init files

.streamlit/config

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+[theme]
+# Primary accent for interactive elements
+primaryColor = '#7792E3'
+
+# Background color for the main content area
+backgroundColor = '#FFFFFF'
+
+# Background color for sidebar and most interactive widgets
+secondaryBackgroundColor = '#B9F1C0'
+
+# Color used for almost all text
+textColor = '#000000'
+
+# Font family for all text in the app, except code blocks
+# Accepted values (serif | sans serif | monospace) 
+# Default: "sans serif"
+font = "sans serif"

app.py

@@ -1,3 +1,38 @@
 import streamlit as st
+import pandas as pd
+from modules import *
 
-st.write("Hello World")
+st.title("Sales Forecasting Dashboard")
+st.write("📈 Welcome User, start using the application by uploading your file in the sidebbar!")
+
+st.set_page_config(
+      page_title="Sales Forecasting System",
+      page_icon="📈",
+      layout="wide",
+      initial_sidebar_state="expanded",
+  )
+
+#   if 'uploaded' not in st.session_state:
+    # st.session_state.uploaded = 'uploaded'
+
+# Sidebar Menu
+with st.sidebar:
+    uploaded_file = st.file_uploader("Upload your Store Data here (must atleast contain Date and Sale)", type=["csv"])
+    err = 0
+    if uploaded_file is not None:
+      if uploaded_file.type != 'text/csv':
+            err = 1
+            st.info('Please upload in CSV format only...')
+      else: 
+        st.success("File uploaded successfully!")
+        df = pd.read_csv(uploaded_file, parse_dates=True)
+        st.write("Your uploaded data:")
+        st.write(df)
+        # Data pre-processing
+      #   df = preprocessor.drop(df)
+      #   df = preprocessor.date_format(df)
+      #   preprocessor.merge_sort(df)
+      #   df = preprocessor.group_to_three(df)
+      #   st.session_state.uploaded = True
+    with open('sample.csv', 'rb') as f:
+       st.download_button("Download our sample CSV", f, file_name='sample.csv')

modules/__init__.py

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+__all__ = ["preprocessor", "arima", "tapas"]

modules/arima.py

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+import numpy as np
+import pandas as pd
+from datetime import datetime
+import pmdarima as pm
+from pmdarima import auto_arima
+
+def train_test(dataframe, n):
+  training_y = dataframe.iloc[:-n,0]
+  test_y = dataframe.iloc[-n:,0]
+  test_y_series = pd.Series(test_y, index=dataframe.iloc[-n:, 0].index)
+  training_X = dataframe.iloc[:-n,1:]
+  test_X = dataframe.iloc[-n:,1:]
+  future_X = dataframe.iloc[0:,1:]
+  return (training_y, test_y, test_y_series, training_X, test_X, future_X)
+
+def model_fitting(dataframe, Exo):
+    futureModel = pm.auto_arima(dataframe['Sales'], X=Exo, start_p=1, start_q=1,
+                         test='adf',min_p=1,min_q=1,
+                         max_p=3, max_q=3, m=12,
+                         start_P=0, seasonal=True,
+                         d=None, D=1, trace=True,
+                         error_action='ignore',
+                         suppress_warnings=True,
+                         stepwise=True)
+    model = futureModel
+    return model
+
+def test_fitting(dataframe, Exo, trainY):
+    trainTestModel = auto_arima(X = Exo, y = trainY, start_p=1, start_q=1,
+                           test='adf',min_p=1,min_q=1,
+                           max_p=3, max_q=3, m=12,
+                           start_P=0, seasonal=True,
+                           d=None, D=1, trace=True,
+                           error_action='ignore',
+                           suppress_warnings=True,
+                           stepwise=True)
+    model = trainTestModel
+    return model
+
+def forecast_accuracy(forecast, actual):
+    mape = np.mean(np.abs(forecast - actual)/np.abs(actual)).round(4)  # MAPE
+    rmse = (np.mean((forecast - actual)**2)**.5).round(2)  # RMSE
+    corr = np.corrcoef(forecast, actual)[0,1]   # corr
+    mins = np.amin(np.hstack([forecast[:,None],
+                            actual[:,None]]), axis=1)
+    maxs = np.amax(np.hstack([forecast[:,None],
+                            actual[:,None]]), axis=1)
+    minmax = 1 - np.mean(mins/maxs)             # minmax
+    return({'mape':mape, 'rmse':rmse, 'corr':corr, 'min-max':minmax})
+
+def sales_growth(dataframe, fittedValues):
+    sales_growth = fittedValues.to_frame()
+    sales_growth = sales_growth.reset_index()
+    sales_growth.columns = ("Date", "Sales")
+    sales_growth = sales_growth.set_index('Date')
+
+    sales_growth['Sales'] = (sales_growth['Sales']).round(2)
+
+    #Calculate and create the column for sales difference and growth
+    sales_growth['Forecasted Sales First Difference']=(sales_growth['Sales']-sales_growth['Sales'].shift(1)).round(2)
+    sales_growth['Forecasted Sales Growth']=(((sales_growth['Sales']-sales_growth['Sales'].shift(1))/sales_growth['Sales'].shift(1))*100).round(2)
+
+    #Calculate and create the first row for sales difference and growth
+    sales_growth['Forecasted Sales First Difference'].iloc[0] = (dataframe['Sales'].iloc[-1]-dataframe['Sales'].iloc[-2]).round(2)
+    sales_growth['Forecasted Sales Growth'].iloc[0]=(((dataframe['Sales'].iloc[-1]-dataframe['Sales'].iloc[-2])/dataframe['Sales'].iloc[-1])*100).round(2)
+
+
+    return sales_growth

