import gradio as gr
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import plotly.express as px

# --- Generate synthetic data ---
def train_and_visualize(seed, samples):
    np.random.seed(seed)
    
    # Generate data
    X = np.random.randn(samples, 2)
    y = (X[:, 0] + X[:, 1] > 0).astype(int)

    # Train/test split
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

    # Train model
    model = LogisticRegression()
    model.fit(X_train, y_train)

    # Evaluate
    preds = model.predict(X_test)
    acc = accuracy_score(y_test, preds)

    # Visualization
    fig = px.scatter(
        x=X[:, 0], y=X[:, 1], color=y.astype(str),
        title="Synthetic Data Distribution",
        labels={"x": "Feature 1", "y": "Feature 2", "color": "Label"},
        opacity=0.8
    )

    fig.update_layout(template="plotly_dark", width=600, height=500)

    return f"Accuracy: {acc:.3f}\nCoefficients: {model.coef_}\nIntercept: {model.intercept_}", fig

# --- UI Layout ---
with gr.Blocks() as demo:
    gr.Markdown("""
    # 🌟 Logistic Regression Demo (Synthetic Data)
    Explore how logistic regression behaves on randomly generated 2-D data. 
    Adjust the **seed** and **sample size** to see different patterns.
    """)

    with gr.Row():
        seed = gr.Slider(0, 999, value=42, label="Random Seed")
        samples = gr.Slider(50, 500, value=100, step=10, label="Number of Samples")

    run_btn = gr.Button("Run Logistic Regression", variant="primary")

    result = gr.Textbox(label="Model Output", lines=4)
    plot = gr.Plot(label="Data Visualization")

    run_btn.click(train_and_visualize, inputs=[seed, samples], outputs=[result, plot])

# For HF Spaces
if __name__ == "__main__":
    demo.launch()