Code Editor for app.py

import streamlit as st
import pandas as pd
import numpy as np
import folium
from streamlit_folium import folium_static
import plotly.express as px
import plotly.graph_objects as go
from datetime import datetime, timedelta
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures

# Page configuration
st.set_page_config(
    page_title="Mazury Hydrological Monitor",
    page_icon="🌊",
    layout="wide"
)

# Title and description
st.title("Mazury Hydrological Monitor")
st.markdown("""
This application monitors and analyzes hydrological changes in the Masurian Lake District 
using satellite imagery and AI-based predictions.
""")

# Sidebar filters
st.sidebar.header("Data Filters")
selected_year = st.sidebar.slider("Select Year", 2015, 2023, 2023)
selected_season = st.sidebar.selectbox(
    "Select Season",
    ["Spring", "Summer", "Autumn", "Winter"]
)
selected_lake = st.sidebar.selectbox(
    "Select Lake",
    ["Śniardwy", "Mamry", "Niegocin", "Mikołajskie", "Tałty"]
)

# Main content organized in tabs
tab1, tab2, tab3, tab4, tab5 = st.tabs([
    "Map View", 
    "Data Analysis", 
    "Trends & Predictions", 
    "Climate Impact", 
    "Report"
])

# Sample data (in real application, this would come from actual sources)
def generate_sample_data():
    dates = pd.date_range(start='2015-01-01', end='2023-12-31', freq='M')
    np.random.seed(42)
    water_levels = 100 + np.random.normal(0, 5, len(dates)) + \
                  np.sin(np.arange(len(dates)) * 2 * np.pi / 12) * 10
    return pd.DataFrame({
        'date': dates,
        'water_level': water_levels,
        'temperature': 15 + np.random.normal(0, 3, len(dates)) + \
                      np.sin(np.arange(len(dates)) * 2 * np.pi / 12) * 10
    })

data = generate_sample_data()

with tab1:
    st.header("Spatial Analysis")
    
    # Create a map centered on Mazury
    m = folium.Map(location=[53.8, 21.6], zoom_start=9)
    
    # Add sample markers for lakes
    lakes = {
        "Śniardwy": [53.7419, 21.7383],
        "Mamry": [54.1311, 21.7828],
        "Niegocin": [53.9131, 21.7492],
        "Mikołajskie": [53.8006, 21.5856],
        "Tałty": [53.9167, 21.5500]
    }
    
    for lake, coords in lakes.items():
        folium.CircleMarker(
            location=coords,
            radius=10,
            popup=lake,
            color='blue' if lake == selected_lake else 'gray',
            fill=True
        ).add_to(m)
    
    # Display the map
    folium_static(m)

with tab2:
    st.header("Data Analysis")
    
    # Create sample time series plot
    fig = px.line(data, x='date', y='water_level', 
                  title=f'Water Level Trends for {selected_lake}')
    st.plotly_chart(fig, use_container_width=True)
    
    # Add some statistics
    col1, col2, col3 = st.columns(3)
    with col1:
        st.metric("Average Water Level", f"{data['water_level'].mean():.1f}m")
    with col2:
        st.metric("Max Level", f"{data['water_level'].max():.1f}m")
    with col3:
        st.metric("Min Level", f"{data['water_level'].min():.1f}m")

with tab3:
    st.header("Trends & Predictions")
    
    # Create a simple prediction model
    X = np.arange(len(data)).reshape(-1, 1)
    y = data['water_level']
    
    model = LinearRegression()
    model.fit(X, y)
    
    # Generate future predictions
    future_dates = pd.date_range(
        start=data['date'].max(), 
        periods=24, 
        freq='M'
    )
    future_X = np.arange(len(data), len(data) + 24).reshape(-1, 1)
    future_predictions = model.predict(future_X)
    
    # Plot historical data and predictions
    fig = go.Figure()
    fig.add_trace(go.Scatter(
        x=data['date'], 
        y=data['water_level'],
        name='Historical Data'
    ))
    fig.add_trace(go.Scatter(
        x=future_dates,
        y=future_predictions,
        name='Predictions',
        line=dict(dash='dash')
    ))
    fig.update_layout(title='Water Level Predictions',
                      xaxis_title='Date',
                      yaxis_title='Water Level (m)')
    st.plotly_chart(fig, use_container_width=True)

with tab4:
    st.header("Climate Impact Simulation")
    
    impact_severity = st.slider(
        "Climate Change Severity",
        min_value=1,
        max_value=5,
        value=3,
        help="1 = Minimal impact, 5 = Severe impact"
    )
    
    # Simple simulation based on severity
    baseline = data['water_level'].mean()
    impact_factor = (impact_severity - 3) * 0.1  # 10% change per severity level
    
    simulated_level = baseline * (1 + impact_factor)
    
    st.metric(
        "Projected Average Water Level",
        f"{simulated_level:.1f}m",
        f"{(simulated_level - baseline):.1f}m"
    )
    
    # Add impact visualization
    impact_data = pd.DataFrame({
        'Category': ['Biodiversity', 'Tourism', 'Agriculture', 'Water Quality'],
        'Impact Score': np.random.normal(impact_severity, 0.5, 4)
    })
    
    fig = px.bar(impact_data, x='Category', y='Impact Score',
                 title='Projected Environmental Impact')
    st.plotly_chart(fig, use_container_width=True)

with tab5:
    st.header("Summary Report")
    
    st.markdown(f"""
    ### Key Findings for {selected_lake}
    
    #### Current Status
    - Average water level: {data['water_level'].mean():.1f}m
    - Annual variation: {data['water_level'].std():.1f}m
    
    #### Predictions
    - Short-term trend: {'Stable' if abs(model.coef_[0]) < 0.1 else 'Increasing' if model.coef_[0] > 0 else 'Decreasing'}
    - Projected change: {(future_predictions[-1] - data['water_level'].iloc[-1]):.1f}m
    
    #### Recommendations
    1. {'Implement water conservation measures' if impact_severity > 3 else 'Monitor water levels regularly'}
    2. {'Develop drought management plans' if impact_severity > 4 else 'Update environmental protection guidelines'}
    3. {'Consider restrictions on water usage' if impact_severity > 3 else 'Promote sustainable tourism'}
    """)
    
    # Export options
    if st.button("Generate PDF Report"):
        st.info("PDF report generation would be implemented here")