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Python Chatbot Using Cosine Similarity
I built a Python-based chatbot that uses cosine similarity to compare text and identify the closest matching response.
The project explores text similarity, vector-based comparison, and conversational interaction, demonstrating how mathematical techniques can be applied to build a simple chatbot.
Key Features
Text similarity matching: Uses cosine similarity to compare text representations.
Response selection: Identifies the closest matching candidate based on similarity scores.
Python development: Implements the chatbot's matching logic using Python.
Applied mathematics: Uses vector similarity to compare text representations.
Technical Approach
Cosine similarity measures the angle between two vectors to estimate how closely they align. When combined with an appropriate text vectorization method, it can help a chatbot match user messages with relevant stored responses.
Technologies
Python · Natural Language Processing (NLP) · Cosine Similarity
Project Source
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I created this font & it is free to download and use in your free or paid projects. Enjoy 🚀 find it at https://twaperdigital.com/anima-editorial
I like this font
Anima is a self-initiated type design project inspired by vintage print, worn ink and analog photography.
The collection includes two experimental serif typefaces: Anima Editorial, with elegant proportions and contrasting strokes, and Anima Retro, with softer, bolder shapes. Both feature grain built into the letterforms.
I developed the creative direction, custom fonts and promotional artwork, combining AI-assisted exploration with font development. The campaign pairs textured typography with motion-blurred photography to capture a warm, nostalgic mood. FREE DOWNLOAD AT https://twaperdigital.com/anima-retro
Great job Denisa the font style background and it's effects are great 🎉🎉🎉🎉
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CrowdVision AI – Real-Time Crowd Monitoring & Intelligence System
An AI-powered computer vision platform that detects and counts people in real time, monitors crowd density and occupancy, tracks entry and exit movements, and provides actionable crowd intelligence through a live monitoring dashboard.
Overview
CrowdVision AI is an intelligent crowd monitoring and safety analytics platform designed to help organisations monitor public spaces, understand crowd behaviour, and identify potentially unsafe crowd conditions.
The system uses computer vision and machine learning inference to analyse camera feeds and video streams, detect people, estimate occupancy, classify crowd density, and monitor movement across designated zones.
The Problem:
Monitoring crowded environments manually can be difficult, time-consuming, and inefficient. Security personnel may struggle to maintain accurate occupancy counts, monitor multiple zones, and identify unusual crowd conditions quickly.
CrowdVision AI was developed to explore how computer vision and real-time analytics could improve crowd visibility and monitoring.
My Solution:
I developed a computer vision system integrating a pretrained YOLO object-detection model with OpenCV for video processing.
The platform includes:
Real-time people detection and counting.
Crowd occupancy estimation.
Crowd-density classification.
Entry and exit line counting.
Zone-based monitoring.
Incident and anomaly monitoring workflows.
Camera health and tamper monitoring.
Evidence management and reporting.
Telegram alert notifications.
Remote edge camera processing.
Live monitoring dashboard.
Technical Implementation:
Machine Learning & Computer Vision
Python
YOLO / Ultralytics
OpenCV
NumPy
Pandas
Backend:
Python
FastAPI
REST API
Frontend:
React
Vite
JavaScript
Other Technologies
Git
GitHub
Telegram Bot API
JSON/CSV data storage
My Role
Founder & Full-Stack Machine Learning Developer
For entry/exit line counting, I'd test someone pausing on the boundary and stepping back without entering. Do you require a track to move fully between two zones before recording an entry, so small position changes don't become extra visits?