update

app.py

@@ -7,11 +7,9 @@ import sklearn
 import joblib
 from PIL import Image
 import base64
-from transformers import pipeline
-import datetime
 from huggingface_hub import hf_hub_download
 
-REPO_ID = "AlbieCofie/predict-customer-churn"
+REPO_ID = "Abubakari/Sales_Prediction"
 
 num_imputer = joblib.load(
     hf_hub_download(repo_id=REPO_ID, filename="numerical_imputer.joblib")
@@ -40,7 +38,6 @@ st.write("<center><h1>Sales Prediction App</h1></center>", unsafe_allow_html=Tru
 col1, col2, col3 = st.columns([1, 3, 3])
 
 
-#st.image("https://www.example.com/logo.png", width=200)
 # Add a subtitle or description
 st.write("This app uses machine learning to predict sales based on certain input parameters. Simply enter the required information and click 'Predict' to get a sales prediction!")
 
@@ -53,28 +50,25 @@ st.subheader("Enter the details to predict sales")
 input_data = {}
 col1,col2 = st.columns(2)
 with col1:
-    input_data["gender"] = st.radio('Select your gender', ('male', 'female'))
-    input_data["SeniorCitizen"] = st.radio("Are you a Seniorcitizen; No=0 and Yes=1", ('0', '1'))
-    input_data["Partner"] = st.radio('Do you have Partner', ('Yes', 'No'))
-    input_data["Dependents"] = st.selectbox('Do you have any Dependents?', ('No', 'Yes'))
-    input_data["tenure"] = st.number_input('Lenght of tenure (no. of months with Telco)', min_value=0, max_value=90, value=1, step=1)
-    input_data["PhoneService"] = st.radio('Do you have PhoneService? ', ('No', 'Yes'))
-    input_data["MultipleLines"] = st.radio('Do you have MultipleLines', ('No', 'Yes'))
-    input_data["InternetService"] = st.radio('Do you have InternetService', ('DSL', 'Fiber optic', 'No'))
-    input_data["OnlineSecurity"] = st.radio('Do you have OnlineSecurity?', ('No', 'Yes'))         
+    input_data['store_nbr'] = st.slider("store_nbr",0,54)
+    input_data['products'] = st.selectbox("products", ['AUTOMOTIVE', 'CLEANING', 'BEAUTY', 'FOODS', 'STATIONERY',
+       'CELEBRATION', 'GROCERY', 'HARDWARE', 'HOME', 'LADIESWEAR',
+       'LAWN AND GARDEN', 'CLOTHING', 'LIQUOR,WINE,BEER', 'PET SUPPLIES'])
+    input_data['onpromotion'] =st.number_input("onpromotion",step=1)
+    input_data['state'] = st.selectbox("state", ['Pichincha', 'Cotopaxi', 'Chimborazo', 'Imbabura',
+       'Santo Domingo de los Tsachilas', 'Bolivar', 'Pastaza',
+       'Tungurahua', 'Guayas', 'Santa Elena', 'Los Rios', 'Azuay', 'Loja',
+       'El Oro', 'Esmeraldas', 'Manabi'])
+    input_data['store_type'] = st.selectbox("store_type",['D', 'C', 'B', 'E', 'A'])
+    input_data['cluster'] = st.number_input("cluster",step=1)
 
 with col2:
-    input_data["OnlineBackup"] = st.radio('Do you have OnlineBackup?', ('No', 'Yes'))
-    input_data["DeviceProtection"] = st.radio('Do you have DeviceProtection?', ('No', 'Yes'))
-    input_data["TechSupport"] = st.radio('Do you have TechSupport?', ('No', 'Yes'))
-    input_data["StreamingTV"] = st.radio('Do you have StreamingTV?', ('No', 'Yes'))
-    input_data["StreamingMovies"] = st.radio('Do you have StreamingMovies?', ('No', 'Yes'))
-    input_data["Contract"] = st.selectbox('which Contract do you use?', ('Month-to-month', 'One year', 'Two year'))
-    input_data["PaperlessBilling"] = st.radio('Do you prefer PaperlessBilling?', ('Yes', 'No'))
-    input_data["PaymentMethod"] = st.selectbox('Which PaymentMethod do you prefer?', ('Electronic check', 'Mailed check', 'Bank transfer (automatic)',
-                                        'Credit card (automatic)'))
-    input_data["MonthlyCharges"] = st.number_input("Enter monthly charges (the range should between 0-120)")
-    input_data["TotalCharges"] = st.number_input("Enter total charges (the range should between 0-10.000)")  
+    input_data['dcoilwtico'] = st.number_input("dcoilwtico",step=1)
+    input_data['year'] = st.number_input("year",step=1)
+    input_data['month'] = st.slider("month",1,12)
+    input_data['day'] = st.slider("day",1,31)
+    input_data['dayofweek'] = st.number_input("dayofweek,0=Sun and 6=Sat",step=1)
+    input_data['end_month'] = st.selectbox("end_month",['True','False'])
 
