Update app.py

app.py

@@ -1,96 +1,96 @@
-import streamlit as st
-import pandas as pd
-import joblib
-import numpy as np
-from huggingface_hub import hf_hub_download
-
-# Configuration
-HF_USERNAME = "Sricharan451706"
-HF_MODEL_REPO = f"{HF_USERNAME}/tourism-prediction-model"
-
-st.title("Wellness Tourism Package Prediction")
-st.write("Enter customer details to predict if they will purchase the package.")
-
-@st.cache_resource
-def load_model():
-    try:
-        # Try loading locally first for development
-        model = joblib.load("models/model.joblib")
-        return model
-    except:
-        # Load from Hugging Face
-        try:
-            model_path = hf_hub_download(repo_id=HF_MODEL_REPO, filename="model.joblib")
-            model = joblib.load(model_path)
-            return model
-        except Exception as e:
-            st.error(f"Error loading model: {e}")
-            return None
-
-model = load_model()
-
-if model:
-    # Input form
-    with st.form("prediction_form"):
-        col1, col2 = st.columns(2)
-        
-        with col1:
-            age = st.number_input("Age", min_value=18, max_value=100, value=30)
-            type_of_contact = st.selectbox("Type of Contact", ["Company Invited", "Self Inquiry"])
-            city_tier = st.selectbox("City Tier", [1, 2, 3])
-            occupation = st.selectbox("Occupation", ["Salaried", "Small Business", "Large Business", "Free Lancer"])
-            gender = st.selectbox("Gender", ["Male", "Female"])
-            number_of_person_visiting = st.number_input("Number of Person Visiting", min_value=1, value=2)
-            preferred_property_star = st.selectbox("Preferred Property Star", [3.0, 4.0, 5.0])
-            marital_status = st.selectbox("Marital Status", ["Married", "Divorced", "Single", "Unmarried"])
-            number_of_trips = st.number_input("Number of Trips", min_value=0, value=1)
-            
-        with col2:
-            passport = st.selectbox("Passport", [0, 1], format_func=lambda x: "Yes" if x == 1 else "No")
-            own_car = st.selectbox("Own Car", [0, 1], format_func=lambda x: "Yes" if x == 1 else "No")
-            number_of_children_visiting = st.number_input("Number of Children Visiting", min_value=0, value=0)
-            designation = st.selectbox("Designation", ["Manager", "Executive", "Senior Manager", "AVP", "VP"])
-            monthly_income = st.number_input("Monthly Income", min_value=0, value=20000)
-            pitch_satisfaction_score = st.slider("Pitch Satisfaction Score", 1, 5, 3)
-            product_pitched = st.selectbox("Product Pitched", ["Basic", "Standard", "Deluxe", "Super Deluxe", "King"])
-            number_of_followups = st.number_input("Number of Followups", min_value=0, value=3)
-            duration_of_pitch = st.number_input("Duration of Pitch (minutes)", min_value=0, value=10)
-        
-        submitted = st.form_submit_button("Predict")
-        
-    if submitted:
-        # Create dataframe from inputs
-        input_data = pd.DataFrame({
-            'Age': [age],
-            'TypeofContact': [type_of_contact],
-            'CityTier': [city_tier],
-            'Occupation': [occupation],
-            'Gender': [gender],
-            'NumberOfPersonVisiting': [number_of_person_visiting],
-            'PreferredPropertyStar': [preferred_property_star],
-            'MaritalStatus': [marital_status],
-            'NumberOfTrips': [number_of_trips],
-            'Passport': [passport],
-            'OwnCar': [own_car],
-            'NumberOfChildrenVisiting': [number_of_children_visiting],
-            'Designation': [designation],
-            'MonthlyIncome': [monthly_income],
-            'PitchSatisfactionScore': [pitch_satisfaction_score],
-            'ProductPitched': [product_pitched],
-            'NumberOfFollowups': [number_of_followups],
-            'DurationOfPitch': [duration_of_pitch]
-        })
-        
-        # Make prediction using the pipeline
-        # The pipeline handles encoding and scaling automatically
-        try:
-            prediction = model.predict(input_data)[0]
-            probability = model.predict_proba(input_data)[0][1]
-            
-            if prediction == 1:
-                st.success(f"Prediction: Will Purchase (Probability: {probability:.2f})")
-            else:
-                st.warning(f"Prediction: Will Not Purchase (Probability: {probability:.2f})")
-        except Exception as e:
-            st.error(f"Prediction Error: {e}")
-            st.info("Ensure all columns used in training are provided in the input dataframe.")
