Delete app.py

app.py

@@ -1,194 +0,0 @@
-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("tf1.jpg")  
-
-# 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("tf1.jpg")  
-
-# 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)