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app.py

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+import pandas as pd
+import gradio as gr
+import matplotlib.pyplot as plt
+from statsmodels.tsa.arima.model import ARIMA
+
+# Load dataset
+df = pd.read_csv("train.csv")
+df["date"] = pd.to_datetime(df["date"])
+
+stores = sorted(df["store"].unique())
+items = sorted(df["item"].unique())
+
+def forecast_single(store_id, item_id, days):
+    data = df[(df["store"] == store_id) & (df["item"] == item_id)]
+    data = data[["date", "sales"]].sort_values("date")
+
+    if len(data) < 50:
+        return None, None, "Not enough data for forecasting"
+
+    ts = data.set_index("date")["sales"]
+
+    model = ARIMA(ts, order=(1, 1, 1))
+    model_fit = model.fit()
+    forecast = model_fit.forecast(steps=days)
+
+    forecast_df = forecast.reset_index()
+    forecast_df.columns = ["Date", "Forecasted_Sales"]
+
+    fig, ax = plt.subplots()
+    ax.plot(ts, label="Historical Sales")
+    ax.plot(forecast, label="Forecast")
+    ax.set_title(f"Store {store_id} - Item {item_id}")
+    ax.legend()
+
+    return forecast_df, fig, forecast_df
+
+def forecast_all_stores(item_id, days):
+    results = []
+
+    for store_id in stores:
+        data = df[(df["store"] == store_id) & (df["item"] == item_id)]
+        data = data[["date", "sales"]].sort_values("date")
+
+        if len(data) < 50:
+            continue
+
+        ts = data.set_index("date")["sales"]
+        model = ARIMA(ts, order=(1, 1, 1))
+        model_fit = model.fit()
+        forecast = model_fit.forecast(steps=days)
+
+        forecast_df = forecast.reset_index()
+        forecast_df.columns = ["Date", "Forecasted_Sales"]
+        forecast_df["Store"] = store_id
+        results.append(forecast_df)
+
+    final_df = pd.concat(results)
+    return final_df, final_df
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Demand Forecasting Tool")
+
+    gr.Markdown("## Forecast for a Single Store and Item")
+    store = gr.Dropdown(stores, label="Select Store", value=1)
+    item = gr.Dropdown(items, label="Select Item", value=1)
+    days = gr.Slider(7, 90, value=30, label="Forecast Days")
+
+    run_single = gr.Button("Generate Forecast")
+
+    table_single = gr.Dataframe()
+    plot_single = gr.Plot()
+    download_single = gr.File()
+
+    run_single.click(
+        forecast_single,
+        inputs=[store, item, days],
+        outputs=[table_single, plot_single, download_single]
+    )
+
+    gr.Markdown("## Forecast One Item Across All Stores")
+    item_all = gr.Dropdown(items, label="Select Item")
+    days_all = gr.Slider(7, 90, value=30, label="Forecast Days")
+    run_all = gr.Button("Forecast All Stores")
+
+    table_all = gr.Dataframe()
+    download_all = gr.File()
+
+    run_all.click(
+        forecast_all_stores,
+        inputs=[item_all, days_all],
+        outputs=[table_all, download_all]
+    )
+
+demo.launch()

requirements.txt

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+pandas
+gradio
+statsmodels
+matplotlib