app.py

import gradio as gr
import pandas as pd
from prophet import Prophet
import matplotlib.pyplot as plt

def forecast_energy(file):
    # Read CSV
    df = pd.read_csv(file)
    
    # Rename columns to fit Prophet's expected format
    df.columns = ['ds', 'y']
    
    # Convert date column to datetime
    df['ds'] = pd.to_datetime(df['ds'])
    
    # Build and train model
    model = Prophet()
    model.fit(df)
    
    # Forecast next 14 days
    future = model.make_future_dataframe(periods=14)
    forecast = model.predict(future)
    
    # Plot forecast
    fig = model.plot(forecast)
    plt.title("Energy Load Forecast")
    
    return fig

# Gradio interface
demo = gr.Interface(
    fn=forecast_energy,
    inputs=gr.File(label="Upload Energy Load CSV", file_types=[".csv"]),
    outputs=gr.Plot(label="Forecasted Load"),
    title="Smart Energy Load Forecasting",
    description="Upload a CSV file with columns 'ds' (date) and 'y' (energy load). This app predicts the next 2 weeks of load."
)

if __name__ == "__main__":
    demo.launch()