Update app.py

app.py

@@ -2,46 +2,54 @@ import streamlit as st
 import pandas as pd
 import numpy as np
 import plotly.express as px
-import requests
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.model_selection import train_test_split
 
 # ----------------------
-# Load Real-World Data
+# Load Sample Data (Fallback)
 # ----------------------
 @st.cache_data
 def load_giga_school_data():
-    """Load Giga school connectivity data (example CSV structure)"""
-    url = "https://raw.githubusercontent.com/Project-Giga/public-datasets/main/school_connectivity.csv"
-    df = pd.read_csv(url)
+    """Load synthetic school connectivity data"""
+    try:
+        # Try original URL first
+        url = "https://raw.githubusercontent.com/Project-Giga/public-datasets/main/school_connectivity.csv"
+        df = pd.read_csv(url)
+    except:
+        # Fallback to synthetic data
+        st.warning("Using synthetic data - replace with real Giga dataset when available")
+        data = {
+            "school_id": [1, 2, 3, 4, 5],
+            "latitude": [40.7128, 34.0522, 41.8781, 29.7604, 33.7490],
+            "longitude": [-74.0060, -118.2437, -87.6298, -95.3698, -84.3880],
+            "connectivity_score": [45, 72, 38, 65, 82]
+        }
+        df = pd.DataFrame(data)
     return df
 
 @st.cache_data
-def load_ookla_speedtest_data(country_code="US"):
-    """Fetch Ookla speedtest data for public sector networks"""
-    url = f"https://ookla-public-datasets.s3.amazonaws.com/performance/{country_code}.csv"
-    df = pd.read_csv(url)
-    return df[['latitude', 'longitude', 'avg_download_mbps', 'avg_latency_ms']]
+def load_ookla_speedtest_data():
+    """Sample speedtest data"""
+    return pd.DataFrame({
+        'latitude': [40.7128, 34.0522, 41.8781, 29.7604, 33.7490],
+        'longitude': [-74.0060, -118.2437, -87.6298, -95.3698, -84.3880],
+        'avg_download_mbps': [25.3, 45.6, 18.9, 32.1, 55.4],
+        'avg_latency_ms': [45, 32, 68, 51, 28]
+    })
 
 # ----------------------
-# AI Model (Energy Optimization)
+# AI Model Training
 # ----------------------
 def train_model(df):
     # Feature engineering
-    df["hour"] = df["timestamp"].dt.hour
-    df["day_of_week"] = df["timestamp"].dt.dayofweek
-    X = df[["hour", "day_of_week", "latency_ms", "traffic_pct", "avg_download_mbps"]]
-    y = df["energy_kwh"]
-    
-    # Train/test split
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+    df["hour"] = np.random.randint(0, 24, len(df))  # Simulate timestamps
+    X = df[["hour", "avg_latency_ms", "avg_download_mbps", "connectivity_score"]]
+    y = df["energy_kwh"] = np.random.normal(200, 50, len(df))  # Synthetic energy data
     
     # Model training
-    model = RandomForestRegressor(n_estimators=20)
-    model.fit(X_train, y_train)
+    model = RandomForestRegressor(n_estimators=10)
+    model.fit(X, y)
     df["predicted_energy"] = model.predict(X)
-    
-    # Calculate savings potential
     df["savings_kwh"] = df["energy_kwh"] - df["predicted_energy"]
     return df, model.feature_importances_
 
@@ -49,75 +57,44 @@ def train_model(df):
 # Streamlit App
 # ----------------------
 st.set_page_config(page_title="Public Sector Energy Optimizer", layout="wide")
-st.title("🏫 AI for School/Healthcare Network Efficiency")
+st.title("🏫 AI for School Network Efficiency")
 
-# Load datasets
+# Load data
 giga_df = load_giga_school_data()
 ookla_df = load_ookla_speedtest_data()
 merged_df = pd.merge(giga_df, ookla_df, on=["latitude", "longitude"], how="left")
 
-# Generate synthetic energy data (replace with real telemetry if available)
-merged_df["energy_kwh"] = np.random.normal(200, 50, len(merged_df))
-merged_df["traffic_pct"] = np.random.randint(0, 100, len(merged_df))
-merged_df["timestamp"] = pd.date_range("2024-01-01", periods=len(merged_df), freq="H")
-
 # Train model
 df, feature_importances = train_model(merged_df)
 
 # ----------------------
 # Dashboard Sections
 # ----------------------
-tab1, tab2, tab3, tab4 = st.tabs(["📈 Analysis", "🗺️ Map", "🔧 Recommendations", "About"])
+tab1, tab2, tab3 = st.tabs(["📈 Analysis", "🗺️ Map", "About"])
 
 with tab1:
     st.subheader("Energy vs. Connectivity Analysis")
-    
-    # Energy vs. Download Speed
     fig = px.scatter(df, x="avg_download_mbps", y="energy_kwh", 
                      color="savings_kwh", title="Download Speed vs. Energy Use")
     st.plotly_chart(fig, use_container_width=True)
-    
-    # Feature Importance
-    st.subheader("Key Drivers of Energy Consumption")
-    features = ["Hour", "Weekday", "Latency", "Traffic", "Download Speed"]
-    fig = px.bar(x=features, y=feature_importances, labels={"x": "Factor", "y": "Importance"})
-    st.plotly_chart(fig, use_container_width=True)
 
 with tab2:
-    st.subheader("School/Healthcare Facility Map")
-    
-    # Filter inefficient devices
-    df["size"] = np.where(df["savings_kwh"] > 20, 10, 2)  # Highlight high-waste locations
-    
+    st.subheader("School Locations")
     fig = px.scatter_mapbox(df, lat="latitude", lon="longitude", 
-                            color="savings_kwh", size="size",
-                            hover_data=["avg_download_mbps", "energy_kwh"],
-                            mapbox_style="carto-positron",
-                            zoom=2)
+                          color="savings_kwh", size="savings_kwh",
+                          hover_data=["connectivity_score"],
+                          mapbox_style="carto-positron",
+                          zoom=3)
     st.plotly_chart(fig, use_container_width=True)
 
 with tab3:
-    st.subheader("Optimization Priorities")
-    
-    # Priority Schools/Hospitals
-    st.write("### Top 5 High-Impact Facilities")
-    priority_df = df.sort_values("savings_kwh", ascending=False).head(5)
-    st.dataframe(priority_df[["latitude", "longitude", "savings_kwh", "avg_download_mbps"]], 
-                 hide_index=True)
-    
-    # Cost-Benefit Calculator
-    st.write("### Cost Savings Estimator")
-    total_savings = df["savings_kwh"].sum() * 0.25  # Assume $0.25/kWh
-    co2_reduction = df["savings_kwh"].sum() * 0.5   # 0.5 kg CO2 per kWh
-    st.metric("Monthly Cost Savings", f"${total_savings:,.0f}")
-    st.metric("CO₂ Reduction", f"{co2_reduction:,.0f} kg")
-
-with tab4:
     st.write("## About")
     st.markdown("""
-    **Public Sector Network Energy Optimizer**  
-    Built for the AI for Connectivity Hackathon II using:
-    - School connectivity data from [Giga](https://giga.global)  
-    - Network performance data from [Ookla](https://ookla.com/open-data)  
-    - Energy optimization AI models  
+    **Temporary Demo Version**  
+    Currently using synthetic data. To use real data:
+    1. Request access to [Giga School Data](https://giga.global)
+    2. Replace URLs in `load_giga_school_data()`
+    3. Update column names as needed
     """)
+
+st.sidebar.markdown("⚠️ Note: This is a prototype using sample data")