Update app.py

app.py

@@ -22,14 +22,11 @@ st.sidebar.header("Input Parameters")
 budget = st.sidebar.number_input("Total Budget (in $1000s):", min_value=10, max_value=1000, step=10)
 priority_area = st.sidebar.selectbox("Priority Area:", ["Rural", "Urban", "Suburban"])
 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)
 
 # Display Dataset Options
-data_to_view = st.sidebar.selectbox("Select Dataset to View:", ["Network Insights"])
-
-# Display Selected Dataset
-if data_to_view == "Network Insights":
-    st.subheader("Network Insights Dataset")
-    st.dataframe(network_insights)
+data_to_view = st.sidebar.selectbox("Select Dataset to View:", ["Network Insights", "Filtered Terrain Data"])
 
 # Terrain and Connectivity Analysis Section
 st.header("Terrain and Connectivity Analysis")
@@ -42,6 +39,7 @@ 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)
     }
     return pd.DataFrame(data)
 
@@ -49,12 +47,25 @@ 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["Signal Strength (dBm)"] >= signal_threshold) & 
+    (terrain_data["Cost ($1000s)"] <= budget) & 
+    (terrain_data["Priority Area"] == priority_area)
 ]
 
-# Display Filtered Results
-st.write("Filtered Results Based on Inputs:")
-st.dataframe(filtered_data)
+# Add Composite Score for Ranking
+filtered_data["Composite Score"] = (
+    (1 - terrain_weight) * filtered_data["Signal Strength (dBm)"] + 
+    (terrain_weight) * (10 - filtered_data["Terrain Difficulty (0-10)"]) - 
+    (cost_weight) * filtered_data["Cost ($1000s)"]
+)
+
+# Display Selected Dataset
+if data_to_view == "Network Insights":
+    st.subheader("Network Insights Dataset")
+    st.dataframe(network_insights)
+elif data_to_view == "Filtered Terrain Data":
+    st.subheader("Filtered Terrain Data")
+    st.dataframe(filtered_data)
 
 # Visualization
 fig = px.scatter(
@@ -77,8 +88,8 @@ st.header("Deployment Recommendations")
 def recommend_deployment(data):
     if data.empty:
         return "No viable deployment regions within the specified parameters."
-    best_region = data.loc[data["Signal Strength (dBm)"].idxmax()]
-    return f"Recommended Region: {best_region['Region']} with Signal Strength: {best_region['Signal Strength (dBm)']} dBm and Estimated Cost: ${best_region['Cost ($1000s)']}k"
+    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"
 
 recommendation = recommend_deployment(filtered_data)
 st.subheader(recommendation)