pages/1.Neural_Network_Visualizer.py

# === Imports ===
import streamlit as st
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_moons, make_circles, make_classification
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score
import io

# === Streamlit Config ===
st.set_page_config(page_title="🧠 ANN Visualizer", layout="wide")
st.markdown("<style>.main { background: linear-gradient(to right, #ece9e6, #ffffff); }</style>", unsafe_allow_html=True)

# === Sidebar ===
st.sidebar.title("⚙️ Model Configuration")

dataset = st.sidebar.selectbox("Select Dataset", ["moons", "circles", "classification"])
n_samples = st.sidebar.slider("Number of Samples", 100, 5000, 500, step=100)
noise = st.sidebar.slider("Noise Level", 0.0, 1.0, 0.2, step=0.05)

st.sidebar.subheader("Network Architecture")
n_layers = st.sidebar.number_input("Number of Hidden Layers", 1, 10, 2)

layer_config = []
activation_options = ["ReLU", "Tanh", "Sigmoid"]
reg_options = ["None", "L1", "L2", "L1_L2"]

for i in range(n_layers):
    with st.sidebar.expander(f"Layer {i+1}"):
        units = st.number_input(f"Units", 1, 512, 8, key=f"units_{i}")
        activation = st.selectbox("Activation", activation_options, key=f"act_{i}")
        dropout = st.slider("Dropout", 0.0, 0.9, 0.0, step=0.05, key=f"drop_{i}")
        reg = st.selectbox("Regularization", reg_options, key=f"reg_{i}")
        reg_strength = st.number_input("Reg Strength", 0.0001, 1.0, 0.001, key=f"reg_strength_{i}") if reg != "None" else 0.0
        layer_config.append((units, activation, dropout, reg, reg_strength))

st.sidebar.subheader("Training Settings")
learning_rate = st.sidebar.number_input("Learning Rate", 0.0001, 1.0, 0.01)
epochs = st.sidebar.slider("Epochs", 100, 5000, 500)
early_stop = st.sidebar.checkbox("Early Stopping", True)
patience = st.sidebar.slider("Patience", 1, 20, 5) if early_stop else None
min_delta = st.sidebar.number_input("Min Improvement (Delta)", 0.0001, 0.1, 0.001) if early_stop else None

# === Dataset Loader ===
def load_data(name, n_samples, noise):
    if name == "moons":
        X, y = make_moons(n_samples=n_samples, noise=noise, random_state=42)
    elif name == "circles":
        X, y = make_circles(n_samples=n_samples, noise=noise, factor=0.5, random_state=42)
    else:
        X, y = make_classification(n_samples=n_samples, n_features=2, n_informative=2, n_redundant=0, n_clusters_per_class=1, random_state=42)
    return X, y

# === Model Definition ===
activation_map = {
    "ReLU": nn.ReLU,
    "Tanh": nn.Tanh,
    "Sigmoid": nn.Sigmoid
}

class CustomLayer(nn.Module):
    def __init__(self, in_dim, out_dim, activation, dropout, reg, reg_strength):
        super().__init__()
        self.linear = nn.Linear(in_dim, out_dim)
        self.activation = activation_map[activation]()
        self.dropout = nn.Dropout(dropout)
        self.reg = reg
        self.reg_strength = reg_strength

    def forward(self, x):
        x = self.linear(x)
        x = self.activation(x)
        x = self.dropout(x)
        return x

    def reg_loss(self):
        if self.reg == "None":
            return 0
        elif self.reg == "L1":
            return self.reg_strength * torch.sum(torch.abs(self.linear.weight))
        elif self.reg == "L2":
            return self.reg_strength * torch.sum(self.linear.weight.pow(2))
        elif self.reg == "L1_L2":
            return self.reg_strength * (torch.sum(torch.abs(self.linear.weight)) + torch.sum(self.linear.weight.pow(2)))

class ANN(nn.Module):
    def __init__(self, input_dim, config, output_dim=2):
        super().__init__()
        self.layers = nn.ModuleList()
        self.reg_layers = []
        prev_dim = input_dim
        for units, act, drop, reg, reg_strength in config:
            layer = CustomLayer(prev_dim, units, act, drop, reg, reg_strength)
            self.layers.append(layer)
            self.reg_layers.append(layer)
            prev_dim = units
        self.output = nn.Linear(prev_dim, output_dim)

    def forward(self, x):
        for layer in self.layers:
            x = layer(x)
        return self.output(x)

    def total_reg_loss(self):
        return sum(layer.reg_loss() for layer in self.reg_layers)

