README.md

metadata

title: Iris Flower Prediction With MachineLearning
emoji: 🐨
colorFrom: purple
colorTo: blue
sdk: docker
pinned: false
license: apache-2.0

Iris Flower Detection Web Application

This is a simple Flask web application that uses a machine learning model to predict the species of iris flowers based on measurements.

Files and Structure

• app.py - The main Flask application
• iris_model.pkl / new_iris_model.pkl - The trained machine learning model
• templates/ - Folder containing HTML templates
  • form.html - Input form for flower measurements
  • result.html - Page showing prediction results
• create_new_model.py - Script to create a fresh model if needed
• test_app.py - Script to test the application functionality
• run_app.bat - Windows batch file to easily run the application

How to Run

1. Double-click on run_app.bat or run python app.py in your terminal
2. Open your web browser and go to http://127.0.0.1:5000
3. Enter the flower measurements and click "Predict Flower Species"

Sample Measurements

Iris Setosa

• Sepal Length: 5.1 cm
• Sepal Width: 3.5 cm
• Petal Length: 1.4 cm
• Petal Width: 0.2 cm

Iris Versicolor

• Sepal Length: 6.0 cm
• Sepal Width: 2.7 cm
• Petal Length: 4.2 cm
• Petal Width: 1.3 cm

Iris Virginica

• Sepal Length: 6.8 cm
• Sepal Width: 3.0 cm
• Petal Length: 5.5 cm
• Petal Width: 2.1 cm

Troubleshooting

If you encounter issues:

1. Run python test_app.py to verify the model is working correctly
2. Check that you have all the required Python packages installed:
   • Flask
   • scikit-learn
   • joblib
   • numpy
3. Try generating a new model with python create_new_model.py