remove comments

app.py

@@ -3,35 +3,25 @@ import pandas as pd
 
 from nomad_data import country_emoji_map, data, terrain_emoji_map
 
-# Create dataframe from imported data
 df = pd.DataFrame(data)
 
-# Create styling functions
 def style_quality_of_life(val):
     """Style the Quality of Life column with color gradient from red to green"""
     if pd.isna(val):
-        # Special styling for null/missing values
         return 'background-color: rgba(200, 200, 200, 0.2); color: #999; font-style: italic;'
     
-    # Define min and max values for Quality of Life (typically on a scale of 0-10)
-    min_val = 5.0  # Anything below this will be bright red
-    max_val = 9.0  # Anything above this will be bright green
+    min_val = 5.0 
+    max_val = 9.0  
     
-    # Normalize value between 0 and 1
     normalized = (val - min_val) / (max_val - min_val)
-    # Clamp between 0 and 1
     normalized = max(0, min(normalized, 1))
     
-    # Calculate percentage fill for gradient
     percentage = int(normalized * 100)
     
-    # Create a linear gradient based on the normalized value
     if normalized < 0.5:
-        # Red to yellow gradient
         start_color = f"rgba(255, {int(255 * (normalized * 2))}, 0, 0.3)"
         end_color = "rgba(255, 255, 255, 0)"
     else:
-        # Yellow to green gradient
         start_color = f"rgba({int(255 * (1 - (normalized - 0.5) * 2))}, 255, 0, 0.3)"
         end_color = "rgba(255, 255, 255, 0)"
     
@@ -40,28 +30,20 @@ def style_quality_of_life(val):
 def style_internet_speed(val):
     """Style the Internet Speed column from red (slow) to green (fast)"""
     if pd.isna(val):
-        # Special styling for null/missing values
         return 'background-color: rgba(200, 200, 200, 0.2); color: #999; font-style: italic;'
     
-    # Define min and max values
     min_val = 20   # Slow internet
     max_val = 300  # Fast internet
     
-    # Normalize value between 0 and 1
     normalized = (val - min_val) / (max_val - min_val)
-    # Clamp between 0 and 1
     normalized = max(0, min(normalized, 1))
     
-    # Calculate percentage fill for gradient
     percentage = int(normalized * 100)
     
-    # Create a linear gradient based on the normalized value
     if normalized < 0.5:
-        # Red to yellow gradient
         start_color = f"rgba(255, {int(255 * (normalized * 2))}, 0, 0.3)"
         end_color = "rgba(255, 255, 255, 0)"
     else:
-        # Yellow to green gradient
         start_color = f"rgba({int(255 * (1 - (normalized - 0.5) * 2))}, 255, 0, 0.3)"
         end_color = "rgba(255, 255, 255, 0)"
     
@@ -69,23 +51,17 @@ def style_internet_speed(val):
 
 def style_dataframe(df):
     """Apply styling to the entire dataframe"""
-    # 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
     
-    # Apply styles to specific columns
     styler = styler.applymap(style_quality_of_life, subset=['Quality of Life'])
     styler = styler.applymap(style_internet_speed, subset=['Internet Speed (Mbps)'])
     
-    # Highlight null values in all columns
     styler = styler.highlight_null(props='color: #999; font-style: italic; background-color: rgba(200, 200, 200, 0.2)')
     
-    # Format numeric columns
     styler = styler.format({
         'Quality of Life': lambda x: f'{x:.1f}' if pd.notna(x) else 'Data Not Available',
         'Internet Speed (Mbps)': lambda x: f'{x:.1f}' if pd.notna(x) else 'Data Not Available',
@@ -104,20 +80,16 @@ def filter_data(country, max_cost):
     if country and country != "All":
         filtered_df = filtered_df[filtered_df["Country"] == country]
     
-    # Filter by maximum cost of living (and handle null values)
     if max_cost < df["Monthly Cost Living (USD)"].max():
-        # Include rows where cost is less than max_cost OR cost is null
         cost_mask = (filtered_df["Monthly Cost Living (USD)"] <= max_cost) | (filtered_df["Monthly Cost Living (USD)"].isna())
         filtered_df = filtered_df[cost_mask]
     
     return style_dataframe(filtered_df)
 
-# Function to get unique values for dropdowns with "All" option
 def get_unique_values(column):
     unique_values = ["All"] + sorted(df[column].unique().tolist())
     return unique_values
 
-# Add country emojis for the dropdown
 def get_country_with_emoji(column):
     choices_with_emoji = ["✈️ All"]
     for c in df[column].unique():
@@ -127,7 +99,6 @@ 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()):
@@ -135,7 +106,6 @@ def get_terrain_with_emoji():
             terrains.append(terrain_emoji_map[terrain])
     return terrains
 
