app.py

import streamlit as st
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

def train_iris_model():
    df = pd.read_csv("Iris.csv")
    df['Species'] = df['Species'].map({'Iris-setosa': 0, 'Iris-virginica': 1, 'Iris-versicolor': 2})
    del df["Id"]
    X = df.loc[:, df.columns != 'Species']
    y = df['Species']
    X_train, _, y_train, _ = train_test_split(X, y, test_size=0.3, random_state=42)

    model = KNeighborsClassifier()
    model.fit(X_train, y_train)

    return model

def predict_iris_species(model, input_data):
    # Reshape the input data to (1, -1) to make it compatible with model.predict
    input_data = np.array(input_data).reshape(1, -1)

    # Make predictions using the trained model
    prediction = model.predict(input_data)

    return prediction

def main():
    st.title("Iris Species Prediction App")

    sepal_length = st.slider("Sepal Length", 0.0, 10.0, 5.0)
    sepal_width = st.slider("Sepal Width", 0.0, 10.0, 5.0)
    petal_length = st.slider("Petal Length", 0.0, 10.0, 5.0)
    petal_width = st.slider("Petal Width", 0.0, 10.0, 5.0)

    trained_model = train_iris_model()
    input_values = [sepal_length, sepal_width, petal_length, petal_width]
    prediction_result = predict_iris_species(trained_model, input_values)

    st.write(f"Predicted Species: {prediction_result[0]}")

if __name__ == "__main__":
    main()