Update app.py

app.py

@@ -26,6 +26,7 @@ priority_area = st.sidebar.selectbox("Priority Area:", ["Rural", "Urban", "Subur
 signal_threshold = st.sidebar.slider("Signal Strength Threshold (dBm):", min_value=-120, max_value=-30, value=-80)
 terrain_weight = st.sidebar.slider("Terrain Difficulty Weight:", min_value=0.0, max_value=1.0, value=0.5)
 cost_weight = st.sidebar.slider("Cost Weight:", min_value=0.0, max_value=1.0, value=0.5)
+include_human_readable = st.sidebar.checkbox("Include Human-Readable Info", value=True)
 
 # Display Dataset Options
 data_to_view = st.sidebar.selectbox("Select Dataset to View:", ["Network Insights", "Filtered Terrain Data"])
@@ -43,7 +44,19 @@ def generate_terrain_data():
         "Terrain Difficulty (0-10)": np.random.randint(1, 10, size=10),
         "Signal Strength (dBm)": np.random.randint(-120, -30, size=10),
         "Cost ($1000s)": np.random.randint(50, 200, size=10),
-        "Priority Area": np.random.choice(["Rural", "Urban", "Suburban"], size=10)
+        "Priority Area": np.random.choice(["Rural", "Urban", "Suburban"], size=10),
+        "Description": [
+            "Flat area with minimal obstacles",
+            "Hilly terrain, moderate construction difficulty",
+            "Dense urban area with high costs",
+            "Suburban area, balanced terrain",
+            "Mountainous region, challenging setup",
+            "Remote rural area, sparse population",
+            "Coastal area, potential for high signal interference",
+            "Industrial zone, requires robust infrastructure",
+            "Dense forest region, significant signal attenuation",
+            "Open plains, optimal for cost-effective deployment"
+        ]
     }
     return pd.DataFrame(data)
 
@@ -69,7 +82,9 @@ 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)
+    st.dataframe(filtered_data[[
+        "Region", "Priority Area", "Signal Strength (dBm)", "Cost ($1000s)", "Terrain Difficulty (0-10)", "Description", "Composite Score"
+    ]])
 
 # Map Visualization
 st.header("Geographical Map of Regions")
@@ -82,10 +97,12 @@ if not filtered_data.empty:
             location=[row["Latitude"], row["Longitude"]],
             popup=(
                 f"<b>Region:</b> {row['Region']}<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>"
                 f"<b>Terrain Difficulty:</b> {row['Terrain Difficulty (0-10)']}"
             ),
+            icon=folium.Icon(color="blue", icon="info-sign")
         ).add_to(region_map)
 
     st_folium(region_map, width=700, height=500)
@@ -114,7 +131,7 @@ 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"
+    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']}"
 
 recommendation = recommend_deployment(filtered_data)
 st.subheader(recommendation)