# === Plot Helpers ===
def plot_boundary(model, X, y, title):
    x_min, x_max = X[:,0].min()-0.5, X[:,0].max()+0.5
    y_min, y_max = X[:,1].min()-0.5, X[:,1].max()+0.5
    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 400), np.linspace(y_min, y_max, 400))
    grid = np.c_[xx.ravel(), yy.ravel()]
    model.eval()
    with torch.no_grad():
        probs = torch.softmax(model(torch.tensor(grid, dtype=torch.float32)), dim=1)[:,1]
    Z = probs.reshape(xx.shape).cpu().numpy()

    fig, ax = plt.subplots()
    ax.contourf(xx, yy, Z, levels=50, cmap="Spectral", alpha=0.8)
    ax.scatter(X[:,0], X[:,1], c=y, edgecolors='k', cmap="Spectral", s=20)
    ax.set_title(title)
    ax.set_xticks([])
    ax.set_yticks([])
    return fig

# === Training Function ===
def train(model, X_train, y_train, X_test, y_test, epochs, lr, early_stop, patience, min_delta):
    optimizer = optim.Adam(model.parameters(), lr=lr)
    criterion = nn.CrossEntropyLoss()
    train_losses, test_losses = [], []
    best_loss = np.inf
    stop_counter = 0

    for epoch in range(epochs):
        model.train()
        optimizer.zero_grad()
        y_pred = model(X_train)
        loss = criterion(y_pred, y_train) + model.total_reg_loss()
        loss.backward()
        optimizer.step()
        train_losses.append(loss.item())

        model.eval()
        with torch.no_grad():
            test_pred = model(X_test)
            test_loss = criterion(test_pred, y_test) + model.total_reg_loss()
            test_losses.append(test_loss.item())

        # Early Stopping
        if early_stop:
            if test_losses[-1] < best_loss - min_delta:
                best_loss = test_losses[-1]
                stop_counter = 0
            else:
                stop_counter += 1
                if stop_counter >= patience:
                    st.warning(f"⏹️ Early Stopping at Epoch {epoch+1}")
                    break

        yield epoch+1, train_losses, test_losses

# === Main Page ===
st.title("🧠 Interactive ANN Visualizer")

if st.button("🚀 Start Training"):
    X, y = load_data(dataset, n_samples, noise)
    X = StandardScaler().fit_transform(X)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

    X_train_tensor = torch.tensor(X_train, dtype=torch.float32)
    y_train_tensor = torch.tensor(y_train, dtype=torch.long)
    X_test_tensor = torch.tensor(X_test, dtype=torch.float32)
    y_test_tensor = torch.tensor(y_test, dtype=torch.long)

    model = ANN(X.shape[1], layer_config)

    tabs = st.tabs(["🌐 Decision Boundary", "📈 Training Curve", "📊 Results"])

    with tabs[0]:
        st.subheader("Initial Random Surface")
        fig_init = plot_boundary(model, X, y, "Initial Decision Surface")
        st.pyplot(fig_init)

    train_losses, test_losses = [], []
    progress = st.progress(0)

    for epoch, train_losses, test_losses in train(model, X_train_tensor, y_train_tensor, X_test_tensor, y_test_tensor, epochs, learning_rate, early_stop, patience, min_delta):
        if epoch % (epochs//5) == 0:
            with tabs[0]:
                st.subheader(f"Epoch {epoch} Decision Surface")
                fig = plot_boundary(model, X, y, f"Decision Surface at Epoch {epoch}")
                st.pyplot(fig)
        progress.progress(epoch/epochs)

    with tabs[1]:
        st.subheader("Loss Curves")
        fig_loss, ax = plt.subplots()
        ax.plot(train_losses, label="Train Loss")
        ax.plot(test_losses, label="Test Loss")
        ax.legend()
        st.pyplot(fig_loss)

    with tabs[2]:
        st.subheader("Final Metrics")
        model.eval()
        train_preds = model(X_train_tensor).argmax(dim=1)
        test_preds = model(X_test_tensor).argmax(dim=1)

        st.metric("Train Accuracy", f"{accuracy_score(y_train, train_preds):.2%}")
        st.metric("Test Accuracy", f"{accuracy_score(y_test, test_preds):.2%}")

        fig_final = plot_boundary(model, X, y, "Final Decision Surface")
        st.pyplot(fig_final)

        buf = io.BytesIO()
        fig_final.savefig(buf, format="png")
        st.download_button("⬇️ Download Final Boundary", buf.getvalue(), "final_boundary.png", "image/png")