Update app.py

app.py

@@ -149,27 +149,66 @@ with gr.Blocks(css="""
         font-weight: 600;
         text-align: center;
     }
+    
+    .app-subtitle {
+        color: rgba(255, 255, 255, 0.8);
+        font-size: 1.2rem;
+        margin-bottom: 15px;
+    } 
+
 """) 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("Explore top digital nomad locations around the world. The bars in numeric columns indicate relative values - longer bars are better!")
+    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():
-        country_dropdown = gr.Dropdown(
-            choices=get_country_with_emoji("Country"),
-            value="✈️ All",
-            label="🌏 Filter by Country"
-        )
+        with gr.Column(scale=1):
+            # Group all sliders together
+            cost_slider = gr.Slider(
+                minimum=500,
+                maximum=4000,
+                value=4000,
+                step=100,
+                label="💰 Maximum Monthly Cost of Living (USD)"
+            )
+            
+            min_internet = gr.Slider(
+                minimum=0,
+                maximum=400,
+                value=0,
+                step=10,
+                label="🌐 Minimum Internet Speed (Mbps)"
+            )
+            
+            min_quality = gr.Slider(
+                minimum=5,
+                maximum=10,
+                value=5,
+                step=0.1,
+                label="⭐ Minimum Quality of Life"
+            )
         
-        cost_slider = gr.Slider(
-            minimum=500,
-            maximum=4000,
-            value=4000,
-            step=100,
-            label="💰 Maximum Monthly Cost of Living (USD)"
-        )
-    
+        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"
+            )
+            
+            # Group all checkboxes together
+            visa_filter = gr.CheckboxGroup(
+                choices=["Has Digital Nomad Visa", "Visa Length ≥ 12 Months"],
+                label="🛂 Visa Requirements"
+            )
+            
+            special_features = gr.CheckboxGroup(
+                choices=["Coastal Cities", "Cultural Hotspots", "Affordable (<$1000/month)"],
+                label="✨ Special Features"
+            )
     
     data_table = gr.Dataframe(
         value=styled_df,
@@ -208,7 +247,170 @@ with gr.Blocks(css="""
         
         return style_dataframe(filtered_df)
     
-    country_dropdown.change(process_country_filter, [country_dropdown, cost_slider], data_table)
-    cost_slider.change(process_country_filter, [country_dropdown, cost_slider], data_table)
+    # Define advanced filters function
+    def apply_advanced_filters(country, cost, min_internet_speed, min_qol, visa_reqs, features):
+        # Process country filter
+        if country and country.startswith("✈️ All"):
+            country = "All"
+        else:
+            for emoji_code in ["🇧🇷", "🇭🇺", "🇺🇾", "🇵🇹", "🇬🇪", "🇹🇭", "🇦🇪", "🇪🇸", "🇮🇹", "🇨🇦", "🇨🇴", "🇲🇽", "🇯🇵", "🇰🇷"]:
+                if country and emoji_code in country:
+                    country = country.split(" ", 1)[1]
+                    break
+        
+        filtered_df = df.copy()
+        
+        # Basic filters (country and cost)
+        if country and country != "All":
+            filtered_df = filtered_df[filtered_df["Country"] == country]
+        
+        if cost < df["Monthly Cost Living (USD)"].max():
+            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]
+        
+        # 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)
+            filtered_df = filtered_df[mask]
+        
+        if "Cultural Hotspots" in features:
+            cultural_keywords = ["cultur", "art", "histor", "heritage"]
+            mask = filtered_df["Key Feature"].str.contains("|".join(cultural_keywords), case=False, na=False)
+            filtered_df = filtered_df[mask]
+        
+        if "Affordable (<$1000/month)" in features:
+            filtered_df = filtered_df[filtered_df["Monthly Cost Living (USD)"] < 1000]
+        
+        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], 
+        data_table
+    )
+    cost_slider.change(
+        apply_advanced_filters, 
+        [country_dropdown, cost_slider, min_internet, min_quality, visa_filter, special_features], 
+        data_table
+    )
+    min_internet.change(
+        apply_advanced_filters, 
+        [country_dropdown, cost_slider, min_internet, min_quality, visa_filter, special_features], 
+        data_table
+    )
+    min_quality.change(
+        apply_advanced_filters, 
+        [country_dropdown, cost_slider, min_internet, min_quality, visa_filter, special_features], 
+        data_table
+    )
+    visa_filter.change(
+        apply_advanced_filters, 
+        [country_dropdown, cost_slider, min_internet, min_quality, visa_filter, special_features], 
+        data_table
+    )
+    special_features.change(
+        apply_advanced_filters, 
+        [country_dropdown, cost_slider, min_internet, min_quality, visa_filter, special_features], 
+        data_table
+    )
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("### 🧳 Digital Nomad Tips")
+            gr.Markdown("- Look for places with digital nomad visas for longer stays  \n"
+                       "- Consider internet speed if you need to attend video meetings  \n"
+                       "- Balance cost of living with quality of life for the best experience  \n"
+                       "- Some newer nomad destinations may have incomplete data")
+    
+  
+    gr.Markdown("### 🎯 Find Your Ideal Destination")
+    with gr.Row():
+        with gr.Column():
+            priority = gr.CheckboxGroup(
+                ["Best Quality of Life", "Fastest Internet", "Most Affordable", "Balance of All Factors"],
+                label="What are Your Priorities?",
+                value=["Balance of All Factors"]
+            )
+            
+            find_btn = gr.Button("Find My Ideal Destination", variant="primary")
+            
+            recommendation = gr.Textbox(label="Recommended Location", lines=3)
+            
+            def recommend_location(priorities, max_budget):
+                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
+                    budget_warning = "⚠️ No cities match your budget. Showing best options regardless of cost.\n\n"
+                
+                recommendations = []
+                
+                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"
+                    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"
+                    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"
+                    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)
+                    
+                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)
+                    
+                    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"
+                    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)
+                
+                return budget_warning + "\n\n".join(recommendations)
+            
+            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()