Create app.py

app.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import streamlit as st
+from PIL import Image
+import pandas as pd
+import numpy as np
+
+# Create two columns
+col1, col2 = st.columns([1, 3])  # Adjust the ratio as needed
+
+# Load and display the logo image in the first column
+with col1:
+    image_path = "Niq.png"  # Update this path if your image is in a different directory
+    st.image(image_path, width=150)  # Adjust the width as needed
+
+# Set the title of the app in the second column
+with col2:
+    st.title("Segmentation Tool")
+
+st.sidebar.title("Welcome to the Dollar General Segmentation Tool!")
+st.sidebar.info(
+    """
+    **Please follow the instructions below to contribute to our research:**
+
+    - On the right side, you will encounter a series of statements.
+    - **Carefully read each statement** and use the dropdowns and sliders to select the option that best describes your preferences or behaviors.
+    - Your thoughtful responses are crucial for the accuracy of our segmentation model.
+    - The information you provide will be used to enhance our understanding of different customer segments.
+
+    **Thank you for participating in our research. Your input is invaluable!**
+    """
+)
+
+
+st.markdown("<h2 style='color: black;'>Demographics</h2>", unsafe_allow_html=True)
+
+
+# In[ ]:
+
+
+# Add statement for Gender
+st.write("**Gender**")
+gender_display_options = ["Male", "Female", "Other", "Prefer not to disclose"]
+gender_encoding = {"Male": 1, "Female": 2, "Other": 3, "Prefer not to disclose": 4}
+selected_gender_display = st.selectbox("Select your gender:", gender_display_options)
+selected_gender_encoded = gender_encoding[selected_gender_display]
+
+# Add statement for Age
+st.write("**Age**")
+age_display_options = ["18-34", "35-44", "45-54", "55-64", "65 and above"]
+age_encoding = {"18-34": 3, "35-44": 4, "45-54": 5, "55-64": 6, "65 and above": 7}
+selected_age_display = st.selectbox("Select your age range:", age_display_options)
+selected_age_encoded = age_encoding[selected_age_display]
+
+
+
+# In[ ]:
+
+
+# Add a heading for Shopping Behaviour section with highlighted color
+st.markdown("<h2 style='color: black;'>Shopping Behaviour</h2>", unsafe_allow_html=True)
+
+# In[ ]:
+
+
+# First statement with dropdown options
+statement1 = "Which of the following best describes how well you know the prices of the household items you buy regularly?"
+statement1_options = [
+    "I know the prices of the household items I buy regularly and always notice when the prices change",
+    "I know the prices of some of the items I buy regularly and usually notice when the prices change",
+    "I generally know about how much I pay for things, but I don’t pay much attention to how much the products I buy cost or when prices change",
+    "Convenience is more important to me than lower prices"
+]
+statement1_encoding = {
+    "I know the prices of the household items I buy regularly and always notice when the prices change": 1,
+    "I know the prices of some of the items I buy regularly and usually notice when the prices change": 2,
+    "I generally know about how much I pay for things, but I don’t pay much attention to how much the products I buy cost or when prices change": 3,
+    "Convenience is more important to me than lower prices": 4
+}
+selected_statement1_display = st.selectbox(f"**{statement1}**", statement1_options)
+
+
+# Save the encoding for the selected statement1 option
+selected_statement1_encoded = statement1_encoding[selected_statement1_display]
+
+
+# In[ ]:
+
+
+# Second statement with dropdown options
+statement2 = "How much did you spend when visiting any Dollar General store in the past month in total?"
+statement2_options = ["$10 or less", "$11-$30", "$31-$70", "$71-$200", "Over $200","I have not shopped in the past month"]
+statement2_encoding = {
+    "$10 or less": 1,
+    "$11-$30": 2,
+    "$31-$70": 3,
+    "$71-$200": 4,
+    "Over $200": 5,
+    "I have not shopped in the past month":1
+}
+selected_statement2_display = st.selectbox(f"**{statement2}**", statement2_options)
+
+# Save the encoding for the selected statement2 option
+selected_statement2_encoded = statement2_encoding[selected_statement2_display]
+
+
+# In[ ]:
+
+
+#Third statement with dropdown options
+statement3 = "On a typical shopping trip to Dollar General, how many items do you purchase?"
+statement3_options = ["1-2 items", "3-4 items", "5-6 items", "7-8 items", "More than 8 items"]
+statement3_encoding = {
+   "1-2 items": 1,
+   "3-4 items": 2,
+   "5-6 items": 3,
+   "7-8 items": 4,
+   "More than 8 items": 5
+}
+selected_statement3_display = st.selectbox(f"**{statement3}**", statement3_options)
+# Save the encoding for the selected statement3 option
+selected_statement3_encoded = statement3_encoding[selected_statement3_display]
+
+
+# In[ ]:
+
+
+#Fourth statement with dropdown options
+statement4 = "How often do you go shopping at any Dollar General?"
