import streamlit as st
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures

st.set_option('deprecation.showPyplotGlobalUse', False)

st.title('Polynomial Regression Prediction App')

default_X = "0, 1, 2, -1, -2"
default_Y = "1, 6, 33, 0, 9"

X_input = st.text_area('Enter the X values (comma-separated):', value=default_X)
Y_input = st.text_area('Enter the Y values (comma-separated):', value=default_Y)

X = np.array([float(x) for x in X_input.split(',')]).reshape(-1, 1)
Y = np.array([float(y) for y in Y_input.split(',')])

degree = len(X)-1

poly = PolynomialFeatures(degree=degree)
X_poly = poly.fit_transform(X)

regressor = LinearRegression()
regressor.fit(X_poly, Y)

x_values = np.linspace(min(X), max(X), 100).reshape(-1, 1)
x_values_poly = poly.transform(x_values)
y_predicted = regressor.predict(x_values_poly)

st.write("### Polynomial Regression Prediction Plot")
plt.scatter(X, Y, color='red', label='Data')
plt.plot(x_values, y_predicted, color='blue', label='Predicted')
plt.title(f'Polynomial Regression Prediction (Degree {degree})')
plt.xlabel('X')
plt.ylabel('Y')
plt.legend()
st.pyplot()

y_predicted = regressor.predict(X_poly)
data = {'X': X.ravel(), 'Y': Y, 'Y_pred': y_predicted}
df = pd.DataFrame(data)
st.write("### Dataframe with Predicted Values")
st.write(df)

coefficients = regressor.coef_
coeff_data = {'Feature': [f'X^{i}' for i in range(1, degree + 1)], 'Coefficient': coefficients[1:]}
coeff_df = pd.DataFrame(coeff_data)
st.write("### Coefficients of Polynomial Terms")
st.write(coeff_df)



coefficients = [i for i in regressor.coef_]
formatted_coefficients = [format(coeff, ".2e") for coeff in coefficients if coeff != 0]

terms = [f'{coeff}X^{i}' for i, coeff in enumerate(coefficients) if coeff != 0]

formatted_intercept = format(regressor.intercept_, ".2e")

latex_equation = r'''
Our Equation: {} + {}
'''.format(formatted_intercept, ' + '.join(formatted_coefficients))

st.write("### Polynomial Equation")
st.latex(latex_equation)





def calculate_polynomial_value(coefficients, X, intercept):
    result = sum(coeff * (X ** i) for i, coeff in enumerate(coefficients))
    return result + intercept

X_to_calculate = st.number_input('Enter the X value for prediction:')
result = calculate_polynomial_value(coefficients, X_to_calculate, regressor.intercept_)
st.write(f"Predicted Y value at X = {X_to_calculate:.2f} is {result:.2f}")