Create app.py
Browse files
app.py
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from geopy.geocoders import OpenCageGeocode
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import streamlit as st
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import pandas as pd
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import numpy as np
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import plotly.express as px
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from datasets import load_dataset
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import folium
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from streamlit_folium import st_folium
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# Initialize geolocator with OpenCage API
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API_KEY = "YOUR_OPENCAGE_API_KEY" # Replace with your OpenCage API key
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geolocator = OpenCageGeocode(API_KEY)
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# Hugging Face Datasets
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@st.cache_data
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def load_data():
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network_insights = load_dataset("infinite-dataset-hub/5GNetworkOptimization", split="train")
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return network_insights.to_pandas()
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# Load Datasets
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network_insights = load_data()
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# Title
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st.title("Smart Network Infrastructure Planner")
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st.sidebar.header("Input Parameters")
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# User Inputs from Sidebar
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budget = st.sidebar.number_input("Total Budget (in $1000s):", min_value=10, max_value=1000, step=10)
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priority_area = st.sidebar.selectbox("Priority Area:", ["Rural", "Urban", "Suburban"])
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signal_threshold = st.sidebar.slider("Signal Strength Threshold (dBm):", min_value=-120, max_value=-30, value=-80)
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terrain_weight = st.sidebar.slider("Terrain Difficulty Weight:", min_value=0.0, max_value=1.0, value=0.5)
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cost_weight = st.sidebar.slider("Cost Weight:", min_value=0.0, max_value=1.0, value=0.5)
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include_human_readable = st.sidebar.checkbox("Include Human-Readable Info", value=True)
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# Display Dataset Options
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data_to_view = st.sidebar.selectbox("Select Dataset to View:", ["Network Insights", "Filtered Terrain Data"])
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# Terrain and Connectivity Analysis Section
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st.header("Terrain and Connectivity Analysis")
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# Simulate Terrain Data
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def generate_terrain_data():
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np.random.seed(42)
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data = {
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"Region": [f"Region-{i}" for i in range(1, 11)],
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"Latitude": np.random.uniform(30.0, 50.0, size=10),
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"Longitude": np.random.uniform(-120.0, -70.0, size=10),
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"Terrain Difficulty (0-10)": np.random.randint(1, 10, size=10),
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"Signal Strength (dBm)": np.random.randint(-120, -30, size=10),
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"Cost ($1000s)": np.random.randint(50, 200, size=10),
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"Priority Area": np.random.choice(["Rural", "Urban", "Suburban"], size=10),
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"Description": [
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"Flat area with minimal obstacles",
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"Hilly terrain, moderate construction difficulty",
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"Dense urban area with high costs",
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"Suburban area, balanced terrain",
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"Mountainous region, challenging setup",
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"Remote rural area, sparse population",
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"Coastal area, potential for high signal interference",
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"Industrial zone, requires robust infrastructure",
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"Dense forest region, significant signal attenuation",
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"Open plains, optimal for cost-effective deployment"
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]
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}
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return pd.DataFrame(data)
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terrain_data = generate_terrain_data()
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# Reverse Geocoding Function
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def get_location_name(lat, lon):
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try:
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location = geolocator.reverse((lat, lon), language="en")
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if location and "formatted" in location:
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return location["formatted"]
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return "Location not found"
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except Exception as e:
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return f"Error: {str(e)}"
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# Add Location Name to Filtered Data
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if include_human_readable:
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filtered_data = terrain_data[
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(terrain_data["Signal Strength (dBm)"] >= signal_threshold) &
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(terrain_data["Cost ($1000s)"] <= budget) &
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(terrain_data["Priority Area"] == priority_area)
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]
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filtered_data["Location Name"] = filtered_data.apply(
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lambda row: get_location_name(row["Latitude"], row["Longitude"]), axis=1
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)
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else:
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filtered_data = terrain_data[
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(terrain_data["Signal Strength (dBm)"] >= signal_threshold) &
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(terrain_data["Cost ($1000s)"] <= budget) &
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(terrain_data["Priority Area"] == priority_area)
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]
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# Add Composite Score for Ranking
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filtered_data["Composite Score"] = (
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(1 - terrain_weight) * filtered_data["Signal Strength (dBm)"] +
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(terrain_weight) * (10 - filtered_data["Terrain Difficulty (0-10)"]) -
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(cost_weight) * filtered_data["Cost ($1000s)"]
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)
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# Display Selected Dataset
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if data_to_view == "Network Insights":
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st.subheader("Network Insights Dataset")
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st.dataframe(network_insights)
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elif data_to_view == "Filtered Terrain Data":
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st.subheader("Filtered Terrain Data")
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columns_to_display = [
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"Region", "Location Name", "Priority Area", "Signal Strength (dBm)",
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"Cost ($1000s)", "Terrain Difficulty (0-10)", "Description", "Composite Score"
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] if include_human_readable else [
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"Region", "Priority Area", "Signal Strength (dBm)", "Cost ($1000s)", "Terrain Difficulty (0-10)", "Description", "Composite Score"
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]
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st.dataframe(filtered_data[columns_to_display])
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# Map Visualization
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st.header("Geographical Map of Regions")
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if not filtered_data.empty:
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map_center = [filtered_data["Latitude"].mean(), filtered_data["Longitude"].mean()]
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region_map = folium.Map(location=map_center, zoom_start=6)
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for _, row in filtered_data.iterrows():
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folium.Marker(
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location=[row["Latitude"], row["Longitude"]],
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popup=(f"<b>Region:</b> {row['Region']}<br>"
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f"<b>Location:</b> {row.get('Location Name', 'N/A')}<br>"
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f"<b>Description:</b> {row['Description']}<br>"
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f"<b>Signal Strength:</b> {row['Signal Strength (dBm)']} dBm<br>"
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f"<b>Cost:</b> ${row['Cost ($1000s)']}k<br>"
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f"<b>Terrain Difficulty:</b> {row['Terrain Difficulty (0-10)']}"),
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icon=folium.Icon(color="blue", icon="info-sign")
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).add_to(region_map)
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st_folium(region_map, width=700, height=500)
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else:
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st.write("No regions match the selected criteria.")
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# Visualization
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fig = px.scatter(
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filtered_data,
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x="Cost ($1000s)",
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y="Signal Strength (dBm)",
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size="Terrain Difficulty (0-10)",
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color="Region",
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title="Signal Strength vs. Cost",
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labels={
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"Cost ($1000s)": "Cost in $1000s",
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"Signal Strength (dBm)": "Signal Strength in dBm",
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},
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)
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st.plotly_chart(fig)
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# Recommendation Engine
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st.header("Deployment Recommendations")
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+
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def recommend_deployment(data):
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if data.empty:
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return "No viable deployment regions within the specified parameters."
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best_region = data.loc[data["Composite Score"].idxmax()]
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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')}"
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+
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recommendation = recommend_deployment(filtered_data)
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st.subheader(recommendation)
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+
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# Footer
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167 |
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st.sidebar.markdown("---")
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st.sidebar.markdown(
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"**Developed for Hackathon using Hugging Face Infinite Dataset Hub**\n\n[Visit Hugging Face](https://huggingface.co)"
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+
)
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