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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import plotly.express as px | |
| from transformers import pipeline | |
| from datasets import load_dataset | |
| # Hugging Face Datasets | |
| def load_data(): | |
| network_insights = load_dataset("huggingface/infrastructure-insights", split="train") | |
| broadband_forecast = load_dataset("huggingface/broadband-access-forecast", split="train") | |
| return network_insights.to_pandas(), broadband_forecast.to_pandas() | |
| # Load Datasets | |
| network_insights, broadband_forecast = load_data() | |
| # Title | |
| st.title("Smart Network Infrastructure Planner") | |
| st.sidebar.header("Input Parameters") | |
| # User Inputs from Sidebar | |
| 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) | |
| # Display Dataset Options | |
| data_to_view = st.sidebar.selectbox("Select Dataset to View:", ["Network Insights", "Broadband Forecast"]) | |
| # Display Selected Dataset | |
| if data_to_view == "Network Insights": | |
| st.subheader("Network Insights Dataset") | |
| st.dataframe(network_insights) | |
| else: | |
| st.subheader("Broadband Forecast Dataset") | |
| st.dataframe(broadband_forecast) | |
| # Terrain and Connectivity Analysis Section | |
| st.header("Terrain and Connectivity Analysis") | |
| # Simulate Terrain Data | |
| def generate_terrain_data(): | |
| np.random.seed(42) | |
| data = { | |
| "Region": [f"Region-{i}" for i in range(1, 11)], | |
| "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), | |
| } | |
| return pd.DataFrame(data) | |
| 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) | |
| ] | |
| # Display Filtered Results | |
| st.write("Filtered Results Based on Inputs:") | |
| st.dataframe(filtered_data) | |
| # Visualization | |
| 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["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" | |
| recommendation = recommend_deployment(filtered_data) | |
| st.subheader(recommendation) | |
| # Footer | |
| st.sidebar.markdown("---") | |
| st.sidebar.markdown( | |
| "**Developed for Hackathon using Hugging Face Infinite Dataset Hub**\n\n[Visit Hugging Face](https://huggingface.co)") | |