Add cache check before downloading models

src/streamlit_app.py

@@ -3,6 +3,7 @@ import pickle
 import numpy as np
 import streamlit as st
 import gdown
+import os
 
 # File IDs
 model_id = "1HSQTjJ_hvBBmVJmYUmrkq5T7ubpfDwzF"
@@ -14,15 +15,19 @@ top_country_url = f"https://drive.google.com/uc?id={top_country_id}"
 
 @st.cache_resource
 def load_model():
-    gdown.download(model_url, "best_rf_model.pkl", quiet=False)
-    with open("best_rf_model.pkl", "rb") as f:
+    model_path = "best_rf_model.pkl"
+    if not os.path.exists(model_path):
+        gdown.download(model_url, model_path, quiet=False)
+    with open(model_path, "rb") as f:
         return pickle.load(f)
 
 
 @st.cache_resource
 def load_top_country():
-    gdown.download(top_country_url, "top_country.pkl", quiet=False)
-    with open("top_country.pkl", "rb") as f:
+    country_path = "top_country.pkl"
+    if not os.path.exists(country_path):
+        gdown.download(top_country_url, country_path, quiet=False)
+    with open(country_path, "rb") as f:
         return pickle.load(f)
 
 
@@ -31,7 +36,8 @@ top_country = load_top_country()
 
 st.set_page_config(page_title="Hotel Booking Prediction", layout="wide")
 
-st.markdown("""
+st.markdown(
+    """
 <div style="
     background-color: white;
     padding: 50px;
@@ -51,14 +57,16 @@ st.markdown("""
         Fill in the form below to predict hotel booking!
     </p>
 </div>
-""", unsafe_allow_html=True)
+""",
+    unsafe_allow_html=True,
+)
 
 st.write("")
 st.write("")
 
 with st.form(key="hotel_bookings"):
     col1, col2 = st.columns(2)
-    
+
     with col1:
         name = st.selectbox("Hotel Type", ("city_hotel", "resort_hotel"), index=0)
         lead = st.number_input(
@@ -88,7 +96,7 @@ with st.form(key="hotel_bookings"):
             ),
             index=0,
         )
-    
+
     with col2:
         arrival_week = st.number_input(
             "Arrival Weeks",
@@ -108,29 +116,29 @@ with st.form(key="hotel_bookings"):
         )
 
     submitted = st.form_submit_button("Predict", use_container_width=True)
-    
+
     if submitted:
         # Prepare data for prediction
         data = {
-            'hotel': name,
-            'lead_time': lead,
-            'arrival_date_year': int(arrival_year),
-            'arrival_date_month': arrival_month,
-            'arrival_date_week_number': arrival_week,
-            'arrival_date_day_of_month': arrival_day
+            "hotel": name,
+            "lead_time": lead,
+            "arrival_date_year": int(arrival_year),
+            "arrival_date_month": arrival_month,
+            "arrival_date_week_number": arrival_week,
+            "arrival_date_day_of_month": arrival_day,
         }
-        
+
         df = pd.DataFrame([data])
-        
+
         try:
             prediction = model.predict(df)
-            
+
             st.success("Prediction Complete!")
-            
+
             if prediction[0] == 1:
                 st.error("⚠️ This booking is likely to be CANCELLED")
             else:
                 st.success("✅ This booking is likely to be CONFIRMED")
-                
+
         except Exception as e:
             st.error(f"Error making prediction: {str(e)}")