app.py

import gradio as gr
import pandas as pd
import plotly.graph_objects as go

# The URL from your original script
CSV_URL = "https://gardenstatemls.stats.showingtime.com/infoserv/s-v1/kpou-Asg"

def plot_csv():
    """
    Reads data from the specified URL, processes it, and creates a plot.

    This function skips the first 9 rows of the CSV, which contain metadata,
    and removes any empty columns before plotting the data.

    Returns:
        A Plotly figure object showing the median sales price over time.
    """
    try:
        # Read the CSV from the URL, skipping the metadata at the top
        df = pd.read_csv(CSV_URL, skiprows=9)

        # Clean up the DataFrame
        if 'Unnamed: 2' in df.columns:
            df = df.drop(columns=['Unnamed: 2'])

        # Rename columns for easier use
        df.columns = ['Date', 'Median Sales Price']

        # Create a Plotly figure
        fig = go.Figure()

        # Add the data series to the plot
        fig.add_trace(go.Scatter(x=df['Date'], y=df['Median Sales Price'], mode='lines', name='Median Sales Price'))

        # Update the plot layout for a professional look
        fig.update_layout(
            title="Median Sales Price - Entire MLS",
            xaxis_title="Date",
            yaxis_title="Median Sales Price ($)",
            showlegend=True
        )

        return fig

    except Exception as e:
        # If there's an error fetching or plotting the data, return the error message
        return str(e)


# Set up the Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("## Median Sales Price Plotter")
    plot_output = gr.Plot()
    run_button = gr.Button("Plot Data")

    # Link the button to the plotting function
    run_button.click(plot_csv, inputs=None, outputs=plot_output)

# Launch the Gradio app
demo.launch(debug=True)