diff --git a/.cache.sqlite b/.cache.sqlite
new file mode 100644
index 000000000000..087b961c1d60
Binary files /dev/null and b/.cache.sqlite differ
diff --git a/requirements.txt b/requirements.txt
index 502d7d1a0d19..86168e91e6de 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,3 +1,6 @@
 altair
 pandas
 streamlit
+openmeteo_requests
+requests_cache
+retry_requests
\ No newline at end of file
diff --git a/streamlit_app.py b/streamlit_app.py
index 7a0ed1272052..efe18842b766 100644
--- a/streamlit_app.py
+++ b/streamlit_app.py
@@ -2,39 +2,90 @@
 import numpy as np
 import pandas as pd
 import streamlit as st
+import openmeteo_requests
+import requests_cache
+from datetime import date
+from retry_requests import retry
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+st.set_page_config(
+        page_title="San Francisco Rainfall",
+)
+
+st.title("San Francisco Rainfall")
+
+DATE_RANGE = st.date_input("Time range", value=[date(2020, 10, 1), date.today()])
+
+def get_season_from_date(datetime):
+    date = datetime.date()
+    season = date.year if date.month > 9 else date.year - 1
+    return season
+
+def get_season_start_date_from_season(season):
+    return date(season, 10, 1)
+
+def get_day_season_rank(datetime):
+    date = datetime.date()
+    delta = date - get_season_start_date_from_season(get_season_from_date(datetime))
+    return delta.days
+
+def get_day_in_season(datetime):
+    return datetime.strftime("%b %d")
+
+
+# Setup the Open-Meteo API client with cache and retry on error
+cache_session = requests_cache.CachedSession('.cache', expire_after = -1)
+retry_session = retry(cache_session, retries = 5, backoff_factor = 0.2)
+openmeteo = openmeteo_requests.Client(session = retry_session)
+
+# Make sure all required weather variables are listed here
+# The order of variables in hourly or daily is important to assign them correctly below
+url = "https://archive-api.open-meteo.com/v1/archive"
+params = {
+	"latitude": 37.7749,
+	"longitude": -122.4194,
+	"start_date": DATE_RANGE[0],
+	"end_date": DATE_RANGE[1],
+	"daily": "precipitation_sum",
+    "precipitation_unit": "inch",
+	"timezone": "America/Los_Angeles"
+}
+
+responses = openmeteo.weather_api(url, params=params)
+
+# Process first location. Add a for-loop for multiple locations or weather models
+response = responses[0]
+
+daily = response.Daily()
+daily_precipitation_sum = daily.Variables(0).ValuesAsNumpy()
+
+daily_data = {"date": pd.date_range(
+	start = pd.to_datetime(daily.Time(), unit = "s"),
+	end = pd.to_datetime(daily.TimeEnd(), unit = "s"),
+	freq = pd.Timedelta(seconds = daily.Interval()),
+	inclusive = "left"
+)}
+daily_data["precipitation_sum"] = daily_precipitation_sum
+
+daily_dataframe = pd.DataFrame(data = daily_data)
+daily_dataframe["date"] = daily_dataframe["date"].apply(lambda x: pd.to_datetime(x))
+daily_dataframe["x_rank"] = daily_dataframe["date"].apply(get_day_season_rank)
+daily_dataframe["Day"] = daily_dataframe["date"].apply(get_day_in_season)
+daily_dataframe["season"] = daily_dataframe["date"].apply(lambda x: str(get_season_from_date(x)))
+daily_dataframe["Cumulative Rainfall (Inches)"] = daily_dataframe.groupby('season')['precipitation_sum'].cumsum()
+daily_dataframe["14 Day Rolling Rainfall (Inches)"] = daily_dataframe.groupby('season')['precipitation_sum'].rolling(window=14,min_periods=14).sum().reset_index(0,drop=True)
+
+st.altair_chart(alt.Chart(daily_dataframe, height=700, width=700, title="Cumulative Seasonal Rainfall")
+    .mark_line().encode(
+    x=alt.X('Day', sort=alt.EncodingSortField(field="x_rank", op="min")),
+    y='Cumulative Rainfall (Inches)',
+    color='season',
+    )
+)
+
+st.altair_chart(alt.Chart(daily_dataframe, height=700, width=700, title="14 Day Rolling Rainfall")
+    .mark_line().encode(
+    x=alt.X('Day', sort=alt.EncodingSortField(field="x_rank", op="min")),
+    y='14 Day Rolling Rainfall (Inches)',
+    color='season',
+    )
+)