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| import streamlit as st | |
| import pandas as pd | |
| from huggingface_hub import hf_hub_download | |
| import joblib | |
| # Download and load the model | |
| model_path = hf_hub_download(repo_id="VeerendraManikonda/visit_with_us_model", filename="best_tourism_model_v1.joblib") | |
| model = joblib.load(model_path) | |
| # Streamlit UI for Machine Failure Prediction | |
| st.title("Tourism Prediction App") | |
| st.write(""" | |
| This application predicts the potential buyers of the products based on the pitch parameters. | |
| Please enter the customer interaction details. | |
| """) | |
| # Input form | |
| with st.form("prediction_form"): | |
| Age = st.number_input("Age", min_value=18, max_value=100, value=30) | |
| TypeofContact = st.selectbox("Type of Contact", ["Self Enquiry", "Company Invited"]) | |
| CityTier = st.selectbox("City Tier", [1, 2, 3]) | |
| DurationOfPitch = st.number_input("Duration Of Pitch", min_value=0, max_value=50, value=10) | |
| Occupation = st.selectbox("Occupation", ["Salaried", "Small Business", "Large Business", "Student", "Free Lancer"]) | |
| Gender = st.selectbox("Gender", ["Male", "Female"]) | |
| NumberOfPersonVisiting = st.number_input("Number Of Persons Visiting", min_value=1, max_value=10, value=1) | |
| NumberOfFollowups = st.number_input("Number Of Follow-ups", min_value=0, max_value=20, value=1) | |
| ProductPitched = st.selectbox("Product Pitched", ["Basic", "Standard", "Deluxe"]) | |
| 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=100, value=1) | |
| Passport = st.selectbox("Passport", [0, 1]) | |
| PitchSatisfactionScore = st.slider("Pitch Satisfaction Score", 1, 5, 3) | |
| OwnCar = st.selectbox("Own Car", [0, 1]) | |
| NumberOfChildrenVisiting = st.number_input("Number Of Children Visiting", min_value=0, max_value=10, value=0) | |
| Designation = st.selectbox("Designation", ["Manager", "Senior Manager", "Executive", "AVP"]) | |
| MonthlyIncome = st.number_input("Monthly Income", min_value=1000, max_value=100000, value=25000) | |
| submit = st.form_submit_button("Predict") | |
| if submit: | |
| # Convert inputs to DataFrame | |
| input_data = pd.DataFrame([{ | |
| "Age": Age, | |
| "TypeofContact": TypeofContact, | |
| "CityTier": CityTier, | |
| "DurationOfPitch": DurationOfPitch, | |
| "Occupation": Occupation, | |
| "Gender": Gender, | |
| "NumberOfPersonVisiting": NumberOfPersonVisiting, | |
| "NumberOfFollowups": NumberOfFollowups, | |
| "ProductPitched": ProductPitched, | |
| "PreferredPropertyStar": PreferredPropertyStar, | |
| "MaritalStatus": MaritalStatus, | |
| "NumberOfTrips": NumberOfTrips, | |
| "Passport": Passport, | |
| "PitchSatisfactionScore": PitchSatisfactionScore, | |
| "OwnCar": OwnCar, | |
| "NumberOfChildrenVisiting": NumberOfChildrenVisiting, | |
| "Designation": Designation, | |
| "MonthlyIncome": MonthlyIncome | |
| }]) | |
| # Predict | |
| prediction = model.predict(input_data)[0] | |
| if prediction == 1: | |
| st.success("Customer is likely to purchase the product.") | |
| else: | |
| st.error("Customer is not likely to purchase the product.") |