Update app.py

app.py

@@ -5,6 +5,10 @@ import plotly.express as px
 from datasets import load_dataset
 import folium
 from streamlit_folium import st_folium
+from geopy.geocoders import Nominatim
+
+# Initialize geolocator
+geolocator = Nominatim(user_agent="geoapiExercises")
 
 # Hugging Face Datasets
 @st.cache_data
@@ -61,12 +65,30 @@ def generate_terrain_data():
 
 terrain_data = generate_terrain_data()
 
-# Filter Data Based on User Inputs
-filtered_data = terrain_data[
-    (terrain_data["Signal Strength (dBm)"] >= signal_threshold) & 
-    (terrain_data["Cost ($1000s)"] <= budget) & 
-    (terrain_data["Priority Area"] == priority_area)
-]
+# Reverse Geocoding Function
+def get_location_name(lat, lon):
+    try:
+        location = geolocator.reverse((lat, lon), exactly_one=True)
+        return location.address if location else "Unknown Location"
+    except Exception as e:
+        return "Error: Unable to fetch location"
+
+# Add Location Name to Filtered Data
+if include_human_readable:
+    filtered_data = terrain_data[
+        (terrain_data["Signal Strength (dBm)"] >= signal_threshold) & 
+        (terrain_data["Cost ($1000s)"] <= budget) & 
+        (terrain_data["Priority Area"] == priority_area)
+    ]
+    filtered_data["Location Name"] = filtered_data.apply(
+        lambda row: get_location_name(row["Latitude"], row["Longitude"]), axis=1
+    )
+else:
+    filtered_data = terrain_data[
+        (terrain_data["Signal Strength (dBm)"] >= signal_threshold) & 
+        (terrain_data["Cost ($1000s)"] <= budget) & 
+        (terrain_data["Priority Area"] == priority_area)
+    ]
 
 # Add Composite Score for Ranking
 filtered_data["Composite Score"] = (
@@ -81,9 +103,13 @@ if data_to_view == "Network Insights":
     st.dataframe(network_insights)
 elif data_to_view == "Filtered Terrain Data":
     st.subheader("Filtered Terrain Data")
-    st.dataframe(filtered_data[[
+    columns_to_display = [
+        "Region", "Location Name", "Priority Area", "Signal Strength (dBm)",
+        "Cost ($1000s)", "Terrain Difficulty (0-10)", "Description", "Composite Score"
+    ] if include_human_readable else [
         "Region", "Priority Area", "Signal Strength (dBm)", "Cost ($1000s)", "Terrain Difficulty (0-10)", "Description", "Composite Score"
-    ]])
+    ]
+    st.dataframe(filtered_data[columns_to_display])
 
 # Map Visualization
 st.header("Geographical Map of Regions")
@@ -96,6 +122,7 @@ if not filtered_data.empty:
             location=[row["Latitude"], row["Longitude"]],
             popup=(
                 f"<b>Region:</b> {row['Region']}<br>"
+                f"<b>Location:</b> {row.get('Location Name', 'N/A')}<br>"
                 f"<b>Description:</b> {row['Description']}<br>"
                 f"<b>Signal Strength:</b> {row['Signal Strength (dBm)']} dBm<br>"
                 f"<b>Cost:</b> ${row['Cost ($1000s)']}k<br>"
@@ -130,7 +157,7 @@ def recommend_deployment(data):
     if data.empty:
         return "No viable deployment regions within the specified parameters."
     best_region = data.loc[data["Composite Score"].idxmax()]
-    return f"Recommended Region: {best_region['Region']} with Composite Score: {best_region['Composite Score']:.2f}, Signal Strength: {best_region['Signal Strength (dBm)']} dBm, Terrain Difficulty: {best_region['Terrain Difficulty (0-10)']}, and Estimated Cost: ${best_region['Cost ($1000s)']}k\nDescription: {best_region['Description']}"
+    return f"Recommended Region: {best_region['Region']} with Composite Score: {best_region['Composite Score']:.2f}, Signal Strength: {best_region['Signal Strength (dBm)']} dBm, Terrain Difficulty: {best_region['Terrain Difficulty (0-10)']}, and Estimated Cost: ${best_region['Cost ($1000s)']}k\nDescription: {best_region['Description']}\nLocation Name: {best_region.get('Location Name', 'N/A')}"
 
 recommendation = recommend_deployment(filtered_data)
 st.subheader(recommendation)