app.py

import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
import folium
from streamlit_folium import st_folium
import requests  
# ----------------------------------------------------
# 1. Load data
# ----------------------------------------------------
@st.cache_data
def load_data():
    daily_df = pd.read_csv("daily_data_2013_2024.csv", parse_dates=["date"])
    monthly_df = pd.read_csv("monthly_data_2013_2024.csv")
    return daily_df, monthly_df

daily_data, monthly_data = load_data()

# Pre-define your location dictionary so we can map lat/lon
LOCATIONS = {
    "Karagwe": {"lat": -1.7718, "lon": 30.9876},
    "Masasi":  {"lat": -10.7167, "lon": 38.8000},
    "Igunga":  {"lat": -4.2833, "lon": 33.8833}
}

# ----------------------------------------------------
# 2. Streamlit UI Layout
# ----------------------------------------------------
st.title("Malaria & Dengue Outbreak Analysis (2013–2024)")

st.sidebar.header("Filters & Options")

# Choose disease type
disease_choice = st.sidebar.radio("Select Disease", ["Malaria", "Dengue"])

# Choose data granularity
data_choice = st.sidebar.radio("Data Granularity", ["Monthly", "Daily"])

# Let user filter location(s)
location_list = list(LOCATIONS.keys())
selected_locations = st.sidebar.multiselect("Select Location(s)", location_list, default=location_list)

# For monthly data
if data_choice == "Monthly":
    year_min = int(monthly_data["year"].min())
    year_max = int(monthly_data["year"].max())
    year_range = st.sidebar.slider(
        "Select Year Range", 
        min_value=year_min, 
        max_value=year_max,
        value=(year_min, year_max)
    )
# For daily data
else:
    date_min = daily_data["date"].min()
    date_max = daily_data["date"].max()
    date_range = st.sidebar.date_input(
        "Select Date Range", 
        [date_min, date_max],
        min_value=date_min,
        max_value=date_max
    )

# ----------------------------------------------------
# 3. Filter data based on user input
# ----------------------------------------------------
if data_choice == "Monthly":
    df = monthly_data[monthly_data["location"].isin(selected_locations)].copy()
    # Filter year range
    df = df[(df["year"] >= year_range[0]) & (df["year"] <= year_range[1])]
    # Create a "date" column for monthly data
    df["date"] = pd.to_datetime(df["year"].astype(str) + "-" + df["month"].astype(str) + "-01")

else:
    df = daily_data[daily_data["location"].isin(selected_locations)].copy()
    # Filter date range
    df = df[
        (df["date"] >= pd.to_datetime(date_range[0])) 
        & (df["date"] <= pd.to_datetime(date_range[1]))
    ]

# ----------------------------------------------------
# 4. Interactive Plotly Time-Series
# ----------------------------------------------------
st.subheader(f"{data_choice} {disease_choice} Risk & Climate Parameters")

risk_col = "malaria_risk" if disease_choice == "Malaria" else "dengue_risk"

if data_choice == "Monthly":
    fig = px.line(
        df, x="date", y=risk_col, color="location", 
        title=f"{disease_choice} Risk Over Time ({data_choice})"
    )
    fig.update_layout(yaxis_title="Risk (0–1)")
    st.plotly_chart(fig, use_container_width=True)
    
    col1, col2 = st.columns(2)
    with col1:
        fig_temp = px.line(
            df, x="date", y="temp_avg", color="location",
            title="Average Temperature (°C)"
        )
        st.plotly_chart(fig_temp, use_container_width=True)
    with col2:
        fig_rain = px.line(
            df, x="date", y="monthly_rainfall_mm", color="location",
            title="Monthly Rainfall (mm)"
        )
        st.plotly_chart(fig_rain, use_container_width=True)

    # Show outbreak months
    if disease_choice == "Malaria":
        flag_col = "malaria_outbreak"
    else:
        flag_col = "dengue_outbreak"

    outbreak_months = df[df[flag_col] == True]
    if not outbreak_months.empty:
        st.write(f"**Months with likely {disease_choice} outbreak:**")
        st.dataframe(outbreak_months[[
            "location","year","month","temp_avg","humidity","monthly_rainfall_mm",flag_col
        ]])
    else:
        st.write(f"No months meet the {disease_choice} outbreak criteria in this selection.")

else:
    # Daily data
    fig = px.line(
        df, x="date", y=risk_col, color="location",
        title=f"{disease_choice} Daily Risk Over Time (2013–2024)"
    )
    fig.update_layout(yaxis_title="Risk (0–1)")
    st.plotly_chart(fig, use_container_width=True)
    
    col1, col2 = st.columns(2)
    with col1:
        fig_temp = px.line(
            df, x="date", y="temp_avg", color="location",
            title="Daily Avg Temperature (°C)"
        )
        st.plotly_chart(fig_temp, use_container_width=True)
    with col2:
        fig_rain = px.line(
            df, x="date", y="daily_rainfall_mm", color="location",
            title="Daily Rainfall (mm)"
        )
        st.plotly_chart(fig_rain, use_container_width=True)

# ----------------------------------------------------
# 5. Correlation Heatmap
# ----------------------------------------------------
st.subheader(f"Correlation Heatmap - {data_choice} Data")

if data_choice == "Monthly":
    subset_cols = ["temp_avg", "humidity", "monthly_rainfall_mm", "malaria_risk", "dengue_risk"]
else:
    subset_cols = ["temp_avg", "humidity", "daily_rainfall_mm", "malaria_risk", "dengue_risk"]

corr_df = df[subset_cols].corr()
fig_corr = px.imshow(
    corr_df, text_auto=True, aspect="auto", 
    title="Correlation Matrix of Weather & Risk"
)
st.plotly_chart(fig_corr, use_container_width=True)

