change name

app.py

@@ -1,139 +1,71 @@
+import joblib
 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"
-access_token = st.secrets["HF_TOKEN"]
-
-cat_imputer = joblib.load(
-    hf_hub_download(repo_id=REPO_ID, filename="categorical_imputer.joblib", token=access_token, repo_type="model")
-)
-
-encoder = joblib.load(
-    hf_hub_download(repo_id=REPO_ID, filename="encoder.joblib", token=access_token, repo_type="model")
-)
-
-num_imputer = joblib.load(
-    hf_hub_download(repo_id=REPO_ID, filename="numerical_imputer.joblib", token=access_token, repo_type="model")
-)
+REPO_ID = "chanyaphas/creditc"
 
-scaler = joblib.load(
-    hf_hub_download(repo_id=REPO_ID, filename="scaler.joblib", token=access_token, repo_type="model")
-)
+access_token = st.secrets["HF_TOKEN"]
 
 model = joblib.load(
-    hf_hub_download(repo_id=REPO_ID, filename="Final_model.joblib", token=access_token, repo_type="model")
+    hf_hub_download(repo_id=REPO_ID, filename='model.joblib', token=access_token, repo_type="space")
 )
 
-# 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 = 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
-
+unique_values = joblib.load(
+    hf_hub_download(repo_id=REPO_ID, filename='unique_values.joblib', token=access_token, repo_type="space")
+)
 
-# Display the prediction
-        st.write(f"The predicted sales are: {prediction_label}.")
-        st.table(input_df)
+EDU_DICT = {'Lower secondary': 1,
+            'Secondary / secondary special': 2,
+            'Academic degree': 3,
+            'Incomplete higher': 4,
+            'Higher education' : 5
+            }
+
+def main():
+    st.title("Credit Card Approval Prediction")
+
+    with st.form("questionaire"):
+
+        Gender = st.selectbox('Gender', unique_values['CODE_GENDER'])
+        Own_car = st.selectbox('Own_car', unique_values['FLAG_OWN_CAR'])
+        Property = st.selectbox('Property', unique_values['FLAG_OWN_REALTY'])
+        Income_type = st.selectbox('Income_type', unique_values['NAME_INCOME_TYPE'])
+        Marital_status = st.selectbox('Marital_status', unique_values['NAME_FAMILY_STATUS'])
+        Housing_type = st.selectbox('Housing_type', unique_values['NAME_HOUSING_TYPE'])
+        Education = st.selectbox('Education', unique_values['NAME_EDUCATION_TYPE'])
+
+        Income = st.slider('Income', min_value=27000, max_value=1575000)
+        Children = st.slider('Children', min_value=0, max_value=19)
+        Day_Employed = st.slider('Day_Employed', min_value=0, max_value=3)
+        Flag_Mobile = st.slider('Flag_Mobile', min_value=0, max_value=1)
+        Flag_work_phone = st.slider('Flag_work_phone', min_value=0, max_value=1)
+        Flag_Phone = st.slider('Flag_Phone', min_value=0, max_value=1)
+        Flag_Email = st.slider('Flag_Email', min_value=0, max_value=1)
+        Family_mem = st.slider('Family_mem', min_value=1, max_value=20)
+
+        clicked = st.form_submit_button("Result")
+        if clicked:
+            result = model.predict(pd.DataFrame({
+                                                "CODE_GENDER": [Gender],
+                                                "FLAG_OWN_CAR": [Own_car],
+                                                "FLAG_OWN_REALTY": [Property],
+                                                "CNT_CHILDREN": [Children],
+                                                "AMT_INCOME_TOTAL": [Income],
+                                                "NAME_INCOME_TYPE": [Income_type],
+                                                "NAME_EDUCATION_TYPE": [EDU_DICT[Education]],
+                                                "NAME_FAMILY_STATUS": [Marital_status],
+                                                "NAME_HOUSING_TYPE": [Housing_type],
+                                                "DAYS_EMPLOYED": [Day_Employed],
+                                                "FLAG_MOBIL": [Flag_Mobile],
+                                                "FLAG_WORK_PHONE": [Flag_work_phone],
+                                                "FLAG_PHONE": [Flag_Phone],
+                                                "FLAG_EMAIL": [Flag_Email],
+                                                "CNT_FAM_MEMBERS": [Family_mem]}))
+
+            result = 'Pass' if result[0] == 1 else 'Did not Pass'
+
+            st.success('Credit Card approval prediction results is {}'.format(result))
+
+if __name__ == '__main__':
+    main()

