changes suggested by Nithish

Author: VirajAgarwal1

Demo.py

@@ -22,13 +22,7 @@ def get_all_airports(_connection):
 
 @st.cache_data       
 def get_routes_for_airports(_connection, selected_airports_df):    
-    airports_faa = "["
-    for i in range(len(selected_airports_df)):
-        if i != len(selected_airports_df) - 1:
-            airports_faa += f'"{(selected_airports_df.iloc[i])["faa"]}", '
-        else:
-            airports_faa += f'"{(selected_airports_df.iloc[i])["faa"]}"'
-    airports_faa += "]"
+    airports_faa = str(selected_airports_df["faa"].to_list()) # Initialize a string to store FAA codes in a list format
     query = f"""
     SELECT * FROM `travel-sample`.`inventory`.`route`
     WHERE (sourceairport IN {airports_faa} AND destinationairport IN {airports_faa});
@@ -41,11 +35,8 @@ def get_routes_for_airports(_connection, selected_airports_df):
 
 def plot_airports_and_routes(airports_df, routes_df):
     fig = go.Figure()
-    airport_coords = {
-        row["faa"]: (row["lat"], row["lon"])
-        for _, row in airports_df.iterrows()
-        if row["faa"] is not None  # Ensure faa is not null
-    }
+    filtered_airports_df = airports_df.dropna(subset=["faa"])  # Remove rows where faa is NaN
+    airport_coords = dict(zip(filtered_airports_df["faa"], zip(filtered_airports_df["lat"], filtered_airports_df["lon"])))
     lats = []
     lons = []
     for _, row in routes_df.iterrows():
@@ -75,7 +66,10 @@ def plot_airports_and_routes(airports_df, routes_df):
         color_discrete_sequence=["red"],  # Color of airport markers
     )
     fig.add_traces(airports_markers.data)
+    fig.update_geos(fitbounds="locations")
     fig.update_layout(
+        map_zoom= 0.5,  # Zoom level
+        showlegend= False,  # Hide legend
         mapbox_style="open-street-map",  
         margin=dict(l=0, r=0, t=50, b=0),  # Remove extra margins
         title="Airports and Flight Routes"
@@ -202,7 +196,11 @@ def get_hotels_near_landmark(_connection, landmark_lat, landmark_lon, max_distan
     return hotels
 
 def create_landmark_map(landmarks, hotels_near_landmark):
-    fig = go.Figure()        
+    fig = go.Figure()   
+         
+    centre = {"lat": 0, "lon": 0}
+    num_points = 0
+    
     for hotel in hotels_near_landmark:
         color = 'red' if hotel.get('distance') <= 3 else 'orange' if hotel.get('distance') <= 6 else 'gold'
         fig.add_trace(go.Scattermap(
@@ -216,6 +214,8 @@ def create_landmark_map(landmarks, hotels_near_landmark):
             hoverinfo='text',
             name=f'Hotel ({color})'
         ))
+        centre = {"lat": centre["lat"] + hotel.get('lat'), "lon": centre["lon"] + hotel.get('lon')}
+        num_points += 1
 
     for landmark in landmarks:
         fig.add_trace(go.Scattermap(
@@ -229,8 +229,16 @@ def create_landmark_map(landmarks, hotels_near_landmark):
             hoverinfo='text',
             name='Landmark'
         ))
+        centre = {"lat": centre["lat"] + landmark.get('lat', 0), "lon": centre["lon"] + landmark.get('lon', 0)}
+        num_points += 1
+    
+    if num_points > 0:
+        centre = {"lat": centre["lat"] / num_points, "lon": centre["lon"] / num_points}
+    fig.update_geos(fitbounds="locations")
 
     fig.update_layout(
+        map_zoom=11,
+        map_center=centre,
         mapbox_style='open-street-map',
         margin=dict(l=0, r=0, t=50, b=0),
         title='Landmarks and Hotels Nearby',
@@ -275,13 +283,7 @@ def get_all_cities(_connection):
 
 @st.cache_data
 def get_all_hotels(_connection, cities):
-    cities_str = "["
-    for i in range(len(cities)):
-        if i != len(cities) - 1:
-            cities_str += f'"{cities[i]}", '
-        else:
-            cities_str += f'"{cities[i]}"'
-    cities_str += "]"
+    cities_str = f"{cities}"
     query = f"""
     SELECT h.*, geo.lat as lat, geo.lon as lon, ARRAY_AVG(ARRAY r.ratings.Overall FOR r IN h.reviews WHEN r.ratings.Overall IS NOT MISSING END) as avg_rating
     FROM `travel-sample`.inventory.hotel h
@@ -318,6 +320,10 @@ def create_hotel_map(hotels_df):
     if 'avg_rating' not in hotels_df.columns:
         hotels_df['avg_rating'] = np.nan  # Add avg_rating column if it doesn't exist
     hotels_df['avg_rating'] = pd.to_numeric(hotels_df['avg_rating'], errors='coerce')
+    centre = {
+        "lat": hotels_df['lat'].mean(),
+        "lon": hotels_df['lon'].mean()
+    }
 
     # Create a column for star ratings
     hotels_df['star_rating'] = hotels_df['avg_rating'].apply(lambda x: '⭐' * int(round(x)) if not np.isnan(x) else 'No rating')
@@ -363,6 +369,8 @@ def create_hotel_map(hotels_df):
     fig.add_traces(no_rating_markers.data)
 
     fig.update_layout(
+        map_zoom=10,
+        map_center=centre,
         mapbox_style="open-street-map",
         margin=dict(l=0, r=0, t=50, b=0),
         title="Hotels (colored by average rating)",
@@ -373,18 +381,11 @@ def create_hotel_map(hotels_df):
         )
     )
 
-    fig.update_layout(
-        mapbox_style="open-street-map",
-        margin=dict(l=0, r=0, t=50, b=0),
-        title="Hotels (colored by average rating)",
-        coloraxis_colorbar_title="Avg Rating"
-    )
-    
     st.plotly_chart(fig, use_container_width=True)
 
 def tab3_visual():
     all_cities = get_all_cities(connection)["city"].tolist()
-    cities = st.multiselect("Select cities", all_cities, default=["Newport", "Birmingham", "London"])
+    cities = st.multiselect("Select cities", all_cities, default=["London"])
     hotels = get_all_hotels(connection, cities)
     create_hotel_map(hotels)