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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.utils import get_column_letter
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
def changewidth(worksheet):
column_widths = {}
# Calculate the maximum width for each column
for column in worksheet.columns:
column_letter = get_column_letter(column[0].column)
max_length = 0
for cell in column:
try:
if len(str(cell.value)) > max_length:
max_length = len(str(cell.value))
except TypeError:
pass
column_widths[column_letter] = max_length
# Set the column widths based on the maximum width for each column
for column_letter, width in column_widths.items():
column_width = (width + 2) * 1.2 # Adjust the width by adding some padding
worksheet.column_dimensions[column_letter].width = column_width
return worksheet
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)
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')
worksheet = changewidth(worksheet)
# 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="Input file")],
outputs=[gr.File(label="Output file"),gr.Textbox(label='Log',interactive=False)],
debug=True)
iface.launch() |