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Iris Flower Prediction App

This project demonstrates full-stack development by integrating both front-end and back-end technologies, along with a machine learning model, to create a dynamic, interactive web application.

  • Front-End: The user interface is built using HTML and JavaScript. Users input Iris flower measurements (sepal length, sepal width, petal length, petal width) through a form, and the interface dynamically updates with the predicted flower species. Interactive data visualization is provided using Plotly.js, which displays a scatter plot matrix of the Iris dataset, and highlights the user's input.

  • Back-End: The back-end is built using Flask (Python), which handles the requests from the front end, serves the machine learning model (trained using scikit-learn), and provides API endpoints (/predict and /get_data) to make predictions and serve dataset visualizations.

  • Machine Learning: A Random Forest Classifier is trained on the famous Iris dataset using scikit-learn. The model is saved as a .pkl file and is loaded by the Flask app to provide real-time predictions based on user input.

  • Full-Stack Integration: The front end and back end communicate through asynchronous HTTP requests (using fetch), enabling real-time interaction between the user and the server. User input is processed by the back-end model, and the result is sent back to the front end for visualization.

This project is an example of how to combine client-side interaction, server-side logic, and data science to create a seamless full-stack web application.

Demo

Iris Flower Prediction App Demo

Features

  • Predict the Iris flower species using a pre-trained machine learning model.
  • Visualize the dataset in a 4x4 scatter plot matrix.
  • Highlight user input on the scatter plot matrix with the prediction.

Prerequisites

  • Python 3.x
  • Flask
  • scikit-learn
  • Plotly

Installation

  1. Clone the repository:

    git clone https://github.com/samanseifi/iriswebapp.git
cd iriswebapp

  2. Create a virtual environment (optional but recommended):

    python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

  3. Install the dependencies:

    pip install -r requirements.txt

Setup

  1. Train and save the model (if not already saved):

    Run the following script to train the model and save it as model.pkl:

    from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
import pickle

# Load the dataset
data = load_iris()
X = data.data
y = data.target

# Train the model
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = RandomForestClassifier()
model.fit(X_train, y_train)

# Save the model
with open('model.pkl', 'wb') as file:
    pickle.dump(model, file)

print("Model saved as model.pkl")

  2. Run the Flask app:

    python3 app_iris.py

  3. Access the application:

    Open your browser and go to http://127.0.0.1:5000 to use the app.

Usage

  • Enter the values for sepal length, sepal width, petal length, and petal width.
  • Click "Predict" to get the predicted Iris flower species.
  • The user input will be highlighted in red across all scatter plots in the scatter plot matrix.

License

This project is licensed under the MIT License - see the LICENSE file for details.