Update src/streamlit_app.py

src/streamlit_app.py

@@ -7,19 +7,23 @@ from sklearn.metrics import mean_squared_error
 
 st.set_page_config(page_title="Linear Regression Playground", layout="centered")
 
-# FIX: make equation always fully visible + box formatting
+# === FIX: fully visible equation with dark box ===
 st.markdown("""
 <style>
 .eq-box {
-    border: 2px solid #333;
+    border: 2px solid #444;
     border-radius: 8px;
-    background: #ffffff;
+    background: #222;         /* DARK background */
     padding: 14px;
     width: 100%;
     font-size: 22px;
+    color: white !important;  /* WHITE text */
     text-align: center;
     margin-top: 14px;
 }
+.mathjax-chtml, .MathJax {
+    color: white !important;  /* Force formula text white */
+}
 </style>
 """, unsafe_allow_html=True)
 
@@ -49,7 +53,6 @@ if mode != st.session_state.current_mode:
     st.session_state.trained = False
     st.session_state.current_mode = mode
 
-
 # ------------------------------------
 # Generate dataset
 # ------------------------------------
@@ -87,7 +90,6 @@ if train_btn:
 
         st.session_state.trained = True
 
-
 # ------------------------------------
 # Visualization
 # ------------------------------------
@@ -112,11 +114,9 @@ if st.session_state.trained:
         with col2:
             st.metric("MSE", f"{mse:.4f}")
 
-            # FIXED FULL EQUATION BOX
             equation = rf"y = {model.coef_[0]:.3f}x + {model.intercept_:.3f}"
             st.markdown(f"<div class='eq-box'>${equation}$</div>", unsafe_allow_html=True)
 
-
     # ----------------- 3D Regression -----------------
     else:
         X1, X2, Z, Z_pred, mse, model = st.session_state.data
@@ -124,24 +124,19 @@ if st.session_state.trained:
         col1, col2 = st.columns([2, 1])
 
         with col1:
-
             if not rotate_3d:
                 fig = plt.figure(figsize=(4.5, 4))
                 ax = fig.add_subplot(111, projection="3d")
 
                 idx = np.random.choice(len(Z.ravel()), min(350, len(Z.ravel())), replace=False)
-                ax.scatter(
-                    X1.ravel()[idx], X2.ravel()[idx], Z.ravel()[idx],
-                    color="orange", alpha=0.25, s=8
-                )
+                ax.scatter(X1.ravel()[idx], X2.ravel()[idx], Z.ravel()[idx],
+                           color="orange", alpha=0.25, s=8)
 
                 ax.plot_surface(X1, X2, Z_pred, alpha=0.75, color="blue")
                 ax.set_title("3D Linear Regression")
                 st.pyplot(fig, clear_figure=True)
-
             else:
                 placeholder = st.empty()
-
                 for angle in range(0, 360, 5):
                     fig = plt.figure(figsize=(4.5, 4))
                     ax = fig.add_subplot(111, projection="3d")
@@ -165,8 +160,6 @@ if st.session_state.trained:
             c = model.intercept_
 
             equation3d = rf"z = {a:.3f}x_1 + {b:.3f}x_2 + {c:.3f}"
-
-            # FIXED FULL EQUATION BOX
             st.markdown(f"<div class='eq-box'>${equation3d}$</div>", unsafe_allow_html=True)
 
 else: