Update pages/Tensorflow.py

pages/Tensorflow.py

@@ -1,68 +1,3 @@
-import streamlit as st
-import base64
-
-# Set page config
-st.set_page_config(page_title="Neural Network Playground", layout="centered")
-
-# Load and encode background image
-def get_base64(file_path):
-    with open(file_path, "rb") as f:
-        data = f.read()
-    return base64.b64encode(data).decode()
-
-img_base64 = get_base64("ann.jpeg")  
-
-# Inject CSS with base64 background
-st.markdown(
-    f"""
-    <style>
-    .stApp {{
-        background-image: url("data:image/jpg;base64,{img_base64}");
-        background-size: cover;
-        background-position: center;
-        background-repeat: no-repeat;
-        background-attachment: fixed;
-    }}
-    </style>
-    """,
-    unsafe_allow_html=True
-)
-
-# Title
-st.markdown(
-    """
-    <h1 style='text-align: center; color: #FF6347; font-weight: bold;'>
-        Neural Network Playground
-    </h1>
-    """,
-    unsafe_allow_html=True
-)
-
-# Subtitle
-st.markdown(
-    """
-    <h3 style='text-align: center; color: #2E8B57; font-weight: normal;'>
-        Dive into the world of neural networks—explore and train with ease!
-    </h3>
-    """,
-    unsafe_allow_html=True
-)
-
-# About section
-st.subheader("🔎 :blue[About the App:]")
-
-st.markdown("""
-Neural Network Playground is an interactive tool designed for hands-on exploration of machine learning models.
-Whether you're just starting or already exploring advanced concepts, this platform lets you:
-- 🧑‍💻 Build and visualize neural networks with ease and fun.
-- 🔬 Train models on interactive datasets with real-time updates.
-- 🛠️ Experiment with various architectures and see instant results.
-- 🧠 Adjust hyperparameters and observe their effects on model learning—live!
-No coding required. Just pure, interactive learning.
-""")
-
-
-
 import streamlit as st
 import base64
 import matplotlib.pyplot as plt
@@ -76,21 +11,14 @@ from keras.optimizers import SGD
 from mlxtend.plotting import plot_decision_regions
 import numpy as np
 import tensorflow as tf
-from tensorflow import keras
 
-
-# Page title with new theme
-st.markdown(
-    "<h1 style='text-align: center; color: #FF6347;'>🤖 Neural Network Playground</h1>",
-    unsafe_allow_html=True
-)
 # Load and encode background image
 def get_base64(file_path):
     with open(file_path, "rb") as f:
         data = f.read()
     return base64.b64encode(data).decode()
 
-img_base64 = get_base64("ann.jpeg")  
+#img_base64 = get_base64("ann.jpeg")  
 
 # Inject CSS with base64 background
 st.markdown(
@@ -107,10 +35,16 @@ st.markdown(
     """,
     unsafe_allow_html=True
 )
-# Sidebar configuration with new theme
+
+# Page title
+st.markdown(
+    "<h1 style='text-align: center; color: #FF6347;'>🤖 Neural Network Playground</h1>",
+    unsafe_allow_html=True
+)
+
+# Sidebar configuration
 st.sidebar.title("⚙️ Model Configuration")
 
-# User input options in sidebar with theme
 num_points = st.sidebar.slider("Number of Data Points", 100, 10000, 1000, step=100)
 noise = st.sidebar.slider("Noise", 0.01, 0.9, 0.1)
 batch_size = st.sidebar.slider("Batch Size", 1, 512, 32)
@@ -120,11 +54,11 @@ hidden_layers = st.sidebar.slider("Hidden Layers", 1, 5, 2)
 neurons_per_layer = st.sidebar.slider("Neurons per Layer", 1, 512, 32)
 activation_name = st.sidebar.selectbox("Activation Function", ["relu", "tanh", "sigmoid", "linear"])
 
-# Dataset selection with new theme
+# Dataset selection
 st.subheader("📊 Dataset Selection")
 dataset_option = st.selectbox("Choose the dataset", ("circle", "moons", "classification"))
 
-# Dataset generation based on user selection
+# Dataset generation
 if dataset_option == "circle":
     x, y = make_circles(n_samples=num_points, noise=noise, factor=0.5, random_state=42)
 elif dataset_option == "moons":
@@ -133,37 +67,34 @@ else:
     x, y = make_classification(n_samples=num_points, n_features=2, n_informative=2,
                                 n_redundant=0, n_clusters_per_class=1, random_state=42)
 
-# Submit button
+# Display data
 if st.button("🚀 Submit"):
     st.subheader("📍 Input Data")
     fig, ax = plt.subplots()
     sns.scatterplot(x=x[:, 0], y=x[:, 1], hue=y, palette='Set2', ax=ax)
     st.pyplot(fig)
 
-# Train button with a fresh theme for model training
+# Train the model
 if st.button("🧠 Train the model"):
     with st.spinner("⏳ Training the model..."):
-        # Data split and scale
         x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=1, stratify=y)
         scaler = StandardScaler()
         x_train = scaler.fit_transform(x_train)
         x_test = scaler.transform(x_test)
 
-        # Model architecture
         model = Sequential()
         model.add(Dense(neurons_per_layer, input_shape=(2,), activation=activation_name))
         for _ in range(hidden_layers - 1):
             model.add(Dense(neurons_per_layer, activation=activation_name))
         model.add(Dense(1, activation='sigmoid'))
 
-        # Compile and train
         sgd = SGD(learning_rate=learning_rate)
         model.compile(optimizer=sgd, loss='binary_crossentropy', metrics=['accuracy'])
         history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.2, verbose=0)
 
     st.success("✅ Training Complete!")
 
-    # Show training plots with a fresh look
+    # Training plot
     st.subheader("📈 Training Progress")
     fig, ax = plt.subplots()
     ax.plot(history.history['loss'], label='Training Loss')
@@ -173,13 +104,13 @@ if st.button("🧠 Train the model"):
     ax.legend()
     st.pyplot(fig)
 
-    # Display final loss metrics
+    # Final metrics
     final_loss = history.history['loss'][-1]
     final_val_loss = history.history['val_loss'][-1]
     st.write(f"🧮 Final Training Loss: **{final_loss:.4f}**")
     st.write(f"✅ Final Validation Loss: **{final_val_loss:.4f}**")
 
-    # Decision boundary visualization with a fresh UI
+    # Decision boundary
     class KerasClassifierWrapper:
         def __init__(self, model):
             self.model = model
@@ -191,4 +122,4 @@ if st.button("🧠 Train the model"):
         st.subheader("📌 Decision Boundary (Training Data)")
         fig, ax = plt.subplots()
         plot_decision_regions(X=x_train, y=y_train, clf=KerasClassifierWrapper(model), ax=ax)
-        st.pyplot(fig)
+        st.pyplot(fig)