modules/preprocessor.py

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+import pandas as pd
+from datetime import datetime
+
+def merge(B, C, A):
+    i = j = k = 0
+
+    # Convert 'Date' columns to datetime.date objects
+    B['Date'] = pd.to_datetime(B['Date']).dt.date
+    C['Date'] = pd.to_datetime(C['Date']).dt.date
+    A['Date'] = pd.to_datetime(A['Date']).dt.date
+
+    while i < len(B) and j < len(C):
+      if B['Date'].iloc[i] <= C['Date'].iloc[j]:
+        A['Date'].iloc[k] = B['Date'].iloc[i]
+        A['Sales'].iloc[k] = B['Sales'].iloc[i]
+        i += 1
+        
+      else:
+        A['Date'].iloc[k] = C['Date'].iloc[j]
+        A['Sales'].iloc[k] = C['Sales'].iloc[j]
+        j += 1
+      k += 1
+
+    while i < len(B):
+      A['Date'].iloc[k] = B['Date'].iloc[i]
+      A['Sales'].iloc[k] = B['Sales'].iloc[i]
+      i += 1
+      k += 1
+
+    while j < len(C):
+      A['Date'].iloc[k] = C['Date'].iloc[j]
+      A['Sales'].iloc[k] = C['Sales'].iloc[j]
+      j += 1
+      k += 1
+
+    return A
+
+def merge_sort(dataframe):
+  if len(dataframe) > 1:
+      center = len(dataframe) // 2
+      left = dataframe.iloc[:center]
+      right = dataframe.iloc[center:]
+      merge_sort(left)
+      merge_sort(right)
+
+      return merge(left, right, dataframe)
+
+  else:
+     return dataframe
+
+def drop (dataframe):
+  def get_columns_containing(dataframe, substrings):
+    return [col for col in dataframe.columns if any(substring.lower() in col.lower() for substring in substrings)]
+
+  columns_to_keep = get_columns_containing(dataframe, ["date", "sale"])
+  dataframe = dataframe.drop(columns=dataframe.columns.difference(columns_to_keep))
+  dataframe = dataframe.dropna()
+      
+  return dataframe
+
+def date_format(dataframe):
+  for i, d, s in dataframe.itertuples():
+    dataframe['Date'][i] = dataframe['Date'][i].strip()
+
+  for i, d, s in dataframe.itertuples():
+    new_date = datetime.strptime(dataframe['Date'][i], "%m/%d/%Y").date()
+    dataframe['Date'][i] = new_date
+
+    return dataframe
+
+def group_to_three(dataframe):
+  dataframe['Date'] = pd.to_datetime(dataframe['Date'])
+  dataframe = dataframe.groupby([pd.Grouper(key='Date', freq='3D')])['Sales'].mean().round(2)
+  dataframe = dataframe.replace(0, pd.np.nan).dropna()
+
+  return dataframe

modules/tapas.py

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+import torch
+from transformers import pipeline, TapasTokenizer, TapasForQuestionAnswering
+
+model_name = "google/tapas-large-finetuned-wtq"
+
+# load the tokenizer and the model from huggingface model hub
+tokenizer = TapasTokenizer.from_pretrained(model_name)
+model = TapasForQuestionAnswering.from_pretrained(model_name, local_files_only=False)
+
+# load the model and tokenizer into a question-answering pipeline
+pipe = pipeline("table-question-answering", model=model, tokenizer=tokenizer)
+
+def get_answer(table, query):
+    answers = pipe(table=table, query=query)
+    print(answers['coordinates']) # FOR DEBUGGING PURPOSES
+    return answers
+
+def convert_answer(answer):
+    if answer['aggregator'] == 'SUM':
+      print(answer['answer']) # FOR DEBUGGING
+      cells = answer['cells']
+      converted = sum(float(value.replace(',', '')) for value in cells)
+      return converted
+
+    if answer['aggregator'] == 'AVERAGE':
+      print(answer['answer']) # FOR DEBUGGING
+      cells = answer['cells']
+      values = [float(value.replace(',', '')) for value in cells]
+      converted = sum(values) / len(values)
+      return converted
+
+    if answer['aggregator'] == 'COUNT':
+      print(answer['answer']) # FOR DEBUGGING
+      cells = answer['cells']
+      converted = sum(int(value.replace(',', '')) for value in cells)
+      return converted
+
+    else:
+      return answer
+
+def get_converted_answer(table, query):
+    converted_answer = convert_answer(get_answer(table, query))
+    return converted_answer

requirements.txt

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+pmdarima
+statsmodels
+transformers
+torch