P

Python (Flask)

an interoperable orchestrator for Self-Driving Labs (SDLs)

Code base for the AndBiodiversity BIOHME application and database

Blur detection using OpenCV and Flask checks image sharpness using Laplacian variance. Users upload images via web app, and the system classifies them as blurry or clear instantly.

AI-powered face detection and recognition identifies faces in images or video using deep learning. It detects, extracts features, and matches them with stored data for secure and accurate identification.

Developed an automated NLP pipeline using Python to clean and standardize unstructured healthcare data through error detection, imputation, and terminology alignment. Improved data accuracy and reduced manual processing time, enhancing data readiness for AI-driven analysis and clinical decision-making.

job search decision support system

A tiny FastAPI webhook service that listens for GitLab Merge Request events, computes a basic risk score from diffs, and posts/updates a single MR comment.

Un service web en Flask/Python pour analyser les enregistrements MX d’un domaine et la bannière SMTP afin d'identifier le serveur SMTP utilisé et de détecter l'hébergeur mail associé.

GroundBi Master Application is an AI-powered terrain generation application that creates detailed terrain visuals for Original War. The application uses stable diffusion models with LoRA adaptations to generate high-quality terrain images based on reference images.

Open ResearchNotes is an electronic lab book (ELN) to manage data obtained during scientific research.

Trading Bot – Algorithmic Crypto Trading with AI Integration

This project is a powerful algorithmic trading bot for cryptocurrency markets. It combines traditional technical analysis with modern machine learning to generate accurate and intelligent trading decisions.

Key Features:

Candlestick Pattern Detection: Identifies classic reversal patterns such as Hammer, Doji, Engulfing, Shooting Star, and complex formations like triangle patterns.

Technical Indicators: Includes standard indicators (RSI, MACD, Moving Averages, Bollinger Bands) and advanced tools like Ichimoku Clouds, SuperTrend, Fibonacci Retracements, and more.

Machine Learning Integration: Uses LSTM-based models for time-series forecasting and momentum strategies, combined with indicator signals through weighted evaluation.

Dynamic Signal Weighting: Customizable signal weighting for patterns, indicators, and ML predictions with automatic adjustments to market volatility.

Trade Execution Engine: Supports long/short positions with stop-loss, take-profit, and trailing stop features. Automatically includes fees and tax deductions in profit calculations.

Backtesting & Debugging: Simulates strategies on historical data with detailed equity/value curve visualization and comprehensive debug logs.

Robust Error Handling: Detects and logs data inconsistencies, index errors, and processing issues to ensure stability.

Modular architecture with key components such as TraderBot, SignalHandler, PatternManager, IndicatorManager, MLModelHandler, SequenceManager, DataAPI, and CryptoCurrency. Additional support provided by PatternCalculator, IndicatorCalculator, and DataProcessing.

Version: V1.3.0.0 | GUI: V1.0.0 Author: Marian Seeger – info@seegersoftwaredevelopment.de

AWS graduation project: Auto-scaling Flask application with Terraform IaC and GitLab CI/CD pipeline

Web interface for Enviro and Enviro+ sensor board plugged into a Raspberry Pi.

Measure air quality (pollutant gases and particulates), temperature, humidity, pressure, light and noise.

This VERY SIMPLE webhook server allows to configure commands to be executed when receiving requests with the right token.

Taken from Real Python

Teenage Mutant Ninja Turtles: Flask Web App. Cowabunga!

A Flask API for a DEPT challenge for a back end position