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 prophet import Prophet
+
+# Simulated school network traffic data
+st.title("📡 Smart Network Resource Allocation")
+
+# Load or generate synthetic data
+@st.cache_data
+def load_data():
+    schools = [f"School_{i}" for i in range(1, 6)]
+    time_range = pd.date_range(start="2024-02-01", periods=24, freq="H")
+    data = []
+
+    for school in schools:
+        for time in time_range:
+            bandwidth_usage = np.random.randint(1, 100)  # Random usage in Mbps
+            data.append([school, time, bandwidth_usage])
+
+    df = pd.DataFrame(data, columns=["School", "Timestamp", "Bandwidth_Usage"])
+    return df
+
+df = load_data()
+
+# Display data
+st.subheader("📊 Network Traffic Data")
+st.write(df.tail())
+
+# Prophet Model for Bandwidth Prediction
+st.subheader("📈 Bandwidth Usage Forecasting")
+df_prophet = df.groupby("Timestamp").mean().reset_index()
+df_prophet.columns = ["ds", "y"]
+
+model = Prophet()
+model.fit(df_prophet)
+future = model.make_future_dataframe(periods=5, freq="H")
+forecast = model.predict(future)
+
+fig = px.line(forecast, x="ds", y="yhat", title="Predicted Bandwidth Demand")
+st.plotly_chart(fig)
+
+# Dynamic Bandwidth Allocation
+st.subheader("⚖️ Smart Bandwidth Allocation")
+
+def allocate_bandwidth(predictions, total_bandwidth=500):
+    total_demand = sum(predictions.values())
+    allocation = {school: (predictions[school] / total_demand) * total_bandwidth for school in predictions}
+    return allocation
+
+# Simulated allocation
+predictions = {f"School_{i}": np.random.randint(30, 100) for i in range(1, 6)}
+allocations = allocate_bandwidth(predictions)
+
+st.write("📌 **Predicted Bandwidth Allocation (Mbps):**")
+st.json(allocations)