Delete streamlit_app.py

streamlit_app.py

@@ -1,217 +0,0 @@
-import streamlit as st
-import pandas as pd
-import seaborn as sns
-import matplotlib.pyplot as plt
-
-
-# Machine Learning Modeling
-from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
-from sklearn.compose import ColumnTransformer
-from sklearn.linear_model import LinearRegression
-from sklearn.model_selection import RandomizedSearchCV
-import xgboost as xgb
-from sklearn.metrics import mean_squared_error
-import joblib
-
-# Set the page layout to full width
-st.set_page_config(layout="wide")
-# Initialize df as None
-df = None
-
-st.sidebar.title("Favorita Stores")
-selected_option = st.sidebar.radio("Select to Proceed", ["Data Statistics", "Visuals", "Time Series Analysis", "Forecasting"])
-
-# Custom CSS styling for the title
-st.markdown(
-    """
-    <style>
-    .title-text {
-        font-size: 28px;
-        text-align: center;
-        background-color: #3498db;
-        color: white;
-        padding: 10px 0;
-        width: 100%;
-        position: sticky;
-        top: 0;
-        z-index: 1;
-    }
-    </style>
-    """,
-    unsafe_allow_html=True
-)
-
-# Streamlit App Title
-st.markdown('<p class="title-text">Machine Learning App for Sales Prediction</p>', unsafe_allow_html=True)
-
-# Function to load and process the data
-def load_and_process_data():
-    global df
-    # Allow the user to upload an Excel file
-    uploaded_file = st.file_uploader("Choose an Excel file", type=["xlsx"])
-    if uploaded_file is not None:
-        # Check if the file is an Excel file
-        if uploaded_file.type == 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet':
-            # Read the Excel file into a DataFrame
-            df = pd.read_excel(uploaded_file)
-            # Remove null values
-            df.dropna(inplace=True)
-            df = df.drop(columns='Unnamed: 0')
-        else:
-            st.write("Please upload a valid Excel file.")
-
-# Load and process the data
-load_and_process_data()
-
-if selected_option == "Data Statistics":
-    # Rest of the code for "Data Statistics" option using df
-    if df is not None:
-        number_sample = st.number_input("Enter sample size to display data", min_value=5, max_value=10, step=1, value=5)
-        displayed_data = df.head(number_sample)
-        st.write("Sample data", displayed_data)
-        st.write("Summary Statistics of float/Integer columns", df.describe())
-        object_columns = df.select_dtypes(include='object').columns.tolist()
-        selected_column = st.selectbox("Select column of Data Type Object to View Unique values", object_columns)
-        if selected_column:
-            unique_values = df[selected_column].unique()
-            st.write("Unique values are", unique_values)
-
-elif selected_option == "Visuals":
-    # Rest of the code for "Visuals" option using df
-    if df is not None:
-        object_columns = df.select_dtypes(include='object').columns.tolist()
-        selected_column = st.selectbox("Select column of Data Type Object for Visualization", object_columns)
-        if selected_column:
-            df['date'] = pd.to_datetime(df['date'])  # Convert to datetime if applicable
-            df_grouped = df.groupby(selected_column)['sales'].sum().head(10)
-            df_grouped = df_grouped.sort_values(ascending=False)
-            fig, ax = plt.subplots(figsize=(15, 6))
-            ax.bar(df_grouped.index, df_grouped.values)
-            ax.set_xlabel(selected_column)
-            ax.set_ylabel('Sales Count')
-            ax.set_title(f'Top 10 Sales Count for {selected_column}')
-            st.pyplot(fig)  # Pass the figure to st.pyplot()
-elif selected_option == "Time Series Analysis":
-    if df is not None:
-        # Choose date and sales columns
-        timeseriesdata = df[['sales', 'date']]
-        timeseriesdata.index = timeseriesdata['date']
-        # Make date the index
-        timeseriesdata = timeseriesdata.resample('D').sum()  # Resample to daily sales
-
-        # Resample the data based on user's choice
-        resample_method = st.selectbox("Select a resampling method", ['M', 'Q', 'Y'])
-        if resample_method:
-            resampled_data = timeseriesdata.resample(resample_method).sum()
-
-            # Plot the time series using Seaborn lineplot
-            plt.figure(figsize=(15, 6))
-            sns.lineplot(data=resampled_data)
-            plt.ylabel('Sales')
-            plt.title(f'Sales Time Series (Resampled by {resample_method})')
-            st.pyplot(plt.gcf())
-else:
-    st.write("Please enter these inputs to predict sales. Thank you!")
