Update app.py

app.py

@@ -7,178 +7,214 @@ 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
-def load_data():
-    network_insights = load_dataset("infinite-dataset-hub/5GNetworkOptimization", split="train")
-    return network_insights.to_pandas()
-
-# Load Datasets
-network_insights = load_data()
-
-# Title Section with Styling
-st.markdown("""
-# 🌍 **Smart Network Infrastructure Planner**
-Effortlessly optimize network infrastructure while accounting for budget, signal strength, terrain challenges, and climate risks.
-""")
-st.sidebar.header("🔧 Input Parameters")
-
-# User Inputs from Sidebar
-with st.sidebar:
-    budget = st.number_input("💰 Total Budget (in $1000s):", min_value=10, max_value=1000, step=10)
-    priority_area = st.selectbox("🌎 Priority Area:", ["Rural", "Urban", "Suburban"])
-    signal_threshold = st.slider("📶 Signal Strength Threshold (dBm):", min_value=-120, max_value=-30, value=-80)
-    terrain_weight = st.slider("🌄 Terrain Difficulty Weight:", min_value=0.0, max_value=1.0, value=0.5)
-    cost_weight = st.slider("💵 Cost Weight:", min_value=0.0, max_value=1.0, value=0.5)
-    climate_risk_weight = st.slider("🌡️ Climate Risk Weight:", min_value=0.0, max_value=1.0, value=0.5)
-    include_human_readable = st.checkbox("🗺️ Include Human-Readable Info", value=True)
-
-# Tabs for Data Display and Analysis
-st.markdown("## 🚀 Insights & Recommendations")
-tabs = st.tabs(["Terrain Analysis", "Filtered Data", "Geographical Map"])
-
-# Simulate Terrain and Climate Risk Data
-def generate_terrain_data():
-    np.random.seed(42)
-    data = {
-        "Region": [f"Region-{i}" for i in range(1, 11)],
-        "Latitude": np.random.uniform(30.0, 50.0, size=10),
-        "Longitude": np.random.uniform(-120.0, -70.0, size=10),
-        "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),
-        "Climate Risk (0-10)": np.random.randint(0, 10, 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)
-
-terrain_data = generate_terrain_data()
-
-# 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"] = (
-    (1 - terrain_weight) * filtered_data["Signal Strength (dBm)"] + 
-    (terrain_weight) * (10 - filtered_data["Terrain Difficulty (0-10)"]) - 
-    (cost_weight) * filtered_data["Cost ($1000s)"] -
-    (climate_risk_weight) * filtered_data["Climate Risk (0-10)"]
+# Top-level menu
+menu = st.sidebar.selectbox(
+    "Select a Section",
+    ["Introduction", "Funding", "Networking", "Content Delivery", "Maintenance"]
 )
 
-# Display Filtered Data in Tab
-with tabs[1]:
-    st.subheader("Filtered Terrain Data")
-    columns_to_display = [
-        "Region", "Location Name", "Priority Area", "Signal Strength (dBm)",
-        "Cost ($1000s)", "Terrain Difficulty (0-10)", "Climate Risk (0-10)", "Description", "Composite Score"
-    ] if include_human_readable else [
-        "Region", "Priority Area", "Signal Strength (dBm)", "Cost ($1000s)", "Terrain Difficulty (0-10)", "Climate Risk (0-10)", "Description", "Composite Score"
-    ]
-    st.dataframe(filtered_data[columns_to_display])
-
-# Map Visualization in Tab
-with tabs[2]:
-    st.subheader("Geographical Map of Regions")
-    if not filtered_data.empty:
-        map_center = [filtered_data["Latitude"].mean(), filtered_data["Longitude"].mean()]
-        region_map = folium.Map(location=map_center, zoom_start=6)
-
-        for _, row in filtered_data.iterrows():
-            folium.Marker(
-                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>"
-                    f"<b>Terrain Difficulty:</b> {row['Terrain Difficulty (0-10)']}<br>"
-                    f"<b>Climate Risk:</b> {row['Climate Risk (0-10)']}"
-                ),
-                icon=folium.Icon(color="blue", icon="info-sign")
-            ).add_to(region_map)
-
-        st_folium(region_map, width=700, height=500)
+# Introduction Section
+if menu == "Introduction":
+    st.title("Welcome to the Smart Network Infrastructure Planner")
+    st.markdown("""
+    This application provides tools and insights to optimize network infrastructure 
+    based on various criteria such as budget, signal strength, terrain challenges, and climate risks.
