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app.py

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+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("tf.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: #FF6336; 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
+import seaborn as sns
+from sklearn.datasets import make_circles, make_moons, make_classification
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from keras.models import Sequential
+from keras.layers import Dense
+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("tf.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
+)
+# Sidebar configuration with new theme
+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)
+epochs = st.sidebar.slider("Epochs", 1, 100, 10)
+learning_rate = st.sidebar.slider("Learning Rate", 0.0001, 1.0, 0.01, step=0.0001, format="%.4f")
+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
+st.subheader("📊 Dataset Selection")
+dataset_option = st.selectbox("Choose the dataset", ("circle", "moons", "classification"))
+
+# Dataset generation based on user selection
+if dataset_option == "circle":
+    x, y = make_circles(n_samples=num_points, noise=noise, factor=0.5, random_state=42)
+elif dataset_option == "moons":
+    x, y = make_moons(n_samples=num_points, noise=noise, random_state=42)
+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
+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
+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
+    st.subheader("📈 Training Progress")
+    fig, ax = plt.subplots()
+    ax.plot(history.history['loss'], label='Training Loss')
+    ax.plot(history.history['val_loss'], label='Validation Loss')
+    ax.set_title("Training vs Validation Loss")
+    ax.set_xlabel("Epoch")
+    ax.legend()
+    st.pyplot(fig)
+
+    # Display final loss 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
+    class KerasClassifierWrapper:
+        def __init__(self, model):
+            self.model = model
+
+        def predict(self, X):
+            return (self.model.predict(X) > 0.5).astype("int32")
+
+    with st.spinner("🔮 Generating decision boundary..."):
+        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)

requirements.txt

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+streamlit
+matplotlib
+seaborn
+scikit-learn
+tensorflow
+keras
+mlxtend