Update pages/Tensorflow.py

pages/Tensorflow.py

@@ -1,96 +1,194 @@
-import streamlit as st
-import tensorflow as tf
-from tensorflow.keras.layers import Input, Dense
-from tensorflow.keras.models import Sequential
-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-from sklearn.datasets import make_moons, make_circles, make_blobs, make_classification
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import StandardScaler
-from mlxtend.plotting import plot_decision_regions
-import numpy as np
-
-st.title("Tensorflow Playground")
-
-data_sets = {
-    "make_classification": make_classification,
-    "make_moons": make_moons,
-    "make_circles": make_circles,
-    "make_blobs": make_blobs
-}
-
-learning_rate = st.sidebar.slider('Learning Rate', min_value=0.0001, max_value=0.1, value=0.001, step=0.0001, format="%.4f")
-noise = st.sidebar.slider('Noise Level', min_value=0.0, max_value=1.0, value=0.1, step=0.01, format="%.2f")
-epochs = st.sidebar.slider("Epochs", 1, 100, 10)
-num_hidden_layers = st.sidebar.slider('Number of Hidden Layers', min_value=1, max_value=10, value=2, step=1)
-neurons_per_layer = st.sidebar.slider("Neurons per Layer", 1, 512, 32)
-activation_function = st.sidebar.radio("Select the activation function", ["relu", "sigmoid", "tanh"])
-batch_size = st.sidebar.slider('Batch Size', min_value=8, max_value=512, value=32, step=8)
-test_size = st.sidebar.slider('test_size:', min_value=0.1, max_value=0.9, value=0.3, step=0.05)
-
-data_choice = st.sidebar.radio("Select the dataset", list(data_sets.keys()))
-
-if data_choice == "make_classification":
-    X, y = make_classification(n_samples=2000, n_features=2, n_redundant=0, n_clusters_per_class=1, random_state=27)
-elif data_choice == "make_blobs":
-    X, y = make_blobs(n_samples=2000, n_features=2, random_state=27)
-else:
-    X, y = data_sets[data_choice](n_samples=2000, noise=noise, random_state=27)
-
-if st.button("Start"):
-    st.subheader("📍 Input Data")
-    fig, ax = plt.subplots(figsize=(8, 6))
-    sns.scatterplot(x=X[:, 0], y=X[:, 1], hue=y, palette='Set2', ax=ax)
-    st.pyplot(fig)
-
-if st.button("Train Model"):
-    model = Sequential()
-    model.add(Input(shape=(2,)))
-    for i in range(num_hidden_layers):
-        model.add(Dense(units=neurons_per_layer, activation=activation_function))
-    model.add(Dense(units=1, activation="sigmoid"))
-
-    optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)
-    model.compile(optimizer=optimizer, loss="binary_crossentropy", metrics=["accuracy"])
-
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=27)
-    scaler = StandardScaler()
-    X_train_scaled = scaler.fit_transform(X_train)
-    X_test_scaled = scaler.transform(X_test)
-
-    history = model.fit(X_train_scaled, y_train, epochs=epochs, batch_size=batch_size, validation_split=0.2, verbose=0)
-
-    st.write(f"🧮 Final Training Loss: **{history.history['loss'][-1]:.4f}**")
-    st.write(f"✅ Final Validation Loss: **{history.history['val_loss'][-1]:.4f}**")
-
-    col1, col2 = st.columns(2)
-
-    with col1:
-        st.markdown("#### Training vs Validation Loss Plot")
-        fig1, ax1 = plt.subplots(figsize=(8, 6))
-        ax1.plot(history.history["loss"], label="Training Loss", color="green")
-        ax1.plot(history.history["val_loss"], label="Validation Loss", color="red")
-        ax1.set_xlabel("Epoch", fontsize=14, fontweight='bold')
-        ax1.set_ylabel("Loss", fontsize=14, fontweight='bold')
-        ax1.legend()
-        st.pyplot(fig1)
-
-    with col2:
-        st.markdown("#### Decision Boundary Plot")
-
-        class KerasClassifierWrapper:
-            def __init__(self, model, scaler):
-                self.model = model
-                self.scaler = scaler
-
-            def predict(self, X):
-                X_scaled = self.scaler.transform(X)
-                preds = self.model.predict(X_scaled)
-                return np.where(preds > 0.5, 1, 0).flatten()
-
-        keras_clf = KerasClassifierWrapper(model, scaler)
-
-        fig2, ax2 = plt.subplots(figsize=(8, 6))
-        plot_decision_regions(X=X_train, y=y_train, clf=keras_clf, ax=ax2)
-        st.pyplot(fig2)
+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("bg.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
+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("bg.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)