Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,6 +1,6 @@
 # app.py
-# NYC StayWise - Airbnb Price Predictor (Fixed: Synthetic Data)
-# 100% Self-Contained • No External URLs • Deploy-Ready
+# NYC StayWise - Airbnb Price Predictor (Fixed: Probabilities Sum to 1)
+# 100% Self-Contained • Synthetic NYC Data • Zero Errors
 
 import streamlit as st
 import pandas as pd
@@ -61,35 +61,40 @@ st.markdown("""
 </style>
 """, unsafe_allow_html=True)
 
-# ------------------ Generate Synthetic Airbnb Data (Self-Contained) ------------------
+# ------------------ Generate Synthetic Airbnb Data (Fixed Probabilities) ------------------
 @st.cache_data
 def generate_airbnb_data(n_samples=10000):
     np.random.seed(42)  # Reproducible
     
-    # Realistic NYC neighborhoods and properties
-    neighborhoods = np.random.choice([
-        'Manhattan', 'Brooklyn', 'Queens', 'Bronx', 'Staten Island'
-    ], n_samples, p=[0.4, 0.35, 0.15, 0.08, 0.02])
+    # Helper to normalize probs to sum exactly 1.0
+    def safe_choice(choices, probs):
+        if probs is not None:
+            probs = np.array(probs)
+            probs = probs / probs.sum()  # Normalize to exactly 1.0
+        return np.random.choice(choices, n_samples, p=probs)
     
-    room_types = np.random.choice([
-        'Entire home/apt', 'Private room', 'Shared room'
-    ], n_samples, p=[0.6, 0.35, 0.05])
+    # Realistic NYC neighborhoods
+    neighborhoods = safe_choice(['Manhattan', 'Brooklyn', 'Queens', 'Bronx', 'Staten Island'], [0.4, 0.35, 0.15, 0.08, 0.02])
     
-    property_types = np.random.choice([
-        'Apartment', 'House', 'Condominium', 'Loft', 'Townhouse', 'Other'
-    ], n_samples, p=[0.7, 0.1, 0.1, 0.05, 0.03, 0.02])
+    # Room types
+    room_types = safe_choice(['Entire home/apt', 'Private room', 'Shared room'], [0.6, 0.35, 0.05])
     
-    # Numeric features with realistic ranges
-    accommodates = np.random.choice([1, 2, 3, 4, 5, 6, 8, 10, 16], n_samples, p=[0.1, 0.2, 0.2, 0.25, 0.15, 0.08, 0.02, 0.01, 0.01])
-    bathrooms = np.random.uniform(0.5, 4.0, n_samples).round(1)
-    bedrooms = np.random.choice([0, 1, 2, 3, 4, 5, 6], n_samples, p=[0.1, 0.4, 0.3, 0.15, 0.04, 0.008, 0.002])
-    beds = np.random.choice([1, 2, 3, 4, 5, 6, 8, 10], n_samples, p=[0.3, 0.3, 0.2, 0.1, 0.05, 0.03, 0.01, 0.01])
+    # Property types
+    property_types = safe_choice(['Apartment', 'House', 'Condominium', 'Loft', 'Townhouse', 'Other'], [0.7, 0.1, 0.1, 0.05, 0.03, 0.02])
     
-    # Price generation: Base on features (Manhattan + entire home = premium)
+    # Accommodates (FIXED: Normalized probs)
+    accommodates = safe_choice([1, 2, 3, 4, 5, 6, 8, 10, 16], [0.1, 0.2, 0.2, 0.25, 0.15, 0.08, 0.02, 0.01, 0.01])
+    
+    # Other numerics
+    bathrooms = np.clip(np.random.uniform(0.5, 4.0, n_samples), 0.5, 4.0).round(1)
+    bedrooms = safe_choice([0, 1, 2, 3, 4, 5, 6], [0.1, 0.4, 0.3, 0.15, 0.04, 0.008, 0.002])
+    beds = safe_choice([1, 2, 3, 4, 5, 6, 8, 10], [0.3, 0.3, 0.2, 0.1, 0.05, 0.03, 0.01, 0.01])
+    
+    # Price generation: Realistic logic
     base_price = np.where(neighborhoods == 'Manhattan', 150, 80)
     base_price += np.where(room_types == 'Entire home/apt', 100, np.where(room_types == 'Private room', 50, 20))
     base_price += accommodates * 15 + bedrooms * 40 + beds * 20 + bathrooms * 30
-    base_price += np.random.normal(0, 50, n_samples)  # Noise for realism
+    base_price += np.random.normal(0, 50, n_samples)  # Noise
     price = np.clip(base_price, 20, 1000).round(0).astype(int)
     
     df = pd.DataFrame({
@@ -100,8 +105,8 @@ def generate_airbnb_data(n_samples=10000):
         'beds': beds,
         'neighbourhood_group_cleansed': neighborhoods,
         'property_type': property_types,
-        'cleaning_fee': np.random.choice([True, False], n_samples, p=[0.7, 0.3]),
-        'instant_bookable': np.random.choice([True, False], n_samples, p=[0.6, 0.4]),
+        'cleaning_fee': safe_choice([True, False], [0.7, 0.3]),
+        'instant_bookable': safe_choice([True, False], [0.6, 0.4]),
         'price': price
     })