-    # Load the pre-trained model and preprocessor
-    model = joblib.load('./xgb_model.joblib')
-    preprocessor = joblib.load('./preprocessor.joblib') 
-
-    
-
-    # Create a layout with 2 columns for even distribution
-    col1, col2 = st.columns(2)  
-
-    # User Inputs - Number
-    with col1:
-        # Create a date input using st.date_input
-        date = st.date_input("Enter Date")      
-
-        # Convert the selected date to a string in the desired format (e.g., YYYY-MM-DD)
-        formatted_date = date.strftime('%Y-%m-%d')      
-
-    # User Inputs - Year
-    with col2:
-        family = st.selectbox("Select product family", ['CELEBRATION', 'CLEANING', 'DAIRY', 'DELI', 'EGGS', 'FROZEN FOODS',
-           'GROCERY I', 'GROCERY II', 'HARDWARE', 'HOME AND KITCHEN I',
-           'HOME AND KITCHEN II', 'HOME APPLIANCES', 'HOME CARE',
-           'LADIESWEAR', 'LAWN AND GARDEN', 'LINGERIE', 'LIQUOR,WINE,BEER',
-           'MAGAZINES', 'MEATS', 'PERSONAL CARE', 'PET SUPPLIES',
-           'PLAYERS AND ELECTRONICS', 'POULTRY', 'PREPARED FOODS', 'PRODUCE',
-           'SCHOOL AND OFFICE SUPPLIES', 'SEAFOOD', 'AUTOMOTIVE', 'BABY CARE',
-           'BEAUTY', 'BEVERAGES', 'BOOKS', 'BREAD/BAKERY']) 
-
-    # User Inputs - On Promotion
-    with col1:
-        onpromotion = st.number_input("Enter Number for onpromotion", min_value=0, step=1)  
-
-
-    # User Inputs - Day of the Week
-    with col2:
-        city = st.selectbox("Select city", ['Quito', 'Cayambe', 'Latacunga', 'Riobamba', 'Ibarra',
-           'Santo Domingo', 'Guaranda', 'Puyo', 'Ambato', 'Guayaquil',
-           'Salinas', 'Daule', 'Babahoyo', 'Quevedo', 'Playas', 'Libertad',
-           'Cuenca', 'Loja', 'Machala', 'Esmeraldas', 'Manta', 'El Carmen'])    
-
-    # User Inputs - Product Category
-    with col1:
-        oil_prices = st.number_input("Enter oil price", min_value=1, step=1)    
- 
-
-    # User Inputs - Day of the Week
-    with col2:
-        holiday_type = st.selectbox("Select holiday type", ['Holiday', 'Additional', 'Transfer', 'Event', 'Bridge'])    
-
-    # User Inputs - Product Category
-    with col1:
-        sales_lag_1 = st.number_input("Enter Number for sales lag", min_value=0, step=1)    
-
-
-    # User Inputs - Day of the Week
-    with col2:
-        moving_average = st.number_input("Enter Number for moving average", min_value=0, step=1)    
-
-    # Placeholder for Predicted Value   
-
-    # Add custom spacing between columns
-    st.markdown("<hr>", unsafe_allow_html=True) 
-
-
-
-    # Predict Button
-    if st.button("Predict"):
-        # Prepare input data for prediction
-        # Prepare input data for prediction
-        # Create a DataFrame with all required columns except "sales"
-        prediction_placeholder = st.empty()
-        input_df = pd.DataFrame({
-            "family": [family],
-            "onpromotion": [onpromotion],
-            "city": [city],
-            "oil_prices": [oil_prices],
-            "holiday_type": [holiday_type],
-            "sales_lag_1": [sales_lag_1],
-            "moving_average": [moving_average]
-        })
-
-        # Transform the input DataFrame using the preprocessor
-        preprocessed_data = preprocessor.transform(input_df)
-
-
-
-        # Make a prediction
-        prediction = model.predict(preprocessed_data)   
-
-         
-        # Display the prediction
-        prediction_placeholder.text(f"Predicted Value for sales: {prediction[0]: ,.2f}")  
-
-        if prediction >= 0:
-            prediction_placeholder.markdown(
-            f'Predicted Value for sales: <span style="background-color: green; padding: 2px 5px; border-radius: 5px;">${prediction[0]:,.2f}</span>',
-            unsafe_allow_html=True
-        )
-        else:
-            prediction_placeholder.markdown(
-            f'Predicted Value for sales: <span style="background-color: red; padding: 2px 5px; border-radius: 5px;">${prediction[0]:,.2f}</span>',
-            unsafe_allow_html=True
-        )