Update app.py

app.py

@@ -1,4 +1,3 @@
-
 import streamlit as st
 import pandas as pd
 import numpy as np
@@ -24,7 +23,7 @@ def fit_model(data, model_type, x_values, y_values):
         x_values = x_values.reshape(-1, 1)
         model.fit(x_values, y_values)
         prediction = model.predict(x_values)
-        equation = f'y = {{model.coef_[0]:.4f}}x + {{model.intercept_:.4f}}'
+        equation = f'y = {model.coef_[0]:.4f}x + {model.intercept_:.4f}'
     elif model_type == 'Polynomial Regression':
         polynomial_features = PolynomialFeatures(degree=2)
         x_values_poly = polynomial_features.fit_transform(x_values.reshape(-1, 1))
@@ -48,7 +47,7 @@ def app():
         
         # Selecting R, G, B, H, S, V
         color_component = st.selectbox("Select color component", ['R', 'G', 'B', 'H', 'S', 'V'])
-        st.write(f"Selected component: {{color_component}}")
+        st.write(f"Selected component: {color_component}")
         selected_data = data[color_component].values
         
         # Selecting regression model
@@ -59,10 +58,10 @@ def app():
 
         # Fitting the selected model
         model, prediction, equation = fit_model(data, regression_model, x_values, y_values)
-        st.write(f"Equation: {{equation}}")
+        st.write(f"Equation: {equation}")
         
         # Plotting the data and model
         plot_data(data, x_values, y_values, model, prediction)
 
-# Running the app (uncomment this line to run the app locally)
+# Uncomment the next line to run the app locally
 # app()