Update pages/Tensorflow.py

pages/Tensorflow.py

@@ -12,114 +12,121 @@ from mlxtend.plotting import plot_decision_regions
 import numpy as np
 import tensorflow as tf
 
-# 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
-# )
-
-# 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")
-
-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)
+# Set page configuration
+st.set_page_config(page_title="🧠 Neural Network Explorer", layout="wide")
+
+# Function to apply blurred background image
+def set_blurred_background(image_path):
+    with open(image_path, "rb") as img_file:
+        img_base64 = base64.b64encode(img_file.read()).decode()
+    st.markdown(
+        f"""
+        <style>
+        .stApp {{
+            background-image: url("data:image/png;base64,{img_base64}");
+            background-size: cover;
+            background-attachment: fixed;
+            background-position: center;
+            filter: blur(4px);
+        }}
+        .main > div {{
+            background-color: rgba(255, 255, 255, 0.9);
+            padding: 2rem;
+            border-radius: 15px;
+        }}
+        </style>
+        """,
+        unsafe_allow_html=True
+    )
+
+# Uncomment and set your image path
+# set_blurred_background("ann.jpeg")
+
+# Title
+st.markdown("""
+<h1 style='text-align: center; color: #4B0082;'>✨ Neural Network Explorer</h1>
+<h4 style='text-align: center; color: #2F4F4F;'>Visualize and train simple neural networks interactively</h4>
+""", unsafe_allow_html=True)
+
+# Customized sidebar layout and colors
+st.sidebar.markdown("""
+<style>
+section[data-testid="stSidebar"] > div:first-child {{
+    background-color: #F0F8FF;
+    padding: 1rem;
+    border-radius: 10px;
+}}
+</style>
+""", unsafe_allow_html=True)
+
+st.sidebar.header("🔧 Configure Model")
+num_points = st.sidebar.slider("Number of Samples", 100, 10000, 1000, step=100)
+noise = st.sidebar.slider("Dataset 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)
+epochs = st.sidebar.slider("Epochs", 1, 100, 20)
 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"])
+neurons_per_layer = st.sidebar.slider("Neurons per Layer", 1, 128, 16)
+activation_name = st.sidebar.selectbox("Activation", ["relu", "tanh", "sigmoid", "linear"])
 
 # Dataset selection
-st.subheader("📊 Dataset Selection")
-dataset_option = st.selectbox("Choose the dataset", ("circle", "moons", "classification"))
+st.markdown("## 🧪 Dataset Selection")
+dataset_option = st.selectbox("Select a dataset", ("circle", "moons", "classification"))
 
-# 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":
     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)
+    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)
 
-# Display data
-if st.button("🚀 Submit"):
-    st.subheader("📍 Input Data")
+# Show input data
+if st.button("📊 Show Dataset"):
+    st.subheader("🎯 Sample Distribution")
     fig, ax = plt.subplots()
-    sns.scatterplot(x=x[:, 0], y=x[:, 1], hue=y, palette='Set2', ax=ax)
+    sns.scatterplot(x=x[:, 0], y=x[:, 1], hue=y, palette="coolwarm", ax=ax)
     st.pyplot(fig)
 
-# Train the model
-if st.button("🧠 Train the model"):
-    with st.spinner("⏳ Training the model..."):
-        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)
+# Train model
+if st.button("🚀 Train Model"):
+    st.subheader("⚙️ Training the Model...")
+    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42, stratify=y)
+    scaler = StandardScaler()
+    x_train = scaler.fit_transform(x_train)
+    x_test = scaler.transform(x_test)
 
-        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'))
+    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"))
 
-        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)
+    optimizer = SGD(learning_rate=learning_rate)
+    model.compile(optimizer=optimizer, 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!")
+    st.success("✅ Model trained successfully!")
 
-    # Training plot
-    st.subheader("📈 Training Progress")
+    st.subheader("📉 Training Metrics")
     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.plot(history.history['loss'], label='Train Loss')
+    ax.plot(history.history['val_loss'], label='Val Loss')
+    ax.set_title("Loss Over Epochs")
     ax.legend()
     st.pyplot(fig)
 
-    # 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}**")
+    st.write(f"🔢 Final Training Loss: **{history.history['loss'][-1]:.4f}**")
+    st.write(f"🔍 Final Validation Loss: **{history.history['val_loss'][-1]:.4f}**")
 
-    # Decision boundary
-    class KerasClassifierWrapper:
+    class ModelWrapper:
         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)
+    st.subheader("🌈 Decision Boundary")
+    fig, ax = plt.subplots()
+    plot_decision_regions(X=x_train, y=y_train, clf=ModelWrapper(model), ax=ax)
+    st.pyplot(fig)