Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,6 +1,5 @@
 import streamlit as st
 import numpy as np
-import time
 import matplotlib.pyplot as plt
 from sklearn.linear_model import LinearRegression
 from sklearn.metrics import mean_squared_error
@@ -22,7 +21,7 @@ noise_level = st.sidebar.slider("Noise Level", 0.0, 5.0, 1.0)
 # ⭐ SHOW ROTATION OPTION ONLY IN 3D MODE
 rotate_3d = False
 if mode == "3D Regression":
-    rotate_3d = st.sidebar.toggle("🔄 Rotate 3D Model (Smooth Animation)", value=False)
+    rotate_3d = st.sidebar.toggle("🔄 Rotate 3D Model", value=False)
 
 train_btn = st.sidebar.button("Generate & Train Model")
 
@@ -41,8 +40,8 @@ if mode != st.session_state.current_mode:
 # ------------------------------------
 if train_btn:
 
-    with st.spinner("⏳ Training model... Please wait 😄"):
-        time.sleep(1.0)
+    with st.spinner("⏳ Training model..."):
+        st.sleep(0.5)
 
         if mode == "2D Regression":
             X = np.linspace(0, 10, num_points).reshape(-1, 1)
@@ -88,16 +87,15 @@ if st.session_state.trained:
         col1, col2 = st.columns([2, 1])
 
         with col1:
-            fig, ax = plt.subplots(figsize=(6, 4))
-            ax.scatter(X, y, color="orange", label="Data")
+            fig, ax = plt.subplots(figsize=(4.5, 4))
+            ax.scatter(X, y, color="orange", label="Data", s=18)
             ax.plot(X, y_pred, color="blue", linewidth=2, label="Regression Line")
             ax.set_title("2D Linear Regression")
             ax.legend()
-            st.pyplot(fig)
+            st.pyplot(fig, clear_figure=True)
 
         with col2:
             st.metric("MSE", f"{mse:.4f}")
-            st.write("### Equation:")
             st.code(f"y = {model.coef_[0]:.3f}x + {model.intercept_:.3f}")
 
     # ----------------- 3D -----------------
@@ -110,39 +108,39 @@ if st.session_state.trained:
 
             # Static 3D plot
             if not rotate_3d:
-                fig = plt.figure(figsize=(6, 5))
+                fig = plt.figure(figsize=(4.5, 4))
                 ax = fig.add_subplot(111, projection="3d")
 
-                idx = np.random.choice(len(Z.ravel()), min(400, len(Z.ravel())), replace=False)
+                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=0.3, s=6)
+                           color="orange", alpha=0.25, s=8)
 
-                ax.plot_surface(X1, X2, Z_pred, alpha=0.7, color="blue")
+                ax.plot_surface(X1, X2, Z_pred, alpha=0.75, color="blue")
                 ax.set_title("3D Linear Regression")
-                st.pyplot(fig)
+                st.pyplot(fig, clear_figure=True)
 
-            # Smooth rotation animation
+            # Smooth rotation animation for HF
             else:
                 placeholder = st.empty()
 
-                for angle in range(0, 360, 3):
-                    fig = plt.figure(figsize=(6, 5))
+                for angle in range(0, 360, 5):  # Less frequent frames → smoother on HF
+                    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)
+                    idx = np.random.choice(len(Z.ravel()), min(300, len(Z.ravel())), replace=False)
                     ax.scatter(X1.ravel()[idx], X2.ravel()[idx], Z.ravel()[idx],
-                               alpha=0.22, color="orange", s=5)
+                               alpha=0.2, color="orange", s=6)
 
                     ax.plot_surface(X1, X2, Z_pred, alpha=0.75, color="blue")
 
                     ax.view_init(elev=25, azim=angle)
-                    ax.set_title("🔄 Rotating 3D Linear Regression")
+                    ax.set_title("🔄 Rotating 3D Regression Model")
 
-                    placeholder.pyplot(fig)
+                    placeholder.pyplot(fig, clear_figure=True)
+                    st.sleep(0.07)  # REQUIRED for HF to animate
 
         with col2:
             st.metric("MSE", f"{mse:.4f}")
-            st.write("### Equation:")
             st.code(f"z = {model.coef_[0]:.3f}x₁ + {model.coef_[1]:.3f}x₂ + {model.intercept_:.3f}")
 
 else: