Create app.py

app.py

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+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
+
+# ----------------------------------------------------
+# 1. Load data
+# ----------------------------------------------------
+@st.cache_data
+def load_data():
+    # Load daily and monthly CSV from local files (or a URL if needed)
+    daily_df = pd.read_csv("daily_data_2013_2024.csv", parse_dates=["date"])
+    monthly_df = pd.read_csv("monthly_data_2013_2024.csv")
+    
+    # If monthly_df also needs a 'date' column for plotting (like first day of month), you can create:
+    # monthly_df["date"] = pd.to_datetime(monthly_df["year"].astype(str) + "-" + monthly_df["month"].astype(str) + "-01")
+
+    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 to focus on
+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, let user select a year range
+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, let user select a date range
+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":
+    # Subset monthly data for selected locations
+    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 plotting (1st of each month)
+    # This can help create a time-series for Plotly
+    df["date"] = pd.to_datetime(df["year"].astype(str) + "-" + df["month"].astype(str) + "-01")
+    
+else:
+    # Subset daily data
+    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")
+
+# Decide which columns are relevant
+risk_col = "malaria_risk" if disease_choice == "Malaria" else "dengue_risk"
+
+if data_choice == "Monthly":
+    # We'll plot a line chart of risk, temperature, and rainfall vs. date
+    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)
+    
+    # Optionally plot temperature / rainfall on another figure
+    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:
+        # 'monthly_rainfall_mm' is total monthly rainfall
+        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 flags if focusing on monthly
+    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:
+    # For daily data, plot daily risk
+    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)
+    
+    # Similarly, temperature & daily rainfall
+    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")
+
+# We'll pick relevant numeric columns
+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. Interactive Map (Folium)
+# ----------------------------------------------------
+st.subheader("Interactive Map")
+st.markdown(
+    """
+    **Note**: We only have 3 locations. Each marker popup shows some aggregated
+    stats for the displayed data range.
+    """
+)
+
+# Create a base map centered roughly in Tanzania
+m = folium.Map(location=[-6.0, 35.0], zoom_start=6)
+
+# We'll show monthly or daily aggregates in the popups
+if data_choice == "Monthly":
+    # For each location, let's gather monthly avg for the current df
+    # Then we can show a simple summary in the popup
+    for loc in selected_locations:
+        loc_info = LOCATIONS[loc]
+        loc_df = df[df["location"] == loc]
+        
+        if loc_df.empty:
+            continue
+        # Basic stats: average risk, average rainfall, etc
+        avg_risk = loc_df[risk_col].mean()
+        avg_temp = loc_df["temp_avg"].mean()
+        avg_rain = loc_df["monthly_rainfall_mm"].mean()
+        
+        # Build popup HTML
+        popup_html = f"""
+        <b>{loc}</b><br/>
+        Disease: {disease_choice}<br/>
+        Avg Risk (in selection): {avg_risk:.2f}<br/>
+        Avg Temp (°C): {avg_temp:.2f}<br/>
+        Avg Rainfall (mm): {avg_rain:.2f}<br/>
+        """
+        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()
+        
+        popup_html = f"""
+        <b>{loc}</b><br/>
+        Disease: {disease_choice}<br/>
+        Avg Risk (in selection): {avg_risk:.2f}<br/>
+        Avg Temp (°C): {avg_temp:.2f}<br/>
+        Avg Rain (mm/day): {avg_rain:.2f}<br/>
+        """
+        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)