app.py

from flask import Flask, render_template, request
import pandas as pd
from sklearn.linear_model import LogisticRegression


app = Flask(__name__)

# Load dataset from URL
iris = pd.read_csv("iris.csv")


# Prepare input and output
X = iris[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']]
y = iris['species']


# Logistic Regression
model = LogisticRegression()
model.fit(X, y)  # Train once

@app.route('/')
def home():
    return render_template('index.html')

@app.route('/predict', methods=['POST'])
def predict():
  sl = float(request.form['sepal_length'])
  sw = float(request.form['sepal_width'])
  pl = float(request.form['petal_length'])
  pw = float(request.form['petal_width'])

  # Predict species
  input_data = [[sl, sw, pl, pw]]
  pred = model.predict(input_data)

  return render_template('index.html', prediction=pred[0])

if __name__ == '__main__':
    app.run(debug=True)