import streamlit as st
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix

# Load the dataset
df = pd.read_csv('iris.csv')

# Prepare data
X = df.drop('species', axis=1)
y = df['species']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# Train the model (only once)
logreg = LogisticRegression()
logreg.fit(X_train, y_train)


# Streamlit app
st.title("Iris Flower Classification")

st.write("Input the features of the iris flower below:")
sepal_length = st.number_input("Sepal Length (cm)", value=5.1)
sepal_width = st.number_input("Sepal Width (cm)", value=3.5)
petal_length = st.number_input("Petal Length (cm)", value=1.4)
petal_width = st.number_input("Petal Width (cm)", value=0.2)


if st.button("Predict"):
    input_data = [[sepal_length, sepal_width, petal_length, petal_width]]
    input_data = scaler.transform(input_data)
    prediction = logreg.predict(input_data)[0]
    st.write(f"Predicted Species: {prediction}")