Update app.py

app.py

@@ -1,7 +1,7 @@
 import gradio as gr
 import pandas as pd
 
-from nomad_data import country_emoji_map, data
+from nomad_data import country_emoji_map, data, terrain_emoji_map
 
 # Create dataframe from imported data
 df = pd.DataFrame(data)
@@ -72,6 +72,9 @@ def style_dataframe(df):
     # Create a copy to avoid SettingWithCopyWarning
     styled_df = df.copy()
     
+    # Apply terrain emojis
+    styled_df['Terrain'] = styled_df['Terrain'].apply(lambda x: terrain_emoji_map.get(x, x) if pd.notna(x) else x)
+    
     # Convert to Styler object
     styler = styled_df.style
     
@@ -124,6 +127,14 @@ def get_country_with_emoji(column):
             choices_with_emoji.append(c)
     return sorted(choices_with_emoji)
 
+# Add terrain filter function
+def get_terrain_with_emoji():
+    terrains = ["✨ All"]
+    for terrain in sorted(df["Terrain"].unique()):
+        if terrain in terrain_emoji_map:
+            terrains.append(terrain_emoji_map[terrain])
+    return terrains
+
 # Initial styled dataframe
 styled_df = style_dataframe(df)
 
@@ -199,6 +210,13 @@ with gr.Blocks(css="""
                 label="🌏 Filter by Country"
             )
             
+            # Add terrain dropdown
+            terrain_dropdown = gr.Dropdown(
+                choices=get_terrain_with_emoji(),
+                value="✨ All",
+                label="🏞️ Filter by Terrain"
+            )
+            
             # Group all checkboxes together
             visa_filter = gr.CheckboxGroup(
                 choices=["Has Digital Nomad Visa", "Visa Length ≥ 12 Months"],
@@ -212,14 +230,14 @@ with gr.Blocks(css="""
     
     data_table = gr.Dataframe(
         value=styled_df,
-        datatype=["str", "str", "number", "number", "number", "str", "number", "number", "str", "str"],
+        datatype=["str", "str", "str", "number", "number", "number", "str", "number", "number", "str", "str"],
         max_height=600,
         interactive=False,
         show_copy_button=True,
         show_row_numbers=True,
         show_search=True,
         show_fullscreen_button=True,
-        pinned_columns=2
+        pinned_columns=3
     )
     
     # Update data when filters change
@@ -248,7 +266,7 @@ with gr.Blocks(css="""
         return style_dataframe(filtered_df)
     
     # Define advanced filters function
-    def apply_advanced_filters(country, cost, min_internet_speed, min_qol, visa_reqs, features):
+    def apply_advanced_filters(country, cost, min_internet_speed, min_qol, visa_reqs, features, terrain):
         # Process country filter
         if country and country.startswith("✈️ All"):
             country = "All"
@@ -258,6 +276,15 @@ with gr.Blocks(css="""
                     country = country.split(" ", 1)[1]
                     break
         
+        # Process terrain filter
+        if terrain and terrain.startswith("✨ All"):
+            terrain = "All"
+        else:
+            for emoji in ["🏖️", "⛰️", "🏙️", "🏜️", "🌴", "🏝️", "🌲", "🌾"]:
+                if terrain and emoji in terrain:
+                    terrain = terrain.split(" ", 1)[1]
+                    break
+        
         filtered_df = df.copy()
         
         # Basic filters (country and cost)
@@ -284,6 +311,10 @@ with gr.Blocks(css="""
         if "Visa Length ≥ 12 Months" in visa_reqs:
             filtered_df = filtered_df[filtered_df["Visa Length (Months)"] >= 12]
         
