Update app.py

app.py

@@ -11,6 +11,7 @@ import numpy as np
 def load_sample_data():
     data = {
         "Date": pd.date_range(start="2023-01-01", periods=100, freq="D"),
+        "Location": np.random.choice(["New York", "Los Angeles", "Chicago", "Houston", "Phoenix"], size=100),
         "AQI": np.random.randint(50, 200, size=100),  # Random AQI values
         "Temperature": np.random.uniform(20, 35, size=100),
         "Humidity": np.random.uniform(30, 80, size=100),
@@ -36,12 +37,13 @@ def predict_aqi(model, temperature, humidity):
     return round(prediction[0], 2)
 
 # Visualization of historical trends
-def plot_trends(data):
+def plot_trends(data, location):
+    filtered_data = data[data["Location"] == location]
     plt.figure(figsize=(10, 6))
-    sns.lineplot(data=data, x="Date", y="AQI", label="AQI")
-    sns.lineplot(data=data, x="Date", y="Temperature", label="Temperature")
-    sns.lineplot(data=data, x="Date", y="Humidity", label="Humidity")
-    plt.title("Historical Data Trends")
+    sns.lineplot(data=filtered_data, x="Date", y="AQI", label="AQI")
+    sns.lineplot(data=filtered_data, x="Date", y="Temperature", label="Temperature")
+    sns.lineplot(data=filtered_data, x="Date", y="Humidity", label="Humidity")
+    plt.title(f"Historical Data Trends for {location}")
     plt.xlabel("Date")
     plt.ylabel("Values")
     plt.legend()
@@ -65,17 +67,18 @@ st.markdown(
 
 # Sidebar inputs
 st.sidebar.header("Input Parameters")
+location = st.sidebar.selectbox("Select Location", data["Location"].unique())
 temperature = st.sidebar.slider("Temperature (°C)", 20, 40, 25)
 humidity = st.sidebar.slider("Humidity (%)", 30, 90, 50)
 
 # Prediction
 st.subheader("Predicted AQI")
 prediction = predict_aqi(model, temperature, humidity)
-st.write(f"The predicted AQI is: {prediction}")
+st.write(f"The predicted AQI for {location} is: {prediction}")
 
 # Historical trends visualization
 st.subheader("Historical Data Trends")
-trends_image = plot_trends(data)
+trends_image = plot_trends(data, location)
 st.image(trends_image)
 
 # Model performance