app.py

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# Load the Iris dataset
iris = load_iris()

# Split the dataset into features (X) and target (y)
X = iris.data
y = iris.target

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create a Logistic Regression model
model = LogisticRegression(max_iter=200)

# Fit the model to the training data
model.fit(X_train, y_train)

# Make predictions on the test data
y_pred = model.predict(X_test)

# Calculate the accuracy of the model
accuracy = accuracy_score(y_test, y_pred)

print(f'Model Accuracy on Test Data: {accuracy*100:.2f}%')

# Define a function to make predictions based on user input
def predict_iris(sepal_length, sepal_width, petal_length, petal_width):
  # Convert input to a numpy array
  input_data = np.array([sepal_length, sepal_width, petal_length, petal_width])

  # Reshape the input to match the model's input shape
  input_data = input_data.reshape(1, -1)

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

  # Get the name of the predicted class
  prediction_name = iris.target_names[prediction[0]]

  return prediction_name

# User input loop for continuous predictions
while True:
  try:
    # Get user input for flower features
    sepal_length = float(input("Enter Sepal Length (cm): "))
    sepal_width = float(input("Enter Sepal Width (cm): "))
    petal_length = float(input("Enter Petal Length (cm): "))
    petal_width = float(input("Enter Petal Width (cm): "))

    # Make prediction and display results
    prediction = predict_iris(sepal_length, sepal_width, petal_length, petal_width)
    print(f"\nPredicted Iris Class: {prediction}")

  except ValueError:
    print("\nInvalid input. Please enter numerical values for flower features.")

  # Ask user if they want to continue
  choice = input("\nDo you want to make another prediction? (y/n): ")
  if choice.lower() != 'y':
    break

print("\nExiting the program.")