Create app.py

app.py

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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
+@st.cache_data
+
+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)")