+import streamlit as st
+import pandas as pd
+import joblib
+import numpy as np
+from huggingface_hub import hf_hub_download
+
+# Configuration
+HF_USERNAME = "Sricharan451706"
+HF_MODEL_REPO = f"{HF_USERNAME}/Tourism-Package-Predictor"
+
+st.title("Wellness Tourism Package Prediction")
+st.write("Enter customer details to predict if they will purchase the package.")
+
+@st.cache_resource
+def load_model():
+    try:
+        # Try loading locally first for development
+        model = joblib.load("models/model.joblib")
+        return model
+    except:
+        # Load from Hugging Face
+        try:
+            model_path = hf_hub_download(repo_id=HF_MODEL_REPO, filename="model.joblib")
+            model = joblib.load(model_path)
+            return model
+        except Exception as e:
+            st.error(f"Error loading model: {e}")
+            return None
+
+model = load_model()
+
+if model:
+    # Input form
+    with st.form("prediction_form"):
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            age = st.number_input("Age", min_value=18, max_value=100, value=30)
+            type_of_contact = st.selectbox("Type of Contact", ["Company Invited", "Self Inquiry"])
+            city_tier = st.selectbox("City Tier", [1, 2, 3])
+            occupation = st.selectbox("Occupation", ["Salaried", "Small Business", "Large Business", "Free Lancer"])
+            gender = st.selectbox("Gender", ["Male", "Female"])
+            number_of_person_visiting = st.number_input("Number of Person Visiting", min_value=1, value=2)
+            preferred_property_star = st.selectbox("Preferred Property Star", [3.0, 4.0, 5.0])
+            marital_status = st.selectbox("Marital Status", ["Married", "Divorced", "Single", "Unmarried"])
+            number_of_trips = st.number_input("Number of Trips", min_value=0, value=1)
+            
+        with col2:
+            passport = st.selectbox("Passport", [0, 1], format_func=lambda x: "Yes" if x == 1 else "No")
+            own_car = st.selectbox("Own Car", [0, 1], format_func=lambda x: "Yes" if x == 1 else "No")
+            number_of_children_visiting = st.number_input("Number of Children Visiting", min_value=0, value=0)
+            designation = st.selectbox("Designation", ["Manager", "Executive", "Senior Manager", "AVP", "VP"])
+            monthly_income = st.number_input("Monthly Income", min_value=0, value=20000)
+            pitch_satisfaction_score = st.slider("Pitch Satisfaction Score", 1, 5, 3)
+            product_pitched = st.selectbox("Product Pitched", ["Basic", "Standard", "Deluxe", "Super Deluxe", "King"])
+            number_of_followups = st.number_input("Number of Followups", min_value=0, value=3)
+            duration_of_pitch = st.number_input("Duration of Pitch (minutes)", min_value=0, value=10)
+        
+        submitted = st.form_submit_button("Predict")
+        
+    if submitted:
+        # Create dataframe from inputs
+        input_data = pd.DataFrame({
+            'Age': [age],
+            'TypeofContact': [type_of_contact],
+            'CityTier': [city_tier],
+            'Occupation': [occupation],
+            'Gender': [gender],
+            'NumberOfPersonVisiting': [number_of_person_visiting],
+            'PreferredPropertyStar': [preferred_property_star],
+            'MaritalStatus': [marital_status],
+            'NumberOfTrips': [number_of_trips],
+            'Passport': [passport],
+            'OwnCar': [own_car],
+            'NumberOfChildrenVisiting': [number_of_children_visiting],
+            'Designation': [designation],
+            'MonthlyIncome': [monthly_income],
+            'PitchSatisfactionScore': [pitch_satisfaction_score],
+            'ProductPitched': [product_pitched],
+            'NumberOfFollowups': [number_of_followups],
+            'DurationOfPitch': [duration_of_pitch]
+        })
+        
+        # Make prediction using the pipeline
+        # The pipeline handles encoding and scaling automatically
+        try:
+            prediction = model.predict(input_data)[0]
+            probability = model.predict_proba(input_data)[0][1]
+            
+            if prediction == 1:
+                st.success(f"Prediction: Will Purchase (Probability: {probability:.2f})")
+            else:
+                st.warning(f"Prediction: Will Not Purchase (Probability: {probability:.2f})")
+        except Exception as e:
+            st.error(f"Prediction Error: {e}")
+            st.info("Ensure all columns used in training are provided in the input dataframe.")