app_2.py

@@ -0,0 +1,139 @@
+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"
+access_token = st.secrets["HF_TOKEN"]
+
+cat_imputer = joblib.load(
+    hf_hub_download(repo_id=REPO_ID, filename="categorical_imputer.joblib", token=access_token, repo_type="model")
+)
+
+encoder = joblib.load(
+    hf_hub_download(repo_id=REPO_ID, filename="encoder.joblib", token=access_token, repo_type="model")
+)
+
+num_imputer = joblib.load(
+    hf_hub_download(repo_id=REPO_ID, filename="numerical_imputer.joblib", token=access_token, repo_type="model")
+)
+
+scaler = joblib.load(
+    hf_hub_download(repo_id=REPO_ID, filename="scaler.joblib", token=access_token, repo_type="model")
+)
+
+model = joblib.load(
+    hf_hub_download(repo_id=REPO_ID, filename="Final_model.joblib", token=access_token, repo_type="model")
+)
+
+# 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 = 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)

app_3.py

@@ -1,71 +0,0 @@
-import joblib
-import pandas as pd
-import streamlit as st
-from huggingface_hub import hf_hub_download
-
-REPO_ID = "chanyaphas/creditc"
-
-access_token = st.secrets["HF_TOKEN"]
-
-model = joblib.load(
-    hf_hub_download(repo_id=REPO_ID, filename='model.joblib', token=access_token, repo_type="space")
-)
-
-unique_values = joblib.load(
-    hf_hub_download(repo_id=REPO_ID, filename='unique_values.joblib', token=access_token, repo_type="space")
-)
-
-EDU_DICT = {'Lower secondary': 1,
-            'Secondary / secondary special': 2,
-            'Academic degree': 3,
-            'Incomplete higher': 4,
-            'Higher education' : 5
-            }
-
-def main():
-    st.title("Credit Card Approval Prediction")
-
-    with st.form("questionaire"):
-
-        Gender = st.selectbox('Gender', unique_values['CODE_GENDER'])
-        Own_car = st.selectbox('Own_car', unique_values['FLAG_OWN_CAR'])
-        Property = st.selectbox('Property', unique_values['FLAG_OWN_REALTY'])
-        Income_type = st.selectbox('Income_type', unique_values['NAME_INCOME_TYPE'])
-        Marital_status = st.selectbox('Marital_status', unique_values['NAME_FAMILY_STATUS'])
-        Housing_type = st.selectbox('Housing_type', unique_values['NAME_HOUSING_TYPE'])
-        Education = st.selectbox('Education', unique_values['NAME_EDUCATION_TYPE'])
-
-        Income = st.slider('Income', min_value=27000, max_value=1575000)
-        Children = st.slider('Children', min_value=0, max_value=19)
-        Day_Employed = st.slider('Day_Employed', min_value=0, max_value=3)
-        Flag_Mobile = st.slider('Flag_Mobile', min_value=0, max_value=1)
-        Flag_work_phone = st.slider('Flag_work_phone', min_value=0, max_value=1)
-        Flag_Phone = st.slider('Flag_Phone', min_value=0, max_value=1)
-        Flag_Email = st.slider('Flag_Email', min_value=0, max_value=1)
-        Family_mem = st.slider('Family_mem', min_value=1, max_value=20)
-
-        clicked = st.form_submit_button("Result")
-        if clicked:
-            result = model.predict(pd.DataFrame({
-                                                "CODE_GENDER": [Gender],
-                                                "FLAG_OWN_CAR": [Own_car],
-                                                "FLAG_OWN_REALTY": [Property],
-                                                "CNT_CHILDREN": [Children],
-                                                "AMT_INCOME_TOTAL": [Income],
-                                                "NAME_INCOME_TYPE": [Income_type],
-                                                "NAME_EDUCATION_TYPE": [EDU_DICT[Education]],
-                                                "NAME_FAMILY_STATUS": [Marital_status],
-                                                "NAME_HOUSING_TYPE": [Housing_type],
-                                                "DAYS_EMPLOYED": [Day_Employed],
-                                                "FLAG_MOBIL": [Flag_Mobile],
-                                                "FLAG_WORK_PHONE": [Flag_work_phone],
-                                                "FLAG_PHONE": [Flag_Phone],
-                                                "FLAG_EMAIL": [Flag_Email],
-                                                "CNT_FAM_MEMBERS": [Family_mem]}))
-
-            result = 'Pass' if result[0] == 1 else 'Did not Pass'
-
-            st.success('Credit Card approval prediction results is {}'.format(result))
-
-if __name__ == '__main__':
-    main()