+statement4_options = ["1-2 times a year", "3-5 times a year", "6-11 times a year", "Once a month", "2-3 times a month", "4 or more times a month"]
+statement4_encoding = {
+   "1-2 times a year": 1,
+   "3-5 times a year": 2,
+   "6-11 times a year": 3,
+   "Once a month": 4,
+   "2-3 times a month": 5,
+   "4 or more times a month": 6
+}
+selected_statement4_display = st.selectbox(f"**{statement4}**", statement4_options)
+
+# Save the encoding for the selected statement4 option
+selected_statement4_encoded = statement4_encoding[selected_statement4_display]
+
+# Add a heading for Shopping Habit section with highlighted color
+st.markdown("<h2 style='color: black;'>Shopping Habit</h2>", unsafe_allow_html=True)
+st.write("**If you were to shop for household items, how would you shop? Please select where on the scale you feel best describes you.**")
+# Create sliders with descriptive statements
+sliders = [
+    ("I always buy well-known brands", "I don’t care much about brands"),
+    ("Promotions / sales rarely change my brand choices", "I buy different brands because of promotions / sales"),
+    ("Often, I am stressed while shopping", "I find shopping enjoyable"),
+    ("I feel shopping is fun" , "I feel shopping is a tedious task"),
+    ("I like to take my time and browse when shopping", "I don’t like spending unnecessary time when shopping"),
+    ("I use apps while shopping", "I do not use apps while shopping"),
+    ("I end up purchasing a lot of things that I didn’t intend to", "I am very disciplined when I shop and only get what I intended to buy"),
+    ("I know prices of household items very well", "I do not pay attention to the price of household items"),
+    ("I know exactly what items to buy before I get to the store", "I tend to make most of my shopping decisions when I’m in the store")
+]
+
+#slider_responses = {}
+#for idx, (left_text, right_text) in enumerate(sliders):
+#    cols = st.columns([1, 2, 1])  # Define columns with the desired width ratio
+#    with cols[0]:
+#        st.write(left_text)  # Right-side statement
+#    with cols[1]:
+#        slider_key = f"slider_{idx}" 
+#        slider_responses[(left_text, right_text)] = st.slider(
+#            "",
+#            min_value=1,
+#            max_value=5,
+#            value=3,
+#            format="%d",
+#            key=slider_key 
+#        )
+#    with cols[2]:
+#        st.write(right_text)  # Left-side statement
+        
+#import streamlit as st
+
+# Custom function to display a slider without showing its value
+def slider_without_value(label, min_value, max_value, value, key):
+    # Create a slider and capture its value
+    selected_value = st.slider(label, min_value, max_value, value, format="", key=key)
+    # Return the selected value without displaying it
+    return selected_value
+
+
+
+slider_responses = {}
+for idx, (left_text, right_text) in enumerate(sliders):
+    cols = st.columns([1, 2, 1])  # Define columns with the desired width ratio
+    with cols[0]:
+        st.write(left_text)  # Left-side statement
+    with cols[1]:
+        slider_key = f"slider_{idx}"
+        slider_responses[(left_text, right_text)] = slider_without_value(
+            "", 1, 5, 3, key=slider_key
+        )
+    with cols[2]:
+        st.write(right_text)  # Right-side statement
+
+
+# Collect responses for each statement
+responses = {
+    "SC2": selected_gender_encoded,
+    "SC3a": selected_age_encoded,
+    "PR2a": selected_statement1_encoded,
+    "SH1": slider_responses[("I always buy well-known brands", "I don’t care much about brands")],
+    "SH2": slider_responses[("Promotions / sales rarely change my brand choices", "I buy different brands because of promotions / sales")],
+    "SH3": slider_responses[("Often, I am stressed while shopping", "I find shopping enjoyable")],
+    "SH4":slider_responses[("I feel shopping is fun" , "I feel shopping is a tedious task")],
+    "SH5": slider_responses[("I like to take my time and browse when shopping", "I don’t like spending unnecessary time when shopping")],
+    "SH6": slider_responses[("I use apps while shopping", "I do not use apps while shopping")],
+    "SH7": slider_responses[("I end up purchasing a lot of things that I didn’t intend to", "I am very disciplined when I shop and only get what I intended to buy")],
+    "SH8": slider_responses[("I know prices of household items very well", "I do not pay attention to the price of household items")],
+    "SH9": slider_responses[("I know exactly what items to buy before I get to the store", "I tend to make most of my shopping decisions when I’m in the store")],
+        "Q21": selected_statement2_encoded,
+    "Q25": selected_statement3_encoded,
+    "Q26": selected_statement4_encoded
+}
+
+
+
+# Load the saved model
+import pickle
+
+model_path = 'Trained_model.pickle'
+with open(model_path, 'rb') as model_file:
+    model = pickle.load(model_file)
+label_mapping = {
+    1: "Stacey",
+    2: "Dana",
+    3: "Marge",
+    4: "Carl",
+    5: "Ivy",
+    6: "Sue",
+    7: "Cora",
+    8: "Strangers"
+}
+df=pd.DataFrame([responses])
+st.write(df)
+# Make prediction
+if st.button('Submit'):
+    prediction_numeric = model.predict(df)[0]
+    prediction_numeric=prediction_numeric+1
+
+    # Convert numpy array to int if it's a single value array
+    if isinstance(prediction_numeric, np.ndarray) and prediction_numeric.size == 1:
+        prediction_numeric = int(prediction_numeric)
+    predicted_label = label_mapping.get(prediction_numeric, "Unknown")
+
+    # Assuming 'predicted_label' is defined and holds the prediction result
+
+    # Create two columns
+    col1, col2 = st.columns(2)
+
+    # Use the first column to display the statement with a border
+    with col1:
+        st.markdown("<div style='border: 2px solid #f0f2f6; padding: 4px; border-radius: 5px; margin: 10px 0;'><strong>Assigned Statement:</strong></div>", unsafe_allow_html=True)
+
+    # Use the second column to display the label aligned to the right with a border
+    with col2:
+        st.markdown(f"<div style='text-align: right; padding-right: 16px; border: 2px solid #f0f2f6; padding: 4px; border-radius: 5px; margin: 10px 0;'><strong>{predicted_label}</strong></div>", unsafe_allow_html=True)
+
+
+    # Add prediction to the DataFrame
+    df['Assgined_Segment'] = predicted_label