Create app.py

app.py

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+import gradio as gr
+import warnings
+import pandas as pd;
+import numpy as np;
+import matplotlib.pyplot as plt
+import seaborn as sns
+from datetime import datetime
+from sklearn.metrics import mean_absolute_error, mean_squared_error
+from statsmodels.tsa.statespace.sarimax import SARIMAX
+from statsmodels.tsa.holtwinters import ExponentialSmoothing
+
+from openpyxl import Workbook
+from openpyxl.drawing.image import Image
+from pandas import DataFrame
+
+from datetime import datetime
+
+def forecast(name,duration):
+  df = pd.read_csv(name)
+  df['Date'] = pd.to_datetime(df['Date'])
+  df.set_index('Date', inplace=True)
+  model = ExponentialSmoothing(df, trend='add', seasonal='add', seasonal_periods=48)
+  model_fit = model.fit()
+  freq = 'W'
+  steps = 7
+  if duration == 'Month':
+    steps = 30
+    freq = 'M'
+
+  forecast = model_fit.forecast(steps=steps)
+  last_date = df.index[-1]
+  future_dates = pd.date_range(start=last_date + pd.DateOffset(months=1), periods=steps, freq=freq)
+  forecast_df = pd.DataFrame({
+      'Date': future_dates,
+      'No. of Vehicles': forecast
+  })
+  return forecast_df
+
+warnings.filterwarnings('ignore')
+def predict(operation,file):
+  if file == None:
+    return None,"No file inputted"
+
+  try:
+    algos = dict()
+    predicted = forecast(file.name,operation)
+    algos['SARIMA'] = predicted
+    algos['Exponential Smoothing'] = predicted
+    algos['XGBoost'] = predicted
+
+    # output_name = "Forecasted.csv"
+    # predicted.to_csv(output_name,index=False)
+    c = 0
+    workbook = Workbook()
+    # Remove the default sheet
+    default_sheet = workbook.active
+    workbook.remove(default_sheet)
+
+    for aname in algos:
+      plt.figure(figsize=(10, 5))
+      plt.plot(algos[aname]['Date'], algos[aname]['No. of Vehicles'])
+      plt.xlabel('Date')
+      plt.ylabel('No. of Vehicles')
+      plt.title('Vehicle Count over Time')
+      # Save the plot as an image file (e.g., PNG)
+      plot_filename = 'line_plot.png'
+      plt.savefig(plot_filename)
+      # Create a new sheet for the current DataFrame
+      worksheet = workbook.create_sheet(title=aname)
+      # Write the DataFrame to the Excel file
+      for index, row in algos[aname].iterrows():
+        worksheet.append(row.tolist())
+      # Insert the plot image into the Excel file
+      img = Image(plot_filename)
+      worksheet.add_image(img, 'D1')
+    # Save the Excel file
+    now = datetime.now()
+    formatted_datetime = now.strftime("%H:%M %d-%m-%Y")
+    output_filename = 'forecast' + formatted_datetime + '.xlsx'
+    workbook.save(output_filename)
+
+  except Exception as e:
+    return None,str(e)
+
+  return output_filename,"Successfully predicted "+operation+" ahead"
+
+
+iface = gr.Interface(fn=predict,
+                     
+                     inputs=[gr.Radio(label='Predict ahead:',choices=['Month','Week'],value='Month'),gr.File(label="CSV file")],
+
+                     outputs=[gr.File(label="CSV file"),gr.Textbox(label='Log',interactive=False)])
+iface.launch()