Update app.py

app.py

@@ -4,14 +4,19 @@ 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():
+    # 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()
@@ -30,7 +35,7 @@ st.title("Malaria & Dengue Outbreak Analysis (2013–2024)")
 
 st.sidebar.header("Filters & Options")
 
-# Choose disease type
+# Choose disease type to focus on
 disease_choice = st.sidebar.radio("Select Disease", ["Malaria", "Dengue"])
 
 # Choose data granularity
@@ -40,75 +45,71 @@ data_choice = st.sidebar.radio("Data Granularity", ["Monthly", "Daily"])
 location_list = list(LOCATIONS.keys())
 selected_locations = st.sidebar.multiselect("Select Location(s)", location_list, default=location_list)
 
-# For monthly data
+# 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
+    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
-    )
+    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 data
+    
+    # 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]))
-    ]
+    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":
-    fig = px.line(
-        df, x="date", y=risk_col, color="location", 
-        title=f"{disease_choice} Risk Over Time ({data_choice})"
-    )
+    # 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)"
-        )
+        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)"
-        )
+        # '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 months
+    # Show outbreak flags if focusing on monthly
     if disease_choice == "Malaria":
         flag_col = "malaria_outbreak"
     else:
@@ -117,33 +118,26 @@ if data_choice == "Monthly":
     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
-        ]])
+        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)"
-    )
+    # 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)"
-        )
+        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)"
-        )
+        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)
 
 # ----------------------------------------------------
@@ -151,118 +145,59 @@ else:
 # ----------------------------------------------------
 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"
-)
+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
+# 6. Interactive Map (Folium)
 # ----------------------------------------------------
 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.
+    **Note**: We only have 3 locations. Each marker popup shows some aggregated
+    stats for the displayed data range.
     """
 )
 
-# --- 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 ---
+# Create a base map centered roughly in Tanzania
 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
+# 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
-        
-        # Averages from the CSV data
+        # 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()
         
-        # 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
+        # Build 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 Risk (in selection): {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:
@@ -271,42 +206,17 @@ else:
         
         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 Risk (in selection): {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,