+    """)
+
+# Funding Section
+elif menu == "Funding":
+    st.title("Funding Overview")
+    st.markdown("""
+    Explore various funding strategies and options to support the development of 
+    smart network infrastructures.
+    """)
+
+# Networking Section
+elif menu == "Networking":
+    # Initialize geolocator
+    geolocator = Nominatim(user_agent="geoapiExercises")
+
+    # Hugging Face Datasets
+    @st.cache_data
+    def load_data():
+        network_insights = load_dataset("infinite-dataset-hub/5GNetworkOptimization", split="train")
+        return network_insights.to_pandas()
+
+    # Load Datasets
+    network_insights = load_data()
+
+    # Title Section with Styling
+    st.markdown("""
+    # 🌍 **Smart Network Infrastructure Planner**
+    Effortlessly optimize network infrastructure while accounting for budget, signal strength, terrain challenges, and climate risks.
+    """)
+    st.sidebar.header("🔧 Input Parameters")
+
+    # User Inputs from Sidebar
+    with st.sidebar:
+        budget = st.number_input("💰 Total Budget (in $1000s):", min_value=10, max_value=1000, step=10)
+        priority_area = st.selectbox("🌎 Priority Area:", ["Rural", "Urban", "Suburban"])
+        signal_threshold = st.slider("📶 Signal Strength Threshold (dBm):", min_value=-120, max_value=-30, value=-80)
+        terrain_weight = st.slider("🌄 Terrain Difficulty Weight:", min_value=0.0, max_value=1.0, value=0.5)
+        cost_weight = st.slider("💵 Cost Weight:", min_value=0.0, max_value=1.0, value=0.5)
+        climate_risk_weight = st.slider("🌡️ Climate Risk Weight:", min_value=0.0, max_value=1.0, value=0.5)
+        include_human_readable = st.checkbox("🗺️ Include Human-Readable Info", value=True)
+
+    # Tabs for Data Display and Analysis
+    st.markdown("## 🚀 Insights & Recommendations")
+    tabs = st.tabs(["Terrain Analysis", "Filtered Data", "Geographical Map"])
+
+    # Simulate Terrain and Climate Risk Data
+    def generate_terrain_data():
+        np.random.seed(42)
+        data = {
+            "Region": [f"Region-{i}" for i in range(1, 11)],
+            "Latitude": np.random.uniform(30.0, 50.0, size=10),
+            "Longitude": np.random.uniform(-120.0, -70.0, size=10),
+            "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),
+            "Climate Risk (0-10)": np.random.randint(0, 10, 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)
+
+    terrain_data = generate_terrain_data()
+
+    # 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:
-        st.write("No regions match the selected criteria.")
-
-# Visualization Tab
-with tabs[0]:
-    st.subheader("Signal Strength vs. Cost")
-    fig = px.scatter(
-        filtered_data,
-        x="Cost ($1000s)",
-        y="Signal Strength (dBm)",
-        size="Terrain Difficulty (0-10)",
-        color="Region",
-        title="Signal Strength vs. Cost",
-        labels={
-            "Cost ($1000s)": "Cost in $1000s",
-            "Signal Strength (dBm)": "Signal Strength in dBm",
-        },
+        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"] = (
+        (1 - terrain_weight) * filtered_data["Signal Strength (dBm)"] + 
+        (terrain_weight) * (10 - filtered_data["Terrain Difficulty (0-10)"]) - 
+        (cost_weight) * filtered_data["Cost ($1000s)"] -
+        (climate_risk_weight) * filtered_data["Climate Risk (0-10)"]
     )
-    st.plotly_chart(fig)
-
-# Recommendation Engine
-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["Composite Score"].idxmax()]
-    return f"**Recommended Region:** {best_region['Region']}  \n" \
-           f"**Composite Score:** {best_region['Composite Score']:.2f}  \n" \
-           f"**Signal Strength:** {best_region['Signal Strength (dBm)']} dBm  \n" \
-           f"**Terrain Difficulty:** {best_region['Terrain Difficulty (0-10)']}  \n" \
-           f"**Climate Risk:** {best_region['Climate Risk (0-10)']}  \n" \
-           f"**Estimated Cost:** ${best_region['Cost ($1000s)']}k  \n" \
