Upload seer.py

seer.py

@@ -0,0 +1,138 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+from PIL import Image
+import requests
+from bokeh.plotting import figure
+from bokeh.models import HoverTool
+import joblib
+import os
+from date_features import getDateFeatures
+
+
+# Get the current directory path
+current_dir = os.path.dirname(os.path.abspath(__file__))
+
+# Load the model from the pickle file
+model_path = os.path.join(current_dir, 'model.pkl')
+model = joblib.load(model_path)
+
+# Load the scaler from the pickle file
+encoder_path = os.path.join(current_dir, 'encoder.pkl')
+encoder = joblib.load(encoder_path)
+
+
+# Set Page Configurations
+st.set_page_config(page_title="ETA Prediction App", page_icon="fas fa-chart-line", layout="wide", initial_sidebar_state="auto")
+
+# Loading GIF
+gif_url = "https://raw.githubusercontent.com/Gilbert-B/Forecasting-Sales/main/app/salesgif.gif"
+
+# Set up sidebar
+st.sidebar.header('Navigation')
+menu = ['Home', 'About']
+choice = st.sidebar.selectbox("Select an option", menu)
+
+def predict(sales_data):
+    sales_data = getDateFeatures(sales_data).set_index('date')
+    # print(sales_data.columns)
+
+    # Make predictions for the next 8 weeks
+    prediction_inputs = []  # Initialize the list for prediction inputs
+
+    # Encode the prediction inputs
+    # numeric_columns = sales_data.select_dtypes(include=['int64', 'float64']).columns.tolist()
+    numeric_columns = ['onpromotion', 'year', 'month', 'dayofmonth', 'dayofweek', 'dayofyear', 'weekofyear', 'quarter', 'year_weekofyear', 'sin(dayofyear)', 'cos(dayofyear)']
+    categoric_columns = ['store_id','category_id','city','store_type','cluster','holiday_type','is_holiday','is_month_start','is_month_end','is_quarter_start','is_quarter_end','is_year_start','is_year_end','is_weekend', 'season'] 
+    print(categoric_columns)
+    # encoder = BinaryEncoder(drop_invariant=False, return_df=True,)
+    # encoder.fit(sales_data[categoric_columns]) 
+    num = sales_data[numeric_columns]
+    encoded_cat = encoder.transform(sales_data[categoric_columns])
+    sales_data = pd.concat([num, encoded_cat], axis=1)
+    
+    # Make the prediction using the loaded machine learning model
+    predicted_sales = model.predict(sales_data)
+
+    return predicted_sales
+
+# Home section
+if choice == 'Home':
+    st.image(gif_url, use_column_width=True)
+    st.markdown("<h1 style='text-align: center;'>Welcome</h1>", unsafe_allow_html=True)
+    st.markdown("<p style='text-align: center;'>This is a Sales Forecasting App.</p>", unsafe_allow_html=True)
+    
+    # Set Page Title
+    st.title('SEER- A Sales Forecasting APP')
+    st.markdown('Enter the required information to forecast sales:')
+
+    
+    # Input form
+    col1, col2 = st.columns(2)
+
+    Stores = ['Store_' + str(i) for i in range(1, 55)]
+    Stores1 = ['Store_' + str(i) for i in range(0, 5)]
+    cities = ['city_' + str(i) for i in range(22)]
+    clusters = ['cluster_' + str(i) for i in range(17)]
+    categories = ['Category_' + str(i) for i in range(33)]
+
+    with col1:
+        date = st.date_input("Date")
+        # Convert the date to datetime format
+        date = pd.to_datetime(date)
+        onpromotion = st.number_input("How many products are on promotion?", min_value=0, step=1)
+        selected_category = st.selectbox("Category", categories)
+
+
+    with col2:
+        selected_store = st.selectbox("Store_type", Stores)
+        selected_store1 = st.selectbox("Store_id", Stores1)
+        selected_city = st.selectbox("City", cities)
+        selected_cluster = st.selectbox("Cluster", clusters)
+
+    # Call getDateFeatures() function on sales_data (replace sales_data with your DataFrame)
+    sales_data = pd.DataFrame({
+        'date': [date],
+        'store_id': [selected_store],
+        'category_id': [selected_category],
+        'onpromotion': [onpromotion],
+        'city' :[selected_city],
+        'store_type': [selected_store1],
+        'cluster':[selected_cluster]
+        })
+    print(sales_data)
+    print(sales_data.info())
+
+
+    if st.button('Predict'):
+        with st.spinner('Predicting sales...'):
+            sales = predict(sales_data)
+            formatted_sales = round(sales[0], 2)
+            st.success(f"Total sales for this week is: #{formatted_sales}")
+
+# About section
+elif choice == 'About':
+    # Load the banner image
+    banner_image_url = "https://raw.githubusercontent.com/Gilbert-B/Forecasting-Sales/0d7b869515bdf5551672f71b6e1f62be9902e3dc/app/seer.png"
+    banner_image = Image.open(requests.get(banner_image_url, stream=True).raw)
+ 
+   # Display the banner image
+    st.image(banner_image, use_column_width=True)
+    st.markdown('''
+            <p style='font-size: 20px; font-style: italic;font-style: bold;'>
+            SEER is a powerful tool designed to assist businesses in making accurate 
+            and data-driven sales predictions. By leveraging advanced algorithms and 
+            machine learning techniques, our app provides businesses with valuable insights 
+            into future sales trends. With just a few input parameters, such as distance and 
+            average speed, our app generates reliable sales forecasts, enabling businesses
+            to optimize their inventory management, production planning, and resource allocation. 
+            The user-friendly interface and intuitive design make it easy for users to navigate 
+            and obtain actionable predictions. With our Sales Forecasting App, 
+            businesses can make informed decisions, mitigate risks, 
+            and maximize their revenue potential in an ever-changing market landscape.
+            </p>
+            ''', unsafe_allow_html=True)
+    st.markdown("<p style='text-align: center;'>This Sales Forecasting App is developed using Streamlit and Python.</p>", unsafe_allow_html=True)
+    st.markdown("<p style='text-align: center;'>It demonstrates how machine learning can be used to predict sales for the next 8 weeks based on historical data.</p>", unsafe_allow_html=True)
+
+