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a72e247 26c66e8 5b5db34 75458d9 2b3dc19 1c62f65 26c66e8 a72e247 75458d9 a72e247 75458d9 a72e247 | 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 | import streamlit as st
import requests
import json
import pandas as pd
from huggingface_hub import hf_hub_download # Corrected import statement
import joblib # Corrected typo
# --- This is a dummy comment to force re-upload after Dockerfile fix ---
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#Download and load the model
model_path = hf_hub_download(repo_id="grkavi0912/Tpro", filename="best_tour_model.joblib", repo_type="model") # Added repo_type
model = joblib.load(model_path)
#Streamlit UI for Tourism package prediction
st.title("Tourism Package Prediction")
st.write("Enter the details to predict the package price")
#User input
Age = st.number_input("Age",min_value=18,max_value=100)
Type_of_contact = st.selectbox("Type of Contact",["Direct","Call"])
City_Tier = st.selectbox("City Tier",[1,2,3])
Duration_of_Pitch = st.number_input("Duration of Pitch",min_value=1,max_value=365)
Occupation = st.selectbox("Occupation",["Self-employed","Salaried","Business"])
Gender = st.selectbox("Gender",["Male","Female"])
Number_of_Person_Visiting= st.number_input("Number of Person Traveling",min_value=1,max_value=10)
Number_of_Followups= st.number_input("Number of Followups",min_value=0,max_value=10)
Product_Pitched= st.selectbox("Product Pitched",["Basic","Standard","Premium"])
Preferred_Property_Star= st.number_input("Preferred Property Star",min_value=1,max_value=5)
Marital_Status= st.selectbox("Marital Status",["Married","Divorced","Single"])
NumberOfTrips= st.number_input("Number of Trips",min_value=1,max_value=10)
Passport= st.selectbox("Passport",["Yes","No"])
Pitch_Satisfaction_Score= st.number_input("Pitch Satisfaction Score",min_value=1,max_value=5)
Own_Car= st.selectbox("Own Car",["Yes","No"])
Number_of_Children= st.number_input("Number of Children",min_value=0,max_value=10)
Designation= st.selectbox("Designation",["Executive","Manager","Senior Manager","Associate","Director"])
Monthly_Income= st.number_input("Monthly Income",min_value=0,max_value=100000)
#Assemble input into DataFrame
input_data = pd.DataFrame({
"Age": [Age],
"TypeofContact": [Type_of_contact], # Corrected variable name
"CityTier": [City_Tier], # Corrected variable name
"DurationOfPitch": [Duration_of_Pitch], # Corrected variable name
"Occupation": [Occupation],
"Gender": [Gender],
"NumberOfPersonVisiting": [Number_of_Person_Visiting], # Corrected variable name
"NumberOfFollowups": [Number_of_Followups], # Corrected variable name
"ProductPitched": [Product_Pitched], # Corrected variable name
"PreferredPropertyStar": [Preferred_Property_Star], # Corrected variable name
"MaritalStatus": [Marital_Status], # Corrected variable name
"NumberOfTrips": [NumberOfTrips], # Corrected variable name
"Passport": [1 if Passport == "Yes" else 0], # Converted to numerical
"PitchSatisfactionScore": [Pitch_Satisfaction_Score], # Corrected variable name
"OwnCar": [1 if Own_Car == "Yes" else 0], # Converted to numerical
"NumberOfChildrenVisiting": [Number_of_Children], # Corrected variable name
"Designation": [Designation],
"MonthlyIncome": [Monthly_Income]
})
if st.button("Predict"):
#Make prediction
prediction = model.predict(input_data)[0]
result = "Tourism package predicted as " + str(prediction)
st.subheader("Predicted Result:")
st.success(f"The model predicts: **{result}**")
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