Update app.py

app.py

@@ -1,58 +1,58 @@
-import streamlit as st
-import base64
-
-# Set page config
-st.set_page_config(page_title="🧠 Neural Network Playground", layout="wide")
-
-# Background image function
-def set_background(image_path):
-    with open(image_path, "rb") as image_file:
-        encoded = base64.b64encode(image_file.read()).decode()
-
-    st.markdown(
-        f"""
-        <style>
-        .stApp {{
-            background-image: url("data:image/png;base64,{encoded}");
-            background-size: cover;
-            background-repeat: no-repeat;
-            background-position: center;
-            background-attachment: fixed;
-        }}
-        </style>
-        """,
-        unsafe_allow_html=True
-    )
-
-# Set background image
-set_background(r"ann.jpeg")
-
-
-# --- Project README/Intro Section ---
-
-st.markdown("""
-# 🧠 Interactive Neural Network Playground
-A Python app that lets users explore how neural networks learn by adjusting hyperparameters and visualizing the results.
----
-### 🚀 What It Does
-- Lets you choose:
-    - **Dataset**: moons, circles, blobs, classification  
-    - **Learning rate**  
-    - **Activation**: ReLU, Sigmoid, Tanh  
-    - **Train-test split ratio**  
-    - **Batch size**
-    - **Epochs**:  
-    - **Hidden Layes**  
-    - **Number of Neurons**
-    
-    
-- Builds & trains a TensorFlow/Keras neural network on synthetic data.
-- Visualizes:
-    - 🌈 **Decision boundaries** (how the model classifies the space)
-    - 📈 **Training vs testing error** across epochs
----
-### 🎯 Why It’s Useful
-✅ Understand hyperparameter effects  
-✅ See overfitting vs underfitting visually  
-✅ Learn neural network behavior interactively
-""")
+import streamlit as st
+import base64
+
+# Set page config
+st.set_page_config(page_title="🧠 Neural Network Playground", layout="wide")
+
+# Background image function
+def set_background(image_path):
+    with open(image_path, "rb") as image_file:
+        encoded = base64.b64encode(image_file.read()).decode()
+
+    st.markdown(
+        f"""
+        <style>
+        .stApp {{
+            background-image: url("data:image/png;base64,{encoded}");
+            background-size: cover;
+            background-repeat: no-repeat;
+            background-position: center;
+            background-attachment: fixed;
+        }}
+        </style>
+        """,
+        unsafe_allow_html=True
+    )
+
+# Set background image
+#set_background(r"ann.jpeg")
+
+
+# --- Project README/Intro Section ---
+
+st.markdown("""
+# 🧠 Interactive Neural Network Playground
+A Python app that lets users explore how neural networks learn by adjusting hyperparameters and visualizing the results.
+---
+### 🚀 What It Does
+- Lets you choose:
+    - **Dataset**: moons, circles, blobs, classification  
+    - **Learning rate**  
+    - **Activation**: ReLU, Sigmoid, Tanh  
+    - **Train-test split ratio**  
+    - **Batch size**
+    - **Epochs**:  
+    - **Hidden Layes**  
+    - **Number of Neurons**
+    
+    
+- Builds & trains a TensorFlow/Keras neural network on synthetic data.
+- Visualizes:
+    - 🌈 **Decision boundaries** (how the model classifies the space)
+    - 📈 **Training vs testing error** across epochs
+---
+### 🎯 Why It’s Useful
+✅ Understand hyperparameter effects  
+✅ See overfitting vs underfitting visually  
+✅ Learn neural network behavior interactively
+""")