# ----------------------------------------------------
# 6. Add Real-Time Weather in Folium Map + Outbreak Info
# ----------------------------------------------------
st.subheader("Interactive Map")
st.markdown(
    """
    **Note**: We only have 3 locations for the CSV data.  
    Markers now also show **real-time weather** from OpenWeather & an **outbreak** indicator.
    """
)

# --- 6A. Helper function to get current weather from OpenWeather ---
API_KEY = "c5b5c5ee6c497c6b1869ed926582a1ea"  # <-- Your OpenWeather API key

def get_current_weather(lat, lon, api_key=API_KEY):
    """
    Fetch current weather data from OpenWeather for given lat/lon.
    Returns a dict with {temp, humidity, description} if successful; else None.
    """
    url = f"https://api.openweathermap.org/data/2.5/weather?lat={lat}&lon={lon}&appid={api_key}&units=metric"
    try:
        resp = requests.get(url)
        if resp.status_code == 200:
            data = resp.json()
            # Extract a few relevant fields:
            current_temp = data["main"]["temp"]
            humidity = data["main"]["humidity"]
            weather_desc = data["weather"][0]["description"]
            return {
                "temp": current_temp,
                "humidity": humidity,
                "description": weather_desc
            }
        else:
            return None
    except Exception as e:
        # In production, you'd handle logging or fallback here
        return None

# --- 6B. Create Folium Map ---
m = folium.Map(location=[-6.0, 35.0], zoom_start=6)

if disease_choice == "Malaria":
    outbreak_flag_col = "malaria_outbreak"
else:
    outbreak_flag_col = "dengue_outbreak"

# For each location, we show both the CSV-based stats AND real-time weather
if data_choice == "Monthly":
    for loc in selected_locations:
        loc_info = LOCATIONS[loc]
        loc_df = df[df["location"] == loc]
        
        if loc_df.empty:
            continue
        
        # Averages from the CSV data
        avg_risk = loc_df[risk_col].mean()
        avg_temp = loc_df["temp_avg"].mean()
        avg_rain = loc_df["monthly_rainfall_mm"].mean()
        
        # Check if there's an outbreak in the filtered monthly data
        outbreak_count = loc_df[loc_df[outbreak_flag_col] == True].shape[0]
        outbreak_status = "Yes" if outbreak_count > 0 else "No"
        
        # Fetch real-time weather
        weather_now = get_current_weather(loc_info["lat"], loc_info["lon"], API_KEY)
        
        if weather_now:
            rt_temp = weather_now["temp"]
            rt_hum = weather_now["humidity"]
            rt_desc = weather_now["description"]
        else:
            rt_temp = None
            rt_hum = None
            rt_desc = "N/A"
        
        # Build the popup HTML
        popup_html = f"""
        <b>{loc}</b><br/>
        Disease: {disease_choice}<br/>
        Outbreak Now (in selection)? {outbreak_status}<br/>
        <br/>
        <u>Historical/Forecasted Averages (CSV)</u><br/>
        Avg Risk (selected range): {avg_risk:.2f}<br/>
        Avg Temp (°C): {avg_temp:.2f}<br/>
        Avg Rainfall (mm): {avg_rain:.2f}<br/>
        <br/>
        <u>Real-Time Weather (OpenWeather)</u><br/>
        Current Temp (°C): {rt_temp if rt_temp else 'N/A'}<br/>
        Current Humidity (%): {rt_hum if rt_hum else 'N/A'}<br/>
        Conditions: {rt_desc}
        """
        
        folium.Marker(
            location=[loc_info["lat"], loc_info["lon"]],
            popup=popup_html,
            tooltip=f"{loc} ({disease_choice})"
        ).add_to(m)

else:
    # Daily data
    for loc in selected_locations:
        loc_info = LOCATIONS[loc]
        loc_df = df[df["location"] == loc]
        
        if loc_df.empty:
            continue
        
        avg_risk = loc_df[risk_col].mean()
        avg_temp = loc_df["temp_avg"].mean()
        avg_rain = loc_df["daily_rainfall_mm"].mean()
        
        # Check outbreak
        outbreak_count = loc_df[loc_df[outbreak_flag_col] == True].shape[0]
        outbreak_status = "Yes" if outbreak_count > 0 else "No"
        
        # Real-time weather
        weather_now = get_current_weather(loc_info["lat"], loc_info["lon"], API_KEY)
        if weather_now:
            rt_temp = weather_now["temp"]
            rt_hum = weather_now["humidity"]
            rt_desc = weather_now["description"]
        else:
            rt_temp = None
            rt_hum = None
            rt_desc = "N/A"
        
        popup_html = f"""
        <b>{loc}</b><br/>
        Disease: {disease_choice}<br/>
        Outbreak Now (in selection)? {outbreak_status}<br/>
        <br/>
        <u>Historical/Forecasted Averages (CSV)</u><br/>
        Avg Risk (selected range): {avg_risk:.2f}<br/>
        Avg Temp (°C): {avg_temp:.2f}<br/>
        Avg Rain (mm/day): {avg_rain:.2f}<br/>
        <br/>
        <u>Real-Time Weather (OpenWeather)</u><br/>
        Current Temp (°C): {rt_temp if rt_temp else 'N/A'}<br/>
        Current Humidity (%): {rt_hum if rt_hum else 'N/A'}<br/>
        Conditions: {rt_desc}
        """
        
        folium.Marker(
            location=[loc_info["lat"], loc_info["lon"]],
            popup=popup_html,
            tooltip=f"{loc} ({disease_choice})"
        ).add_to(m)

# Render Folium map in Streamlit
st_data = st_folium(m, width=700, height=500)