+        # Terrain filter
+        if terrain and terrain != "All":
+            filtered_df = filtered_df[filtered_df["Terrain"] == terrain]
+        
         # Special features filters
         if "Coastal Cities" in features:
             coastal_keywords = ["coast", "beach", "sea", "ocean"]
@@ -303,32 +334,37 @@ with gr.Blocks(css="""
     # Connect all filters to use the advanced filter function
     country_dropdown.change(
         apply_advanced_filters, 
-        [country_dropdown, cost_slider, min_internet, min_quality, visa_filter, special_features], 
+        [country_dropdown, cost_slider, min_internet, min_quality, visa_filter, special_features, terrain_dropdown], 
         data_table
     )
     cost_slider.change(
         apply_advanced_filters, 
-        [country_dropdown, cost_slider, min_internet, min_quality, visa_filter, special_features], 
+        [country_dropdown, cost_slider, min_internet, min_quality, visa_filter, special_features, terrain_dropdown], 
         data_table
     )
     min_internet.change(
         apply_advanced_filters, 
-        [country_dropdown, cost_slider, min_internet, min_quality, visa_filter, special_features], 
+        [country_dropdown, cost_slider, min_internet, min_quality, visa_filter, special_features, terrain_dropdown], 
         data_table
     )
     min_quality.change(
         apply_advanced_filters, 
-        [country_dropdown, cost_slider, min_internet, min_quality, visa_filter, special_features], 
+        [country_dropdown, cost_slider, min_internet, min_quality, visa_filter, special_features, terrain_dropdown], 
         data_table
     )
     visa_filter.change(
         apply_advanced_filters, 
-        [country_dropdown, cost_slider, min_internet, min_quality, visa_filter, special_features], 
+        [country_dropdown, cost_slider, min_internet, min_quality, visa_filter, special_features, terrain_dropdown], 
         data_table
     )
     special_features.change(
         apply_advanced_filters, 
-        [country_dropdown, cost_slider, min_internet, min_quality, visa_filter, special_features], 
+        [country_dropdown, cost_slider, min_internet, min_quality, visa_filter, special_features, terrain_dropdown], 
+        data_table
+    )
+    terrain_dropdown.change(
+        apply_advanced_filters, 
+        [country_dropdown, cost_slider, min_internet, min_quality, visa_filter, special_features, terrain_dropdown], 
         data_table
     )
     
@@ -371,21 +407,24 @@ with gr.Blocks(css="""
                 
                 if "Best Quality of Life" in priorities:
                     top_city = budget_filtered_df.sort_values("Quality of Life", ascending=False).iloc[0]
-                    message = f"🌟 {top_city['City']}, {top_city['Country']} - Quality of Life: {top_city['Quality of Life']}\n"
+                    terrain_emoji = terrain_emoji_map.get(top_city['Terrain'], top_city['Terrain']).split()[0]
+                    message = f"{terrain_emoji} {top_city['City']}, {top_city['Country']} - Quality of Life: {top_city['Quality of Life']}\n"
                     message += f"Monthly Cost: ${top_city['Monthly Cost Living (USD)']}\n"
                     message += f"Key Feature: {top_city['Key Feature']}"
                     recommendations.append(message)
                     
                 if "Fastest Internet" in priorities:
                     top_city = budget_filtered_df.sort_values("Internet Speed (Mbps)", ascending=False).iloc[0]
-                    message = f"🚀 {top_city['City']}, {top_city['Country']} - Internet Speed: {top_city['Internet Speed (Mbps)']} Mbps\n"
+                    terrain_emoji = terrain_emoji_map.get(top_city['Terrain'], top_city['Terrain']).split()[0]
+                    message = f"{terrain_emoji} {top_city['City']}, {top_city['Country']} - Internet Speed: {top_city['Internet Speed (Mbps)']} Mbps\n"
                     message += f"Monthly Cost: ${top_city['Monthly Cost Living (USD)']}\n"
                     message += f"Key Feature: {top_city['Key Feature']}"
                     recommendations.append(message)
                     
                 if "Most Affordable" in priorities:
                     top_city = budget_filtered_df.sort_values("Monthly Cost Living (USD)", ascending=True).iloc[0]
-                    message = f"💰 {top_city['City']}, {top_city['Country']} - Monthly Cost: ${top_city['Monthly Cost Living (USD)']}\n"
+                    terrain_emoji = terrain_emoji_map.get(top_city['Terrain'], top_city['Terrain']).split()[0]
+                    message = f"{terrain_emoji} {top_city['City']}, {top_city['Country']} - Monthly Cost: ${top_city['Monthly Cost Living (USD)']}\n"
                     message += f"Quality of Life: {top_city['Quality of Life']}, Internet: {top_city['Internet Speed (Mbps)']} Mbps\n"
                     message += f"Key Feature: {top_city['Key Feature']}"
                     recommendations.append(message)
@@ -401,7 +440,8 @@ with gr.Blocks(css="""
                     df_temp['composite_score'] = (df_temp['quality_norm'] + df_temp['internet_norm'] + df_temp['cost_norm']) / 3
                     top_city = df_temp.sort_values("composite_score", ascending=False).iloc[0]
                     
-                    message = f"✨ {top_city['City']}, {top_city['Country']} - Balanced Choice\n"
+                    terrain_emoji = terrain_emoji_map.get(top_city['Terrain'], top_city['Terrain']).split()[0]
+                    message = f"{terrain_emoji} {top_city['City']}, {top_city['Country']} - Balanced Choice\n"
                     message += f"Quality: {top_city['Quality of Life']}, Internet: {top_city['Internet Speed (Mbps)']} Mbps, Cost: ${top_city['Monthly Cost Living (USD)']}\n"
                     message += f"Key Feature: {top_city['Key Feature']}"
                     recommendations.append(message)