app.py

import streamlit as st
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers import InputLayer, Dense
from sklearn.datasets import make_circles, make_classification, make_moons, make_blobs
from mlxtend.plotting import plot_decision_regions
from keras.optimizers import SGD
import time

# Custom background and styling
st.markdown(
    """
    <style>
        .main {
            background: linear-gradient(to right, #f0f4f8, #d9e2ec);
        }
    </style>
    """,
    unsafe_allow_html=True
)

# App Title
st.title("🧠 NeuroVision Lab - Interactive Neural Network Playground")

# Sidebar: Dataset selection
st.sidebar.header("🎲 Generate Synthetic Data")
data_type = st.sidebar.selectbox("Select Dataset Type", ["make_circles", "make_classification", "make_moons", "make_blobs"])
factor = st.sidebar.slider("Circle Factor (for make_circles)", 0.1, 1.0, 0.2)
noise = st.sidebar.slider("Add Noise", 0.0, 1.0, 0.1)
samples = st.sidebar.slider("Total Samples", 1000, 10000, 10000, step=100)

generate_scatter = st.sidebar.button("🔄 Create Dataset")

# Initialize session state
if 'X' not in st.session_state:
    st.session_state['X'] = None
if 'y' not in st.session_state:
    st.session_state['y'] = None

# Function to generate data
def generate_data(data_type, samples, noise, factor):
    if data_type == "make_circles":
        st.session_state['X'], st.session_state['y'] = make_circles(n_samples=samples, noise=noise, factor=factor, random_state=42)
    elif data_type == "make_classification":
        st.session_state['X'], st.session_state['y'] = make_classification(n_samples=samples, n_features=2, n_informative=2,
                                                                             n_redundant=0, n_clusters_per_class=1, flip_y=noise, random_state=42)
    elif data_type == "make_moons":
        st.session_state['X'], st.session_state['y'] = make_moons(n_samples=samples, noise=noise, random_state=42)
    elif data_type == "make_blobs":
        st.session_state['X'], st.session_state['y'] = make_blobs(n_samples=samples, centers=2, cluster_std=1.0, random_state=42)

# Scatterplot of generated data
if generate_scatter:
    generate_data(data_type, samples, noise, factor)
    if st.session_state['X'] is not None and st.session_state['y'] is not None:
        df = pd.DataFrame(st.session_state['X'], columns=["x1", "x2"])
        df["label"] = st.session_state['y']

        st.subheader(f"🧩 Visualizing: {data_type}")
        fig1, ax1 = plt.subplots()
        sns.scatterplot(data=df, x="x1", y="x2", hue="label", palette="viridis", ax=ax1)
        st.pyplot(fig1)
    else:
        st.warning("Data generation unsuccessful. Please check your parameters.")

# Sidebar: Training Configuration
st.sidebar.header("⚙️ Model Configuration")
test_percent = st.sidebar.slider("Test Set (%)", 10, 90, 20)
test_size = test_percent / 100
learning_rate = st.sidebar.selectbox("Choose Learning Rate", [0.0001, 0.001, 0.01, 0.1])
act_fun = st.sidebar.selectbox("Activation Function", ["sigmoid", "tanh", "relu"])
batch_size = st.sidebar.slider("Batch Size", 1, 10000, 6400)
epochs = st.sidebar.slider("Training Epochs", 1, 1000, 600)

# Train Model and Plot Decision Surface
if st.sidebar.button("🧮 Train Model & Show Decision Surface"):
    if st.session_state['X'] is None or st.session_state['y'] is None:
        st.error("⚠️ Please generate a dataset first.")
    else:
        # Preprocessing
        x_train, x_test, y_train, y_test = train_test_split(st.session_state['X'], st.session_state['y'], test_size=test_size, stratify=st.session_state['y'], random_state=1)
        scaler = StandardScaler()
        x_train = scaler.fit_transform(x_train)
        x_test = scaler.transform(x_test)

        # Build model
        model = Sequential()
        model.add(InputLayer(input_shape=(2,)))
        for units in [4, 2, 2]:
            model.add(Dense(units, activation=act_fun))
        model.add(Dense(1, activation="sigmoid"))

        sgd = SGD(learning_rate=learning_rate)
        model.compile(optimizer=sgd, loss="binary_crossentropy", metrics=["accuracy"])

        # Show training progress
        st.subheader("🔁 Model Training Progress")
        progress_bar = st.progress(0)
        progress_pct = st.empty()

        history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=0, validation_split=0.2)
        for epoch in range(epochs):
            progress = int((epoch + 1) / epochs * 100)
            progress_bar.progress(progress)
            progress_pct.write(f"{progress}%")
            time.sleep(0.01)

        # Decision surface visualization
        st.subheader("🧠 Neural Network Decision Boundary")
        fig2, ax2 = plt.subplots()
        plot_decision_regions(x_train, y_train, clf=model, legend=2, ax=ax2)
        st.pyplot(fig2)

        st.session_state['history'] = history

# Show Loss Curve
if st.sidebar.button("📉 Display Loss Curve"):
    if 'history' in st.session_state:
        st.subheader("📉 Training vs Validation Loss")
        history = st.session_state['history']
        fig3, ax3 = plt.subplots()
        ax3.plot(history.history['loss'], label='Train Loss')
        ax3.plot(history.history['val_loss'], label='Val Loss')
        ax3.set_xlabel("Epochs")
        ax3.set_ylabel("Loss")
        ax3.set_title("Loss Progress Over Time")
        ax3.legend()
        st.pyplot(fig3)
    else:
        st.warning("⏳ Train the model to visualize the loss curve.")