-# Initial styled dataframe
 styled_df = style_dataframe(df)
 
 with gr.Blocks(css="""
@@ -168,16 +138,13 @@ with gr.Blocks(css="""
     } 
 
 """) as demo:
-    # Remove header container and directly show title and subtitle with regular markdown
     gr.HTML(elem_classes="title", value="🌍")
     gr.HTML("<img src='https://see.fontimg.com/api/rf5/JpZqa/MWMyNzc2ODk3OTFlNDk2OWJkY2VjYTIzNzFlY2E4MWIudHRm/bm9tYWQgZGVzdGluYXRpb25z/super-feel.png?r=fs&h=130&w=2000&fg=e2e2e2&bg=FFFFFF&tb=1&s=65' alt='Graffiti fonts'></a>")
 
     gr.Markdown("Discover the best places for digital nomads around the globe")
     
-    # Remove the separate row for basic filters and integrate all filters into one section
     with gr.Row():
         with gr.Column(scale=1):
-            # Group all sliders together
             cost_slider = gr.Slider(
                 minimum=500,
                 maximum=4000,
@@ -203,21 +170,18 @@ with gr.Blocks(css="""
             )
         
         with gr.Column(scale=1):
-            # Put country dropdown with the checkboxes
             country_dropdown = gr.Dropdown(
                 choices=get_country_with_emoji("Country"),
                 value="✈️ All",
                 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"],
                 label="🛂 Visa Requirements"
@@ -240,9 +204,7 @@ with gr.Blocks(css="""
         pinned_columns=3
     )
     
-    # Update data when filters change
     def process_country_filter(country, cost):
-        # Remove emoji from country name if present
         if country and country.startswith("✈️ All"):
             country = "All"
         else:
@@ -253,11 +215,9 @@ with gr.Blocks(css="""
                     
         filtered_df = df.copy()
         
-        # Filter by country
         if country and country != "All":
             filtered_df = filtered_df[filtered_df["Country"] == country]
         
-        # Filter by cost with special handling for nulls
         if cost < df["Monthly Cost Living (USD)"].max():
             cost_mask = (filtered_df["Monthly Cost Living (USD)"] <= cost) & (filtered_df["Monthly Cost Living (USD)"].notna())
         
@@ -265,9 +225,7 @@ 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, terrain):
-        # Process country filter
         if country and country.startswith("✈️ All"):
             country = "All"
         else:
@@ -276,7 +234,6 @@ with gr.Blocks(css="""
                     country = country.split(" ", 1)[1]
                     break
         
-        # Process terrain filter
         if terrain and terrain.startswith("✨ All"):
             terrain = "All"
         else:
@@ -287,7 +244,6 @@ with gr.Blocks(css="""
         
         filtered_df = df.copy()
         
-        # Basic filters (country and cost)
         if country and country != "All":
             filtered_df = filtered_df[filtered_df["Country"] == country]
         
@@ -295,27 +251,21 @@ with gr.Blocks(css="""
             cost_mask = (filtered_df["Monthly Cost Living (USD)"] <= cost) & (filtered_df["Monthly Cost Living (USD)"].notna())
             filtered_df = filtered_df[cost_mask]
         
-        # Advanced filters
-        # Internet speed filter
         if min_internet_speed > 0:
             filtered_df = filtered_df[filtered_df["Internet Speed (Mbps)"] >= min_internet_speed]
-        
-        # Quality of life filter
+
         if min_qol > 5:
             filtered_df = filtered_df[filtered_df["Quality of Life"] >= min_qol]
         
-        # Visa filters
         if "Has Digital Nomad Visa" in visa_reqs:
             filtered_df = filtered_df[filtered_df["Digital Nomad Visa"] == "Yes"]
         
         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"]
             mask = filtered_df["Key Feature"].str.contains("|".join(coastal_keywords), case=False, na=False)
@@ -331,7 +281,6 @@ with gr.Blocks(css="""
         
         return style_dataframe(filtered_df)
     
-    # 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, terrain_dropdown], 
@@ -394,10 +343,8 @@ with gr.Blocks(css="""
                 if not priorities:
                     return "Please select at least one priority to get a recommendation."
                 
-                # Filter by budget first
                 budget_filtered_df = df[df["Monthly Cost Living (USD)"] <= max_budget]
                 
-                # If no cities match the budget, use the full dataset but mention it
                 budget_warning = ""
                 if len(budget_filtered_df) == 0:
                     budget_filtered_df = df
@@ -430,9 +377,7 @@ with gr.Blocks(css="""
                     recommendations.append(message)
                     
                 if "Balance of All Factors" in priorities:
-                    # Create a composite score
                     df_temp = budget_filtered_df.copy()
-                    # Normalize and weight each factor
                     df_temp['quality_norm'] = df_temp['Quality of Life'] / 10
                     df_temp['internet_norm'] = df_temp['Internet Speed (Mbps)'] / 400
                     df_temp['cost_norm'] = 1 - (df_temp['Monthly Cost Living (USD)'] / 4000)
@@ -450,7 +395,6 @@ with gr.Blocks(css="""
             
             find_btn.click(recommend_location, inputs=[priority, cost_slider], outputs=recommendation)
             
-            # Also update when budget slider changes
             cost_slider.change(recommend_location, inputs=[priority, cost_slider], outputs=recommendation)
             
 demo.launch()