Upload app.py with huggingface_hub

app.py

@@ -1,62 +1,50 @@
+# week_3_mls/deployment/app.py
 import streamlit as st
 import pandas as pd
 import joblib
 from huggingface_hub import hf_hub_download
 
-# Download model from Hugging Face
-model_path = hf_hub_download(
-    repo_id="roshra/machine-failure-prediction",
-    filename="tourism_clean_model.joblib",
-    repo_type="space"
-)
+# Load model from HF Hub
+MODEL_REPO = "roshra/machine-failure-prediction"
+MODEL_FILE = "tourism_clean_model.joblib"
+
+model_path = hf_hub_download(repo_id=MODEL_REPO, filename=MODEL_FILE, repo_type="space")
 model = joblib.load(model_path)
 
-st.title("🏖️ Tourism Package Purchase Prediction")
-
-st.write("Fill in customer details to predict if they will buy a package:")
-
-# Inputs
-age = st.number_input("Age", min_value=18, max_value=100, value=30)
-typeofcontact = st.selectbox("Type of Contact", ["Company Invited", "Self Enquiry"])
-citytier = st.selectbox("City Tier", [1, 2, 3])
-duration = st.number_input("Duration of Pitch", min_value=1, max_value=100, value=20)
-num_persons = st.number_input("Number of Persons Visiting", min_value=1, max_value=10, value=2)
-followups = st.number_input("Number of Followups", min_value=0, max_value=10, value=2)
-star = st.selectbox("Preferred Property Star", [3, 4, 5])
-marital = st.selectbox("Marital Status", ["Married", "Single", "Divorced"])
-trips = st.number_input("Number of Trips", min_value=0, max_value=20, value=2)
-passport = st.selectbox("Passport", [0, 1])
-income = st.number_input("Monthly Income", min_value=1000, max_value=100000, value=50000)
-satisfaction = st.slider("Pitch Satisfaction Score", 1, 5, 3)
-owncar = st.selectbox("Own Car", [0, 1])
-children = st.number_input("Number of Children Visiting", min_value=0, max_value=5, value=1)
-designation = st.selectbox("Designation", ["Executive", "Manager", "Senior Manager", "AVP", "VP"])
-product = st.selectbox("Product Pitched", ["Basic", "Standard", "Deluxe", "Super Deluxe", "King"])
-
-# Prediction
-if st.button("Predict"):
+st.title("🧳 Tourism Package Purchase Prediction App")
+st.write("Fill in details to predict if the customer will purchase the package.")
+
+# UI inputs
+age = st.number_input("Age", 18, 100, 30)
+duration = st.number_input("Trip Duration (days)", 1, 30, 5)
+adults = st.number_input("Number of Adults", 1, 10, 2)
+children = st.number_input("Number of Children", 0, 10, 0)
+income = st.number_input("Monthly Income", 1000, 200000, 30000)
+
+prop = st.selectbox("Preferred Property", ["Hotel", "Resort", "Guest House", "Other"])
+travel = st.selectbox("Mode of Travel", ["Air", "Train", "Car", "Bus"])
+gender = st.selectbox("Gender", ["Male", "Female", "Other"])
+occupation = st.selectbox("Occupation", ["Salaried", "Self-Employed", "Business", "Other"])
+marital = st.selectbox("Marital Status", ["Single", "Married", "Divorced", "Other"])
+
+if st.button("Predict Purchase"):
     input_df = pd.DataFrame([{
         "Age": age,
-        "TypeofContact": typeofcontact,
-        "CityTier": citytier,
-        "DurationOfPitch": duration,
-        "NumberOfPersonVisiting": num_persons,
-        "NumberOfFollowups": followups,
-        "PreferredPropertyStar": star,
-        "MaritalStatus": marital,
-        "NumberOfTrips": trips,
-        "Passport": passport,
+        "Duration": duration,
+        "NumberOfAdults": adults,
+        "NumberOfChildren": children,
         "MonthlyIncome": income,
-        "PitchSatisfactionScore": satisfaction,
-        "OwnCar": owncar,
-        "NumberOfChildrenVisiting": children,
-        "Designation": designation,
-        "ProductPitched": product
+        "PreferredProperty": prop,
+        "ModeOfTravel": travel,
+        "Gender": gender,
+        "Occupation": occupation,
+        "MaritalStatus": marital
     }])
 
     prediction = model.predict(input_df)[0]
     if prediction == 1:
-        st.success("✅ Likely to Purchase the Package!")
+        st.success("✅ Customer is likely to purchase the package!")
     else:
-        st.error("❌ Not Likely to Purchase the Package")
+        st.error("❌ Customer is unlikely to purchase the package.")
+