src/streamlit_app.py

import streamlit as st
import numpy as np
import time
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

st.set_page_config(page_title="Linear Regression Playground", layout="centered")

# === FIX: fully visible equation with dark box ===
st.markdown("""
<style>
.eq-box {
    border: 2px solid #444;
    border-radius: 8px;
    background: #222;         /* DARK background */
    padding: 14px;
    width: 100%;
    font-size: 22px;
    color: white !important;  /* WHITE text */
    text-align: center;
    margin-top: 14px;
}
.mathjax-chtml, .MathJax {
    color: white !important;  /* Force formula text white */
}
</style>
""", unsafe_allow_html=True)

st.title("📉 Linear Regression Playground (2D & 3D)")
st.write("Experiment with regression, noise, slope, intercept — and visualize the model!")

# ------------------------------------
# Sidebar Controls
# ------------------------------------
st.sidebar.header("⚙️ Controls")

mode = st.sidebar.radio("Choose Mode", ["2D Regression", "3D Regression"])
num_points = st.sidebar.slider("Number of Data Points", 20, 500, 100)
noise_level = st.sidebar.slider("Noise Level", 0.0, 5.0, 1.0)

rotate_3d = False
if mode == "3D Regression":
    rotate_3d = st.sidebar.toggle("🔄 Rotate 3D Model", value=False)

train_btn = st.sidebar.button("Generate & Train Model")

if "trained" not in st.session_state:
    st.session_state.trained = False
    st.session_state.current_mode = None

if mode != st.session_state.current_mode:
    st.session_state.trained = False
    st.session_state.current_mode = mode

# ------------------------------------
# Generate dataset
# ------------------------------------
if train_btn:

    with st.spinner("⏳ Training model..."):
        time.sleep(0.5)

        if mode == "2D Regression":
            X = np.linspace(0, 10, num_points).reshape(-1, 1)
            y = 2.5 * X.flatten() + 5 + np.random.randn(num_points) * noise_level

            model = LinearRegression().fit(X, y)
            y_pred = model.predict(X)
            mse = mean_squared_error(y, y_pred)

            st.session_state.data = (X, y, y_pred, mse, model)

        else:
            x1 = np.linspace(0, 10, num_points)
            x2 = np.linspace(0, 10, num_points)
            X1, X2 = np.meshgrid(x1, x2)

            noise = np.random.randn(num_points, num_points) * noise_level
            Z = 3 * X1 + 2 * X2 + 10 + noise

            X_flat = np.column_stack((X1.ravel(), X2.ravel()))
            Z_flat = Z.ravel()

            model = LinearRegression().fit(X_flat, Z_flat)
            Z_pred = model.predict(X_flat).reshape(num_points, num_points)
            mse = mean_squared_error(Z_flat, Z_pred.ravel())

            st.session_state.data = (X1, X2, Z, Z_pred, mse, model)

        st.session_state.trained = True

# ------------------------------------
# Visualization
# ------------------------------------
if st.session_state.trained:

    st.success("🎉 Model trained successfully!")

    # ----------------- 2D Regression -----------------
    if mode == "2D Regression":
        X, y, y_pred, mse, model = st.session_state.data

        col1, col2 = st.columns([2, 1])

        with col1:
            fig, ax = plt.subplots(figsize=(4.5, 4))
            ax.scatter(X, y, color="orange", label="Data", s=18)
            ax.plot(X, y_pred, color="blue", linewidth=2, label="Regression Line")
            ax.set_title("2D Linear Regression")
            ax.legend()
            st.pyplot(fig, clear_figure=True)

        with col2:
            st.metric("MSE", f"{mse:.4f}")

            equation = rf"y = {model.coef_[0]:.3f}x + {model.intercept_:.3f}"
            st.markdown(f"<div class='eq-box'>${equation}$</div>", unsafe_allow_html=True)

    # ----------------- 3D Regression -----------------
    else:
        X1, X2, Z, Z_pred, mse, model = st.session_state.data

        col1, col2 = st.columns([2, 1])

        with col1:
            if not rotate_3d:
                fig = plt.figure(figsize=(4.5, 4))
                ax = fig.add_subplot(111, projection="3d")

                idx = np.random.choice(len(Z.ravel()), min(350, len(Z.ravel())), replace=False)
                ax.scatter(X1.ravel()[idx], X2.ravel()[idx], Z.ravel()[idx],
                           color="orange", alpha=0.25, s=8)

                ax.plot_surface(X1, X2, Z_pred, alpha=0.75, color="blue")
                ax.set_title("3D Linear Regression")
                st.pyplot(fig, clear_figure=True)
            else:
                placeholder = st.empty()
                for angle in range(0, 360, 5):
                    fig = plt.figure(figsize=(4.5, 4))
                    ax = fig.add_subplot(111, projection="3d")

                    idx = np.random.choice(len(Z.ravel()), min(300, len(Z.ravel())), replace=False)
                    ax.scatter(X1.ravel()[idx], X2.ravel()[idx], Z.ravel()[idx],
                               alpha=0.2, color="orange", s=6)

                    ax.plot_surface(X1, X2, Z_pred, alpha=0.75, color="blue")
                    ax.view_init(elev=25, azim=angle)
                    ax.set_title("🔄 Rotating 3D Regression Model")

                    placeholder.pyplot(fig, clear_figure=True)
                    time.sleep(0.07)

        with col2:
            st.metric("MSE", f"{mse:.4f}")

            a = model.coef_[0]
            b = model.coef_[1]
            c = model.intercept_

            equation3d = rf"z = {a:.3f}x_1 + {b:.3f}x_2 + {c:.3f}"
            st.markdown(f"<div class='eq-box'>${equation3d}$</div>", unsafe_allow_html=True)

else:
    st.info("Click **Generate & Train Model** to begin.")