app.py

import pandas as pd
import matplotlib.pyplot as plt
from statsmodels.tsa.arima.model import ARIMA
import pickle
import gradio as gr

def load_model():
    try:
        with open('arima_sales_model.pkl', 'rb') as f:
            arima_model = pickle.load(f)
        return arima_model
    except Exception as e:
        return None, f"Failed to load model: {str(e)}"

def forecast_sales(uploaded_file, forecast_period=30):
    if uploaded_file is None:
        return "No file uploaded.", None

    try:
        df = pd.read_csv(uploaded_file)
    except Exception as e:
        return f"Failed to read the uploaded CSV file: {str(e)}", None

    if 'Date' not in df.columns or 'Sale' not in df.columns:
        return "The uploaded file must contain 'Date' and 'Sale' columns.", None

    try:
        df['Date'] = pd.to_datetime(df['Date'])
        df = df.rename(columns={'Date': 'ds', 'Sale': 'y'})

        arima_model, error = load_model()
        if arima_model is None:
            return error, None

        forecast = arima_model.get_forecast(steps=forecast_period)
        forecast_index = pd.date_range(df['ds'].max(), periods=forecast_period + 1, freq='D')[1:]
        forecast_df = pd.DataFrame({'Date': forecast_index, 'Sales Forecast': forecast.predicted_mean})
    except Exception as e:
        return f"Failed during forecasting: {str(e)}", None

    try:
        # Create the plot
        fig, ax = plt.subplots(figsize=(10, 6))
        ax.plot(df['ds'], df['y'], label='Historical Sales', color='blue')
        ax.plot(forecast_df['Date'], forecast_df['Sales Forecast'], label='Sales Forecast', color='red', linestyle='--')
        ax.set_xlabel('Date')
        ax.set_ylabel('Sales')
        ax.set_title('Sales Forecasting with ARIMA')
        ax.legend()
        return None, fig
    except Exception as e:
        return f"Failed to generate plot: {str(e)}", None

def setup_interface():
    with gr.Blocks() as demo:
        gr.Markdown("## MLCast v1.1 - Intelligent Sales Forecasting System")
        file_input = gr.File(label="Upload your store data here (must contain Date and Sales)")
        forecast_button = gr.Button("Forecast Sales")
        output_plot = gr.Plot()
        output_text = gr.Textbox()
        forecast_button.click(forecast_sales, inputs=[file_input], outputs=[output_text, output_plot])

    return demo

if __name__ == "__main__":
    interface = setup_interface()
    interface.launch()