Upload folder using huggingface_hub

app.py

@@ -3,38 +3,38 @@ import pandas as pd
 from huggingface_hub import hf_hub_download
 import joblib
 
-# Download the model from the Model Hub
+# Downloading the model from the Model Hub
 model_path = hf_hub_download(repo_id="skalpitin/Tourism-Package-Prediction", filename="Tourism-Package-Prediction_v1.joblib")
 
-# Load the model
+# Loading the model
 model = joblib.load(model_path)
 
 # Streamlit UI for Customer Churn Prediction
-st.title("Visit with us Prediction App")
-st.write("The Visit with us Prediction App is an internal tool for the internal staff that predicts whether customers are going to take the tourism package or not.")
+st.title("`Visit with us` Prediction App")
+st.write("The `Visit with us` Prediction App is an internal tool for the internal staff that predicts whether customers are going to take the tourism package or not.")
 st.write("Kindly enter the customer details to check whether they are buy the tourism package.")
 
-# Collect user input
-Age = st.number_input("Age (customer's age in years)", min_value=18, max_value=100, value=30)
+# Collecting user input
+Age = st.number_input("Age (customer's age in years)", min_value=18, value=25)
 TypeofContact = st.selectbox("Type of contact", ["Company Invited", "Self Inquiry"])
 CityTier = st.selectbox("City Tier", ["Tier 1", "Tier 2", "Tier 3"])
 Occupation = st.selectbox("Occupation", ["Salaried", "Freelancer", "Large Business", "Small Business"])
 Gender = st.selectbox("Gender", ["Male", "Female"])
-NumberOfPersonVisiting = st.number_input("Number Of Person Visiting", min_value=0, max_value=25, value=2)
+NumberOfPersonVisiting = st.number_input("Number Of Person Visiting", min_value=0, max_value=25, value=1)
 PreferredPropertyStar = st.selectbox("Preferred Property Star", ["1", "2", "3","4", "5"])
 MaritalStatus = st.selectbox("Marital Status", ["Single", "Married", "Divorced"])
-NumberOfTrips = st.number_input("Number Of Trips", min_value=0, max_value=50, value=5)
+NumberOfTrips = st.number_input("Number Of Trips", min_value=0, value=5)
 Passport = st.selectbox("Has Passport?", ["Yes", "No"])
 OwnCar = st.selectbox("Owns Car?", ["Yes", "No"])
-NumberOfChildrenVisiting = st.number_input("Number Of Children (Below 5) Visiting", min_value=0, max_value=10, value=2)
+NumberOfChildrenVisiting = st.number_input("Number Of Children (Below 5) Visiting", min_value=0, value=2)
 Designation = st.selectbox("Designation", ["Executive", "Manager", "AVP", "Senior Manager", "VP"])
-MonthlyIncome = st.number_input("Monthly Income", min_value=0.0, max_value=50000.0, value=5000.0)
+MonthlyIncome = st.number_input("Monthly Income", min_value=0.0, value=5000.0)
 PitchSatisfactionScore = st.selectbox("Pitch Satisfaction Score", ["1", "2", "3","4", "5"]) 
 ProductPitched = st.selectbox("Product Pitched", ["Basic", "Deluxe", "King", "Standard", "Super Deluxe"])
 NumberOfFollowups = st.selectbox("Number Of Followups", ["1", "2", "3","4", "5"]) 
 DurationOfPitch = st.number_input("Duration Of Pitch", min_value=0.0, value=30.0)
 
-# Convert categorical inputs to match model training
+# Converting inputs to a dataframe to pass to the model
 input_data = pd.DataFrame([{
     'Age': Age,
     'TypeofContact': TypeofContact,
@@ -56,10 +56,10 @@ input_data = pd.DataFrame([{
     'DurationOfPitch': DurationOfPitch
 }])
 
-# Set the classification threshold
+# Setting the classification threshold
 classification_threshold = 0.45
 
-# Predict button
+# Predict button -  Calling the model with input dataframe
 if st.button("Predict"):
     prediction_proba = model.predict_proba(input_data)[0, 1]
     prediction = (prediction_proba >= classification_threshold).astype(int)