Update app.py

app.py

@@ -1,84 +1,61 @@
 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 os
 import math
-
-import matplotlib.ticker as ticker
-
 import torch
 from chronos import ChronosPipeline
 import warnings
+
+from seafoam import Seafoam
+
 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",
-        )
+import numpy as np
+import matplotlib.ticker as ticker
+
+os.makedirs("example_files", exist_ok=True)
+
+def process_csv(file):
+    if file is None:
+        return None, gr.Dropdown(choices=[])
 
-seafoam = Seafoam()
+    if not file.name.endswith('.csv'):
+        raise gr.Error("Please upload a CSV file only")
+    df = pd.read_csv(file.name)
+    columns = df.columns.tolist()
+    transformed_columns = list(map(lambda x: ' '.join([word.capitalize() for word in x.split('_')]), columns))
+    data_columns = gr.Dropdown(choices=transformed_columns, value=None)
+    return df, data_columns, data_columns
 
 
-def process_data(csv_file):
+def process_data(csv_file, date_column_value, target_column_value):
     try:
+        if not csv_file:
+            return "Error: Upload Csv File"
+
+        if not date_column_value or not target_column_value:
+            return "Error: Both date and target columns must be selected"
+
+        date_column = date_column_value.lower().replace(" ", "_")
+        target_column = target_column_value.lower().replace(" ", "_")
+
         # Read the CSV file
         df = pd.read_csv(csv_file.name)
 
-        df['date'] = pd.to_datetime(df['date'])
+        numeric_mask = df[date_column].apply(lambda x: isinstance(x, (int, float)))
+        if numeric_mask.any():
+            return "Error: Found numeric values in column '{date_column}'. Please provide dates in string format like 'YYYY-MM-DD'."
+
+        df['date'] = pd.to_datetime(df[date_column])
+
         df['month'] = df['date'].dt.month
         df['year'] = df['date'].dt.year
+        df['sold_qty'] = df[target_column]
 
         monthly_sales = df.groupby(['year', 'month'])['sold_qty'].sum().reset_index()
         monthly_sales = monthly_sales.rename(columns={'year': 'year', 'month': 'month', 'sold_qty': 'y'})
@@ -166,13 +143,45 @@ def process_data(csv_file):
         return None
 
 # Create Gradio interface
-with gr.Blocks(theme=seafoam) as demo:
+with gr.Blocks(theme=Seafoam()) as demo:
     gr.Markdown("# Chronos Forecasting - Tops infosolutions Pvt Ltd")
     gr.Markdown("Upload a CSV file and click 'Forecast' to generate sales forecast for next 12 months .")
 
+    df_state = gr.State()
+
     with gr.Row():
         file_input = gr.File(label="Upload CSV File", file_types=[".csv"])
 
+
+    with gr.Row():
+        date_column = gr.Dropdown(
+            choices=[],
+            label="Select Date column",
+            multiselect=False,
+            value=None
+        )
+
+        target_column = gr.Dropdown(
+            choices=[],
+            label="Select Target column",
+            multiselect=False,
+            value=None
+        )
+
+    gr.Examples(
+        examples=[
+            ["example_files/13dec_product_id96airaco.csv"],
+            ["example_files/13dec_product_id346airaco.csv"],
+            ["example_files/13dec_product_id567airaco.csv"],
+            ["example_files/13dec_product_id856airaco.csv"],
+            ["example_files/airaco_product_id215.csv"]
+        ],
+        inputs=file_input,
+        outputs=[df_state, date_column, target_column],
+        fn=process_csv,
+        cache_examples=True
+    )
+
     with gr.Row():
         visualize_btn = gr.Button("Forecast", variant="primary")
 
@@ -182,12 +191,35 @@ with gr.Blocks(theme=seafoam) as demo:
     with gr.Row():
         pivot_plot_output = gr.Plot(label="Monthly Sales Pivot Table")
 
+    file_input.upload(
+        process_csv,
+        inputs=[file_input],
+        outputs=[df_state, date_column, target_column]
+    )
+
+    # Column selection handler
+    date_column.change(
+        lambda x: x if x else "",
+        inputs=[date_column],
+        outputs=[]
+    )
+
+    target_column.change(
+        lambda x: x if x else "",
+        inputs=[target_column],
+        outputs=[]
+    )
+
     visualize_btn.click(
         fn=process_data,
-        inputs=[file_input],
+        inputs=[file_input, date_column, target_column],
         outputs=[plot_output, pivot_plot_output]
     )
 
+
+
+
+
 # Launch the app
 if __name__ == "__main__":
     demo.launch()