https://github.com/matheusafonseca/streamlit-studies
A repository dedicated to storing code specifically for the Streamlit framework.
https://github.com/matheusafonseca/streamlit-studies
css css-styles docker docker-compose dockerfile opencv opencv-haar-cascade opencv-python python python3 streamlit streamlit-framework
Last synced: 8 months ago
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A repository dedicated to storing code specifically for the Streamlit framework.
- Host: GitHub
- URL: https://github.com/matheusafonseca/streamlit-studies
- Owner: matheusAFONSECA
- License: mit
- Created: 2024-12-14T20:16:51.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-21T23:04:14.000Z (about 1 year ago)
- Last Synced: 2025-04-03T18:16:14.708Z (11 months ago)
- Topics: css, css-styles, docker, docker-compose, dockerfile, opencv, opencv-haar-cascade, opencv-python, python, python3, streamlit, streamlit-framework
- Language: Python
- Homepage:
- Size: 33.2 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# streamlit-studies
A repository dedicated to storing code specifically for the ``Streamlit`` framework.
## Index
- [Projects](#projects)
- [Detection of Faces and Video Recording](#detection-of-faces-and-video-recording)
- [How to run](#how-to-run)
- [Authors](#authors)
## Projects
This repository contains various mini-projects designed to explore and showcase the different components and capabilities of the Streamlit framework. These projects also demonstrate techniques for integrating custom components using ``CSS`` to create visually appealing layouts.
### Detection of Faces and Video Recording
This project focuses on integrating a real-time camera feed with a Streamlit interface to detect faces and record videos. It also explores the use of ``CSS`` for designing components and layouts within the project. The video is displayed in real-time, and face detection is performed using the ``Haar Cascade classifier`` from ``OpenCV``. Additionally, the application uses sessions to manage the application's state and metrics to dynamically display its status.
The documentation for this project can be found [here](src/WebCamRecordFaceDetection/explanation.md).
## How to Run
### Running localy
1. Clone the repository:
```bash
git clone https://github.com/matheusAFONSECA/streamlit-studies.git
cd streamlit-studies
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
3. Run the projects:
```bash
streamlit run src/main.py
```
4. Open the link provided in the terminal (e.g., `http://localhost:8501`) to access the interface.
### Running via docker
1. Clone the repository:
```bash
git clone https://github.com/matheusAFONSECA/streamlit-studies.git
cd streamlit-studies
```
2. run this command:
```bash
docker compose --build
```
3. Open the link provided in the terminal (e.g., `http://localhost:8501`) to access the interface.
## Authors
### [Matheus Fonseca](https://github.com/matheusAFONSECA)
Undergraduate student in the eighth (8th) semester of Computer Engineering at the National Institute of Telecommunications (Inatel). I participated in a Scientific Initiation at the Cybersecurity and Internet of Things Laboratory (CS&ILAB), where, in the Park Here project, I developed skills in computer vision applied to parking systems, focusing on license plate recognition and vehicle identification. Additionally, I served as a teaching assistant for Physics 1, 2, and 3, helping with practical classes, report writing, and answering theoretical questions. Currently, I am an intern at the Inatel Competence Center (ICC) in the PDI SW department.