How abubakkaraslam/streamlit works
This repository is a collection of Streamlit learning apps, including interactive-widget examples, a dataset exploration app, and a larger machine-learning analysis app. The principal analytical workflow selects or uploads data, inspects it, trains and compares models, then displays evaluation results and supports predictions. The ML source excerpt is partial; the map includes only capabilities visible in the supplied excerpts and does not infer connections for the empty 06_langchain directory.
Main entry points: ml_model_app.py ML analysis app · 02_streamlit/app.py Dataset explorer · 01_streamlit/app.py Widget demo · 03_streamalit_all_commands/app.py Widget gallery
Components 17
Streamlit demos
- Widget demo
01_streamlit/app.py - Widget gallery
03_streamalit_all_commands/app.py
Data exploration
- Dataset explorer
02_streamlit/app.py - Summary and plots
02_streamlit/app.py
Machine learning
- ML analysis app
04_streamlit_ml_project/ml_model_app.py - Data validation
04_streamlit_ml_project/ml_model_app.py - Preprocessing
04_streamlit_ml_project/ml_model_app.py - Model training
04_streamlit_ml_project/ml_model_app.py - Model evaluation
04_streamlit_ml_project/ml_model_app.py - Comparison results
04_streamlit_ml_project/ml_model_app.py - Prediction input
04_streamlit_ml_project/ml_model_app.py - Prediction
04_streamlit_ml_project/ml_model_app.py
Other components
- User
- Streamlit UI
- Sample datasets
- Uploaded dataset
- Input dataset
Connections 23
- User → Widget demo: uses
- Widget demo → Streamlit UI: renders controls
- User → Widget gallery: uses
- Widget gallery → Streamlit UI: renders controls
- User → Dataset explorer: selects data
- Dataset explorer → Sample datasets: loads dataset
- User → Uploaded dataset: uploads
- Dataset explorer → Uploaded dataset: reads upload
- Dataset explorer → Summary and plots: computes and renders
- Dataset explorer → Streamlit UI: renders interface
- User → ML analysis app: configures analysis
- User → Input dataset: provides dataset
- ML analysis app → Data validation: validates data
- ML analysis app → Preprocessing: builds pipelines
- ML analysis app → Model training: trains models
- Model training → Preprocessing: uses transforms
- ML analysis app → Model evaluation: scores models
- ML analysis app → Comparison results: displays results
- User → Prediction input: enters features
- ML analysis app → Prediction input: collects input
- ML analysis app → Prediction: predicts with best model
- Prediction input → Prediction: supplies features
- ML analysis app → Streamlit UI: renders interface