Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error
+import numpy as np
+
+# Load sample data (replace with real pollution dataset)
+def load_sample_data():
+    data = {
+        "Date": pd.date_range(start="2023-01-01", periods=100, freq="D"),
+        "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),
+    }
+    return pd.DataFrame(data)
+
+# Train a simple model to predict AQI
+def train_model(data):
+    X = data[["Temperature", "Humidity"]]
+    y = data["AQI"]
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    model = RandomForestRegressor(n_estimators=100, random_state=42)
+    model.fit(X_train, y_train)
+
+    y_pred = model.predict(X_test)
+    mse = mean_squared_error(y_test, y_pred)
+    return model, mse
+
+# Predict AQI for a given input
+def predict_aqi(model, temperature, humidity):
+    prediction = model.predict([[temperature, humidity]])
+    return round(prediction[0], 2)
+
+# Visualization of historical trends
+def plot_trends(data):
+    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")
+    plt.xlabel("Date")
+    plt.ylabel("Values")
+    plt.legend()
+    plt.grid()
+    plt.tight_layout()
+
+    # Save the plot to a file
+    plt.savefig("trends.png")
+    return "trends.png"
+
+# Load data and train model
+data = load_sample_data()
+model, mse = train_model(data)
+
+# Streamlit app
+st.title("\ud83c\udf0d Pollution Data Analysis Tool")
+st.markdown(
+    "This app predicts air pollution levels (AQI) based on temperature and humidity. "
+    "It also provides a visualization of historical trends."
+)
+
+# Sidebar inputs
+st.sidebar.header("Input Parameters")
+temperature = st.sidebar.slider("Temperature (\u00b0C)", 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}")
+
+# Historical trends visualization
+st.subheader("Historical Data Trends")
+trends_image = plot_trends(data)
+st.image(trends_image)
+
+# Model performance
+st.sidebar.subheader("Model Performance")
+st.sidebar.write(f"Mean Squared Error: {mse:.2f}")