Create app.py

app.py

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+# Install necessary libraries before running
+# !pip install geopandas pycaret prophet matplotlib streamlit folium requests streamlit-folium
+
+import streamlit as st
+import pandas as pd
+import numpy as np
+import geopandas as gpd
+import matplotlib.pyplot as plt
+from prophet import Prophet
+from streamlit_folium import folium_static
+import folium
+import os
+
+# Function to download the naturalearth_lowres dataset
+def download_naturalearth_lowres():
+    url = "https://naturalearth.s3.amazonaws.com/110m_cultural/ne_110m_admin_0_countries.zip"
+    zip_path = "ne_110m_admin_0_countries.zip"
+    extracted_folder = "ne_110m_admin_0_countries"
+    shapefile_path = os.path.join(extracted_folder, "ne_110m_admin_0_countries.shp")
+
+    if not os.path.exists(shapefile_path):
+        import requests
+        import zipfile
+
+        # Download the zip file
+        r = requests.get(url)
+        with open(zip_path, "wb") as f:
+            f.write(r.content)
+
+        # Extract the zip file
+        with zipfile.ZipFile(zip_path, "r") as zip_ref:
+            zip_ref.extractall(extracted_folder)
+
+    return shapefile_path
+
+# Load Geospatial Data
+@st.cache_data
+def load_geospatial_data():
+    shapefile_path = download_naturalearth_lowres()
+    world = gpd.read_file(shapefile_path)
+    underserved_region = world[world['CONTINENT'] == 'Africa']  # Updated column name for compatibility
+    underserved_locations = gpd.GeoDataFrame({
+        "Location": ["Region A", "Region B", "Region C"],
+        "Latitude": [-1.2921, 1.2921, -3.2921],
+        "Longitude": [36.8219, 37.8219, 38.8219]
+    }, geometry=gpd.points_from_xy([36.8219, 37.8219, 38.8219], [-1.2921, 1.2921, -3.2921]))
+    return underserved_region, underserved_locations
+
+@st.cache_data
+def load_demand_data():
+    data = {
+        "date": pd.date_range(start="2022-01-01", periods=24, freq="M"),
+        "demand": np.random.randint(50, 200, size=24)
+    }
+    demand_df = pd.DataFrame(data)
+    return demand_df
+
+underserved_region, underserved_locations = load_geospatial_data()
+demand_df = load_demand_data()
+
+# Streamlit App Title
+st.title("AI-Driven Network Management for Underserved Regions")
+
+# Sidebar for Navigation
+st.sidebar.title("Navigation")
+module = st.sidebar.radio("Choose a Module:", ["Overview", "Demand Forecasting", "Underserved Regions"])
+
+# Overview
+if module == "Overview":
+    st.subheader("Welcome to the AI-Driven Network Management App!")
+    st.write("""
+        This app provides AI-powered tools to optimize network planning and management for underserved regions.
+
+        Modules:
+        - **Demand Forecasting:** Predict demand for internet services over time.
+        - **Underserved Regions:** Identify underserved regions and visualize their locations.
+    """)
+
+# Demand Forecasting Module
+elif module == "Demand Forecasting":
+    st.subheader("Demand Forecasting")
+
+    # Prepare data for Prophet
+    demand_df.rename(columns={"date": "ds", "demand": "y"}, inplace=True)
+
+    # Initialize and fit Prophet model
+    model = Prophet()
+    model.fit(demand_df)
+
+    # Forecast future demand
+    future = model.make_future_dataframe(periods=12, freq='M')
+    forecast = model.predict(future)
+
+    # Plot forecast
+    st.write("### Historical and Forecasted Demand")
+    fig = model.plot(forecast)
+    st.pyplot(fig)
+
+    st.write("### Forecast Data")
+    st.dataframe(forecast[["ds", "yhat", "yhat_lower", "yhat_upper"]].tail(12))
+
+# Underserved Regions Module
+elif module == "Underserved Regions":
+    st.subheader("Underserved Regions")
+
+    # Show map with underserved regions
+    m = folium.Map(location=[0, 20], zoom_start=3)
+
+    # Add underserved regions
+    for _, row in underserved_locations.iterrows():
+        folium.Marker(
+            location=[row["Latitude"], row["Longitude"]],
+            popup=f"Location: {row['Location']}",
+            icon=folium.Icon(color="red")
+        ).add_to(m)
+
+    st.write("### Geospatial Visualization")
+    folium_static(m, width=700, height=500)
+
+    # Show region data
+    st.write("### Region Details")
+    st.dataframe(underserved_locations.drop(columns="geometry"))