Code Editor for app.py

import streamlit as st
import numpy as np
import plotly.graph_objects as go
from sklearn.datasets import make_blobs
import plotly.express as px

st.title("3D Data Visualization")

# Sidebar controls
viz_type = st.sidebar.selectbox(
    "Select Visualization",
    ["Spiral Helix", "Surface Plot", "3D Clusters"]
)

# Common styling controls
st.sidebar.subheader("Style Controls")
point_size = st.sidebar.slider("Point Size", 2, 10, 5)
opacity = st.sidebar.slider("Opacity", 0.1, 1.0, 0.7)
colorscale = st.sidebar.selectbox(
    "Color Scheme",
    ["Viridis", "Plasma", "Inferno", "Magma", "Cividis"]
)

# Generate spiral helix data
def generate_helix(n_points=100, noise=0.1):
    t = np.linspace(0, 10*np.pi, n_points)
    x = np.cos(t)
    y = np.sin(t)
    z = t/10
    # Add noise
    x += np.random.normal(0, noise, n_points)
    y += np.random.normal(0, noise, n_points)
    z += np.random.normal(0, noise, n_points)
    return x, y, z

# Generate surface plot data
def generate_surface(x_range=(-5, 5), y_range=(-5, 5), n_points=100):
    x = np.linspace(x_range[0], x_range[1], n_points)
    y = np.linspace(y_range[0], y_range[1], n_points)
    X, Y = np.meshgrid(x, y)
    Z = np.sin(X) * np.cos(Y)
    return X, Y, Z

# Generate cluster data
def generate_clusters(n_clusters=3, n_points=50):
    X, y = make_blobs(n_samples=n_points*n_clusters, centers=n_clusters, 
                      n_features=3, random_state=42)
    return X[:, 0], X[:, 1], X[:, 2], y

if viz_type == "Spiral Helix":
    st.subheader("3D Spiral Helix with Noise")
    
    noise_level = st.sidebar.slider("Noise Level", 0.0, 0.5, 0.1)
    rotation_speed = st.sidebar.slider("Animation Speed", 0, 100, 50)
    
    x, y, z = generate_helix(noise=noise_level)
    
    fig = go.Figure(data=[go.Scatter3d(
        x=x, y=y, z=z,
        mode='markers',
        marker=dict(
            size=point_size,
            opacity=opacity,
            color=z,
            colorscale=colorscale.lower(),
        )
    )])

elif viz_type == "Surface Plot":
    st.subheader("3D Surface Plot")
    
    freq_x = st.sidebar.slider("X Frequency", 0.1, 2.0, 1.0)
    freq_y = st.sidebar.slider("Y Frequency", 0.1, 2.0, 1.0)
    show_contours = st.sidebar.checkbox("Show Contours", False)
    
    X, Y, Z = generate_surface()
    Z = np.sin(X * freq_x) * np.cos(Y * freq_y)
    
    fig = go.Figure(data=[go.Surface(
        x=X, y=Y, z=Z,
        colorscale=colorscale.lower(),
        opacity=opacity,
        contours=dict(
            z=dict(
                show=show_contours,
                usecolormap=True,
                project_z=True
            )
        )
    )])

else:  # 3D Clusters
    st.subheader("3D Clusters")
    
    n_points = st.sidebar.slider("Points per Cluster", 20, 100, 50)
    x, y, z, labels = generate_clusters(n_points=n_points)
    
    fig = go.Figure(data=[go.Scatter3d(
        x=x, y=y, z=z,
        mode='markers',
        marker=dict(
            size=point_size,
            opacity=opacity,
            color=labels,
            colorscale=colorscale.lower(),
        )
    )])

# Common figure layout settings
fig.update_layout(
    scene=dict(
        xaxis_title="X",
        yaxis_title="Y",
        zaxis_title="Z",
        aspectmode='cube'
    ),
    width=800,
    height=800,
    margin=dict(l=0, r=0, b=0, t=30)
)

# Display the plot
st.plotly_chart(fig, use_container_width=True)

# Add info about interactivity
st.info("💡 You can interact with the plot using your mouse/touchpad:\n"
        "- Click and drag to rotate\n"
        "- Scroll to zoom\n"
        "- Right-click and drag to pan")