Upload folder using huggingface_hub

app.py

@@ -4,19 +4,23 @@ import joblib
 
 from huggingface_hub import hf_hub_download
 
+# -------------------------------------------------------------------
+# CONFIG
+# -------------------------------------------------------------------
 MODEL_REPO_ID = "bhumitps/tourism_model"
 MODEL_FILENAME = "best_tourism_model_v3.joblib"
 
 
 @st.cache_resource
 def load_model():
-    st.write("Loading model from Hugging Face Hub...")  # simple log in UI
+    """Download the model from HF Hub and load it with joblib."""
+    st.write("Loading model from Hugging Face Hub...")
     try:
         model_path = hf_hub_download(
             repo_id=MODEL_REPO_ID,
             filename=MODEL_FILENAME,
             repo_type="model",
-            force_download=True,
+            force_download=True,       # always fetch latest v3 model
         )
         model = joblib.load(model_path)
         st.write("Model loaded successfully.")
@@ -28,11 +32,15 @@ def load_model():
 
 model = load_model()
 
+# -------------------------------------------------------------------
+# UI
+# -------------------------------------------------------------------
 st.title("Wellness Tourism Package Purchase Prediction")
 
 st.write(
     """
-    Predict whether a customer is likely to purchase the **Wellness Tourism Package**.
+    Predict whether a customer is likely to purchase the
+    **Wellness Tourism Package**.
 
     Fill in the customer details below and click **Predict**.
     """
@@ -44,56 +52,102 @@ col1, col2 = st.columns(2)
 with col1:
     Age = st.number_input("Age", min_value=0, max_value=100, value=35)
     CityTier = st.selectbox("CityTier", options=[1, 2, 3], index=0)
-    DurationOfPitch = st.number_input("DurationOfPitch (minutes)", min_value=0, max_value=300, value=15)
-    NumberOfPersonVisiting = st.number_input("NumberOfPersonVisiting", min_value=1, max_value=20, value=2)
-    NumberOfFollowups = st.number_input("NumberOfFollowups", min_value=0, max_value=20, value=2)
-    PreferredPropertyStar = st.selectbox("PreferredPropertyStar", options=[1, 2, 3, 4, 5], index=2)
-    NumberOfTrips = st.number_input("NumberOfTrips", min_value=0, max_value=50, value=1)
-    NumberOfChildrenVisiting = st.number_input("NumberOfChildrenVisiting", min_value=0, max_value=10, value=0)
-    MonthlyIncome = st.number_input("MonthlyIncome", min_value=0, max_value=1000000, value=50000, step=1000)
+    DurationOfPitch = st.number_input(
+        "DurationOfPitch (minutes)", min_value=0, max_value=300, value=15
+    )
+    NumberOfPersonVisiting = st.number_input(
+        "NumberOfPersonVisiting", min_value=1, max_value=20, value=2
+    )
+    NumberOfFollowups = st.number_input(
+        "NumberOfFollowups", min_value=0, max_value=20, value=2
+    )
+    PreferredPropertyStar = st.selectbox(
+        "PreferredPropertyStar", options=[1, 2, 3, 4, 5], index=2
+    )
+    NumberOfTrips = st.number_input(
+        "NumberOfTrips", min_value=0, max_value=50, value=1
+    )
+    NumberOfChildrenVisiting = st.number_input(
+        "NumberOfChildrenVisiting", min_value=0, max_value=10, value=0
+    )
+    MonthlyIncome = st.number_input(
+        "MonthlyIncome", min_value=0, max_value=1_000_000, value=50_000, step=1000
+    )
 
 with col2:
-    TypeofContact = st.selectbox("TypeofContact", options=["Self Enquiry", "Company Invited", "Other"])
-    Occupation = st.selectbox("Occupation", options=["Salaried", "Self Employed", "Free Lancer", "Other"])
-    Gender = st.selectbox("Gender", options=["Male", "Female", "Other"])
+    TypeofContact = st.selectbox(
+        "TypeofContact",
+        options=["Self Enquiry", "Company Invited", "Other"],
+    )
+    Occupation = st.selectbox(
+        "Occupation", options=["Salaried", "Self Employed", "Free Lancer", "Other"]
+    )
+    Gender = st.selectbox(
+        "Gender", options=["Male", "Female", "Other"]
+    )
     ProductPitched = st.text_input("ProductPitched (raw value)", value="Basic")
-    MaritalStatus = st.selectbox("MaritalStatus", options=["Married", "Single", "Divorced", "Other"])
+    MaritalStatus = st.selectbox(
+        "MaritalStatus", options=["Married", "Single", "Divorced", "Other"]
+    )
     Passport = st.selectbox("Passport", options=["No", "Yes"])
-    PitchSatisfactionScore = st.selectbox("PitchSatisfactionScore", options=[1, 2, 3, 4, 5], index=2)
+    PitchSatisfactionScore = st.selectbox(
+        "PitchSatisfactionScore", options=[1, 2, 3, 4, 5], index=2
+    )
     OwnCar = st.selectbox("OwnCar", options=["No", "Yes"])
-    Designation = st.selectbox("Designation", options=["Executive", "Manager", "Senior Manager", "AVP", "VP", "Other"])
+    Designation = st.selectbox(
+        "Designation",
+        options=["Executive", "Manager", "Senior Manager", "AVP", "VP", "Other"],
+    )
 
 st.markdown("---")
 
+# -------------------------------------------------------------------
+# Prediction
+# -------------------------------------------------------------------
 if st.button("Predict"):
-    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,
-    }])
-
+    # Map Yes/No to the numeric format used during training (0/1)
+    passport_num = 1 if Passport == "Yes" else 0
+    owncar_num = 1 if OwnCar == "Yes" else 0
+
+    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_num,  # numeric
+                "PitchSatisfactionScore": PitchSatisfactionScore,
+                "OwnCar": owncar_num,       # numeric
+                "NumberOfChildrenVisiting": NumberOfChildrenVisiting,
+                "Designation": Designation,
+                "MonthlyIncome": MonthlyIncome,
+            }
+        ]
+    )
+
+    # Make prediction
     pred_proba = model.predict_proba(input_data)[0][1]
     pred_label = model.predict(input_data)[0]
 
     st.subheader("Prediction Result")
     if pred_label == 1:
-        st.success(f"Customer is **LIKELY** to purchase the Wellness Tourism Package. (Probability: {pred_proba:.2%})")
+        st.success(
+            f"Customer is **LIKELY** to purchase the Wellness Tourism Package. "
+            f"(Probability: {pred_proba:.2%})"
+        )
     else:
-        st.info(f"Customer is **UNLIKELY** to purchase the Wellness Tourism Package. (Probability: {pred_proba:.2%})")
+        st.info(
+            f"Customer is **UNLIKELY** to purchase the Wellness Tourism Package. "
+            f"(Probability: {pred_proba:.2%})"
+        )
 
     st.caption("Note: probabilities are model-based estimates and not guarantees.")