Spaces:
Sleeping
Sleeping
File size: 3,484 Bytes
c5afcce | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 | import streamlit as st
import pandas as pd
from huggingface_hub import hf_hub_download
import joblib
# Download the model from Hugging Face Hub
model_path = hf_hub_download(
repo_id="mdsalmon159/MLOps_Project_space1",
filename="MLOps_Tourism_Prediction_model_v1.joblib"
)
# Load the trained model
model = joblib.load(model_path)
# Streamlit UI
st.title("MLOPS – Customer Tourism Package Purchase Prediction App")
st.write(
"This application predicts whether a customer will be "
"purchasing a travel package based on demographic and interaction."
)
st.write("Please enter the customer details.")
# Customer Details
Age = st.number_input("Age", min_value=18, max_value=100, value=30)
TypeofContact = st.selectbox(
"Type of Contact",
["Company Invited", "Self Inquiry"]
)
CityTier = st.selectbox("City Tier", [1, 2, 3])
Occupation = st.selectbox( "Occupation",
["Salaried", "Freelancer", "Small Business", "Large Business"]
)
Gender = st.selectbox("Gender", ["Male", "Female"])
NumberOfPersonVisiting = st.number_input( "Number of Persons Visiting",
min_value=1, max_value=10, value=2
)
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 (per year)",
min_value=0, max_value=50, value=2
)
Passport = st.selectbox("Has Passport?", ["Yes", "No"])
OwnCar = st.selectbox("Owns a Car?", ["Yes", "No"])
NumberOfChildrenVisiting = st.number_input(
"Number of Children Visiting",
min_value=0, max_value=5, value=0
)
Designation = st.selectbox( "Designation",
["Executive", "Manager", "Senior Manager", "VP"]
)
MonthlyIncome = st.number_input( "Monthly Income",
min_value=5000, max_value=500000, value=50000
)
# Contacted Details
PitchSatisfactionScore = st.slider( "Pitch Satisfaction Score",
min_value=1, max_value=5, value=3
)
ProductPitched = st.selectbox( "Product Pitched",
["Basic", "Standard", "Deluxe", "Super Deluxe"]
)
NumberOfFollowups = st.number_input( "Number of Follow-ups",
min_value=0, max_value=20, value=2
)
DurationOfPitch = st.number_input( "Duration of Pitch (minutes)",
min_value=1, max_value=120, value=15
)
# Prepare input data
input_data = pd.DataFrame([{
"Age": Age,
"TypeofContact": TypeofContact,
"CityTier": CityTier,
"Occupation": Occupation,
"Gender": Gender,
"NumberOfPersonVisiting": NumberOfPersonVisiting,
"PreferredPropertyStar": PreferredPropertyStar,
"MaritalStatus": MaritalStatus,
"NumberOfTrips": NumberOfTrips,
"Passport": 1 if Passport == "Yes" else 0,
"OwnCar": 1 if OwnCar == "Yes" else 0,
"NumberOfChildrenVisiting": NumberOfChildrenVisiting,
"Designation": Designation,
"MonthlyIncome": MonthlyIncome,
"PitchSatisfactionScore": PitchSatisfactionScore,
"ProductPitched": ProductPitched,
"NumberOfFollowups": NumberOfFollowups,
"DurationOfPitch": DurationOfPitch
}])
# Classification threshold
classification_threshold = 0.5
# Prediction
if st.button("Predict"):
prediction_proba = model.predict_proba(input_data)[0, 1]
prediction = (prediction_proba >= classification_threshold).astype(int)
if prediction == 1:
st.success("The customer is likely to purchase the package.")
else:
st.error("The customer is unlikely to purchase the package.")
|