Create app.py

app.py

@@ -0,0 +1,151 @@
+from __future__ import annotations
+from typing import Iterable
+import gradio as gr
+from gradio.themes.base import Base
+from gradio.themes.utils import colors, fonts, sizes
+import time
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+
+import torch
+from chronos import ChronosPipeline
+import warnings
+warnings.filterwarnings("ignore")
+
+class Seafoam(Base):
+    def __init__(
+        self,
+        *,
+        primary_hue: colors.Color | str = colors.emerald,
+        secondary_hue: colors.Color | str = colors.blue,
+        neutral_hue: colors.Color | str = colors.blue,
+        spacing_size: sizes.Size | str = sizes.spacing_md,
+        radius_size: sizes.Size | str = sizes.radius_md,
+        text_size: sizes.Size | str = sizes.text_lg,
+        font: fonts.Font
+        | str
+        | Iterable[fonts.Font | str] = (
+            fonts.GoogleFont("Quicksand"),
+            "ui-sans-serif",
+            "sans-serif",
+        ),
+        font_mono: fonts.Font
+        | str
+        | Iterable[fonts.Font | str] = (
+            fonts.GoogleFont("IBM Plex Mono"),
+            "ui-monospace",
+            "monospace",
+        ),
+    ):
+        super().__init__(
+            primary_hue=primary_hue,
+            secondary_hue=secondary_hue,
+            neutral_hue=neutral_hue,
+            spacing_size=spacing_size,
+            radius_size=radius_size,
+            text_size=text_size,
+            font=font,
+            font_mono=font_mono,
+        )
+        super().set(
+            body_background_fill="repeating-linear-gradient(45deg, *primary_200, *primary_200 10px, *primary_50 10px, *primary_50 20px)",
+            body_background_fill_dark="repeating-linear-gradient(45deg, *primary_800, *primary_800 10px, *primary_900 10px, *primary_900 20px)",
+            button_primary_background_fill="linear-gradient(90deg, *primary_300, *secondary_400)",
+            button_primary_background_fill_hover="linear-gradient(90deg, *primary_200, *secondary_300)",
+            button_primary_text_color="white",
+            button_primary_background_fill_dark="linear-gradient(90deg, *primary_600, *secondary_800)",
+            slider_color="*secondary_300",
+            slider_color_dark="*secondary_600",
+            block_title_text_weight="600",
+            block_border_width="3px",
+            block_shadow="*shadow_drop_lg",
+            button_primary_shadow="*shadow_drop_lg",
+            button_large_padding="32px",
+        )
+
+seafoam = Seafoam()
+
+
+import numpy as np
+import matplotlib.ticker as ticker
+
+def process_data(csv_file):
+    try:
+        # Read the CSV file
+        df = pd.read_csv(csv_file.name)
+
+        df['date'] = pd.to_datetime(df['date'])
+        df['month'] = df['date'].dt.month
+        df['year'] = df['date'].dt.year
+
+        monthly_sales = df.groupby(['year', 'month'])['sold_qty'].sum().reset_index()
+        monthly_sales = monthly_sales.rename(columns={'year': 'year', 'month': 'month', 'sold_qty': 'y'})
+
+        pipeline = ChronosPipeline.from_pretrained(
+            "amazon/chronos-t5-base",
+            device_map="cpu",
+            torch_dtype=torch.float32,
+            )
+        context = torch.tensor(monthly_sales["y"])
+        prediction_length = 12
+        forecast = pipeline.predict(context, prediction_length)
+
+        # Prepare forecast data
+        forecast_index = range(len(monthly_sales), len(monthly_sales) + prediction_length)
+        low, median, high = np.quantile(forecast[0].numpy(), [0.1, 0.5, 0.9], axis=0)
+
+        # Visualization
+        plt.figure(figsize=(30, 10))
+        plt.plot(monthly_sales["y"], color="royalblue", label="Historical Data", linewidth=2)
+        plt.plot(forecast_index, median, color="tomato", label="Median Forecast", linewidth=2)
+        plt.fill_between(forecast_index, low, high, color="tomato", alpha=0.3, label="80% Prediction Interval")
+        plt.title("Sales Forecasting Visualization", fontsize=16)
+        plt.xlabel("Months", fontsize=20)
+        plt.ylabel("Sold Qty", fontsize=20)
+
+        plt.xticks(fontsize=18)
+        plt.yticks(fontsize=18)
+
+        ax = plt.gca()
+        ax.xaxis.set_major_locator(ticker.MultipleLocator(3))
+        ax.yaxis.set_major_locator(ticker.MultipleLocator(5))
+        ax.grid(which='major', linestyle='--', linewidth=1.2, color='gray', alpha=0.7)
+
+        plt.legend(fontsize=18)
+        plt.grid(linestyle='--', linewidth=1.2, color='gray', alpha=0.7)
+        plt.tight_layout()
+
+        return plt.gcf()
+
+    except Exception as e:
+        print(f"Error: {str(e)}")
+        return None
+
+# Create Gradio interface
+with gr.Blocks(theme=seafoam) as demo:
+    gr.Markdown("# Chronos Forecasting - Tops Infosolution Pvt. Ltd")
+    gr.Markdown("Upload a CSV file and click 'Forecast' to generate sales forecast for next 12 months .")
+
+    with gr.Row():
+        file_input = gr.File(label="Upload CSV File", file_types=[".csv"])
+
+    with gr.Row():
+        visualize_btn = gr.Button("Forecast", variant="primary")
+
+    with gr.Row():
+        plot_output = gr.Plot(label="Chronos Forecasting Visualization")
+
+    with gr.Row():
+        plot_output = gr.Plot(label="Chronos Forecasting Visualization")
+
+    visualize_btn.click(
+        fn=process_data,
+        inputs=[file_input],
+        outputs=[plot_output]
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()