-           f"**Description:** {best_region['Description']}  \n" \
-           f"**Location Name:** {best_region.get('Location Name', 'N/A')}"
-
-recommendation = recommend_deployment(filtered_data)
-
-# Display Recommendation
-st.markdown("### 🌟 Final Recommendation")
-st.markdown(recommendation)
+
+    # Display Filtered Data in Tab
+    with tabs[1]:
+        st.subheader("Filtered Terrain Data")
+        columns_to_display = [
+            "Region", "Location Name", "Priority Area", "Signal Strength (dBm)",
+            "Cost ($1000s)", "Terrain Difficulty (0-10)", "Climate Risk (0-10)", "Description", "Composite Score"
+        ] if include_human_readable else [
+            "Region", "Priority Area", "Signal Strength (dBm)", "Cost ($1000s)", "Terrain Difficulty (0-10)", "Climate Risk (0-10)", "Description", "Composite Score"
+        ]
+        st.dataframe(filtered_data[columns_to_display])
+
+    # Map Visualization in Tab
+    with tabs[2]:
+        st.subheader("Geographical Map of Regions")
+        if not filtered_data.empty:
+            map_center = [filtered_data["Latitude"].mean(), filtered_data["Longitude"].mean()]
+            region_map = folium.Map(location=map_center, zoom_start=6)
+
+            for _, row in filtered_data.iterrows():
+                folium.Marker(
+                    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>"
+                           f"<b>Terrain Difficulty:</b> {row['Terrain Difficulty (0-10)']}<br>"
+                           f"<b>Climate Risk:</b> {row['Climate Risk (0-10)']}")
+                ).add_to(region_map)
+
+            st_folium(region_map, width=700, height=500)
+        else:
+            st.write("No regions match the selected criteria.")
+
+    # Visualization Tab
+    with tabs[0]:
+        st.subheader("Signal Strength vs. Cost")
+        fig = px.scatter(
+            filtered_data,
+            x="Cost ($1000s)",
+            y="Signal Strength (dBm)",
+            size="Terrain Difficulty (0-10)",
+            color="Region",
+            title="Signal Strength vs. Cost",
+            labels={
+                "Cost ($1000s)": "Cost in $1000s",
+                "Signal Strength (dBm)": "Signal Strength in dBm",
+            },
+        )
+        st.plotly_chart(fig)
+
+    # Recommendation Engine
+    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["Composite Score"].idxmax()]
+        return f"**Recommended Region:** {best_region['Region']}  \n" \
+               f"**Composite Score:** {best_region['Composite Score']:.2f}  \n" \
+               f"**Signal Strength:** {best_region['Signal Strength (dBm)']} dBm  \n" \
+               f"**Terrain Difficulty:** {best_region['Terrain Difficulty (0-10)']}  \n" \
+               f"**Climate Risk:** {best_region['Climate Risk (0-10)']}  \n" \
+               f"**Estimated Cost:** ${best_region['Cost ($1000s)']}k  \n" \
+               f"**Description:** {best_region['Description']}  \n" \
+               f"**Location Name:** {best_region.get('Location Name', 'N/A')}"
+
+    recommendation = recommend_deployment(filtered_data)
+
+    # Display Recommendation
+    st.markdown("### 🌟 Final Recommendation")
+    st.markdown(recommendation)
+
+# Content Delivery Section
+elif menu == "Content Delivery":
+    st.title("Content Delivery Strategies")
+    st.markdown("""
+    Learn how to effectively deliver content to enhance user engagement and ensure seamless communication.
+    """)
+
+# Maintenance Section
+elif menu == "Maintenance":
+    st.title("System Maintenance")
+    st.markdown("""
+    Discover best practices for maintaining your smart network infrastructure to ensure reliability and longevity.
+    """)
 
 # Footer
 st.sidebar.markdown("---")
 st.sidebar.markdown(
-    "**Developed for Hackathon using Hugging Face Infinite Dataset Hub**\n\n[Visit Hugging Face](https://huggingface.co)")
+    "**Developed for Hackathon using Hugging Face Infinite Dataset Hub**\n\n[Visit Hugging Face](https://huggingface.co)"
+)