Multi-Label Disease Prediction from Nutrition Data: A full stack machine learning web app predicting risk across seven conditions, including diabetes, hypertension, and heart disease, from nutrition and lifestyle data. The production Random Forest model reached 99.71 percent accuracy, served through a live web interface with a model transparency dashboard.
Voice Controlled AI Assistant: Takes spoken questions, analyzes uploaded medical images, and replies out loud, useful for hands free clinical workflows like dictating notes. Supports OpenAI, Anthropic, and local open source models.
Tools I Use Most Often:
LangChain and LangGraph for RAG and AI agent development, OpenAI GPT-4, Claude, and Hugging Face for LLM and machine learning models, TensorFlow and PyTorch for deep learning, Pinecone and ChromaDB for vector search and document retrieval, Docker and AWS Lambda for hosting and deployment. For the application layer, React, Node.js, Python, FastAPI, and PostgreSQL for dashboards, APIs, and connecting everything together. For analytics and reporting, Tableau, Streamlit, and Gradio.
Handling Healthcare Data:
Healthcare and clinical data need careful handling. On my RAG project, I built automatic PHI detection and removal before any data entered the system, so documents could be searched without exposing patient details. On every project, I also set up role based access and encrypt data both in storage and in transit, following HIPAA aligned practices.
If you have a healthcare AI, EHR integration, or clinical data project in mind, send me a message with the type of data you are working with and the main question you want your system to answer. I will reply within a day with some initial thoughts.
Steps for completing your project
After purchasing the project, send requirements so Faizan can start the project.
Delivery time starts when Faizan receives requirements from you.
Faizan works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement Review
Review client requirements, operating system, and any custom feature requests before starting development.
Environment Setup
Set up the Python environment, install all dependencies, and verify webcam and microphone compatibility on the target system.