 
 # Define CSS style for the download button
@@ -95,8 +89,24 @@ predict_button_css = """
     </style>
 """
 
+download_button_css = """
+    <style>
+    .download-button {
+        background-color: #C4C4C4;
+        color: white;
+        padding: 0.75rem 2rem;
+        border-radius: 0.5rem;
+        border: none;
+        font-size: 1.1rem;
+        font-weight: bold;
+        text-align: center;
+        margin-top: 1rem;
+    }
+    </style>
+"""
+
 # Display the custom CSS
-st.markdown(predict_button_css, unsafe_allow_html=True)
+st.markdown(predict_button_css + download_button_css, unsafe_allow_html=True)
 
 
   # Create a button to make a prediction
@@ -129,10 +139,34 @@ if st.button("Predict", key="predict_button", help="Click to make a prediction."
 
 # Make a prediction
         prediction = dt_model.predict(final_df)[0]
-        prediction_label = "Beware!!! This customer is likely to Churn" if prediction.item() == "Yes" else "This customer is Not likely churn"
-        prediction_label
 
 
 # Display the prediction
-        st.write(f"The predicted sales are: {prediction_label}.")
-        st.table(input_df)
+        st.write(f"The predicted sales are: {prediction}.")
+        input_df.to_csv("data.csv", index=False)
+        st.table(input_df)
+
+# Define custom CSS
+css = """
+table {
+    background-color: #f2f2f2;
+    color: #333333;
+}
+"""
+
+# Set custom CSS
+st.write(f'<style>{css}</style>', unsafe_allow_html=True)
+
+
+# Add the download button
+def download_csv():
+    with open("data.csv", "r") as f:
+        csv = f.read()
+    b64 = base64.b64encode(csv.encode()).decode()
+    button = f'<button class="download-button"><a href="data:file/csv;base64,{b64}" download="data.csv">Download Data CSV</a></button>'
+    return button
+
+st.markdown(
+    f'<div style="text-align: center">{download_csv()}</div>',
+    unsafe_allow_html=True
+)

