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---
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`
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