app_2.py

@@ -0,0 +1,138 @@
+import pandas as pd
+import streamlit as st
+import numpy as np
+from matplotlib import pyplot as plt
+import pickle
+import sklearn
+import joblib
+from PIL import Image
+import base64
+from transformers import pipeline
+import datetime
+from huggingface_hub import hf_hub_download
+
+REPO_ID = "AlbieCofie/predict-customer-churn"
+
+num_imputer = joblib.load(
+    hf_hub_download(repo_id=REPO_ID, filename="numerical_imputer.joblib")
+)
+
+cat_imputer = joblib.load(
+    hf_hub_download(repo_id=REPO_ID, filename="categorical_imputer.joblib")
+)
+
+encoder = joblib.load(
+    hf_hub_download(repo_id=REPO_ID, filename="encoder.joblib")
+)
+
+scaler = joblib.load(
+    hf_hub_download(repo_id=REPO_ID, filename="scaler.joblib")
+)
+
+dt_model = joblib.load(
+    hf_hub_download(repo_id=REPO_ID, filename="Final_model.joblib")
+)
+
+# Add a title and subtitle
+st.write("<center><h1>Sales Prediction App</h1></center>", unsafe_allow_html=True)
+
+# Set up the layout
+col1, col2, col3 = st.columns([1, 3, 3])
+
+
+#st.image("https://www.example.com/logo.png", width=200)
+# Add a subtitle or description
+st.write("This app uses machine learning to predict sales based on certain input parameters. Simply enter the required information and click 'Predict' to get a sales prediction!")
+
+st.subheader("Enter the details to predict sales")
+
+# Add some text
+#st.write("Enter some data for Prediction.")
+
+ # Create the input fields
+input_data = {}
+col1,col2 = st.columns(2)
+with col1:
+    input_data["gender"] = st.radio('Select your gender', ('male', 'female'))
+    input_data["SeniorCitizen"] = st.radio("Are you a Seniorcitizen; No=0 and Yes=1", ('0', '1'))
+    input_data["Partner"] = st.radio('Do you have Partner', ('Yes', 'No'))
+    input_data["Dependents"] = st.selectbox('Do you have any Dependents?', ('No', 'Yes'))
+    input_data["tenure"] = st.number_input('Lenght of tenure (no. of months with Telco)', min_value=0, max_value=90, value=1, step=1)
+    input_data["PhoneService"] = st.radio('Do you have PhoneService? ', ('No', 'Yes'))
+    input_data["MultipleLines"] = st.radio('Do you have MultipleLines', ('No', 'Yes'))
+    input_data["InternetService"] = st.radio('Do you have InternetService', ('DSL', 'Fiber optic', 'No'))
+    input_data["OnlineSecurity"] = st.radio('Do you have OnlineSecurity?', ('No', 'Yes'))         
+
+with col2:
+    input_data["OnlineBackup"] = st.radio('Do you have OnlineBackup?', ('No', 'Yes'))
+    input_data["DeviceProtection"] = st.radio('Do you have DeviceProtection?', ('No', 'Yes'))
+    input_data["TechSupport"] = st.radio('Do you have TechSupport?', ('No', 'Yes'))
+    input_data["StreamingTV"] = st.radio('Do you have StreamingTV?', ('No', 'Yes'))
+    input_data["StreamingMovies"] = st.radio('Do you have StreamingMovies?', ('No', 'Yes'))
+    input_data["Contract"] = st.selectbox('which Contract do you use?', ('Month-to-month', 'One year', 'Two year'))
+    input_data["PaperlessBilling"] = st.radio('Do you prefer PaperlessBilling?', ('Yes', 'No'))
+    input_data["PaymentMethod"] = st.selectbox('Which PaymentMethod do you prefer?', ('Electronic check', 'Mailed check', 'Bank transfer (automatic)',
+                                        'Credit card (automatic)'))
+    input_data["MonthlyCharges"] = st.number_input("Enter monthly charges (the range should between 0-120)")
+    input_data["TotalCharges"] = st.number_input("Enter total charges (the range should between 0-10.000)")  
+
+
+# Define CSS style for the download button
+# Define the custom CSS
+predict_button_css = """
+    <style>
+    .predict-button {
+        background-color: #C4C4C4;
+        color: gray;
+        padding: 0.75rem 2rem;
+        border-radius: 0.5rem;
+        border: none;
+        font-size: 1.1rem;
+        font-weight: bold;
+        text-align: center;
+        margin-top: 2rem;
+    }
+    </style>
+"""
+
+# Display the custom CSS
+st.markdown(predict_button_css, unsafe_allow_html=True)
+
+
+  # Create a button to make a prediction
+
+if st.button("Predict", key="predict_button", help="Click to make a prediction."):
+    # Convert the input data to a pandas DataFrame
+        input_df = pd.DataFrame([input_data])
+
+
+# Selecting categorical and numerical columns separately
+        cat_columns = [col for col in input_df.columns if input_df[col].dtype == 'object']
+        num_columns = [col for col in input_df.columns if input_df[col].dtype != 'object']
+
+
+# Apply the imputers
+        input_df_imputed_cat = cat_imputer.transform(input_df[cat_columns])
+        input_df_imputed_num = num_imputer.transform(input_df[num_columns])
+
+
+ # Encode the categorical columns
+        input_encoded_df = pd.DataFrame(encoder.transform(input_df_imputed_cat).toarray(),
+                                   columns=encoder.get_feature_names(cat_columns))
+
+# Scale the numerical columns
+        input_df_scaled = scaler.transform(input_df_imputed_num)
+        input_scaled_df = pd.DataFrame(input_df_scaled , columns = num_columns)
+
+#joining the cat encoded and num scaled
+        final_df = pd.concat([input_encoded_df, input_scaled_df], axis=1)
+
+# Make a prediction
+        prediction = dt_model.predict(final_df)[0]
+        prediction_label = "Beware!!! This customer is likely to Churn" if prediction.item() == "Yes" else "This customer is Not likely churn"
+        prediction_label
+
+
+# Display the prediction
+        st.write(f"The predicted sales are: {prediction_label}.")
+        st.table(input_df)