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+
+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+RUN useradd -m -u 1000 user
+
+USER user
+
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+
+COPY --chown=user . $HOME/app
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+from fastapi import FastAPI
+import uvicorn
+
+import pandas as pd
+import numpy as np
+import pickle
+import rasterio
+import h5py
+from skimage.morphology import disk
+from geopy.extra.rate_limiter import RateLimiter
+from geopy.geocoders import Nominatim
+
+app = FastAPI()
+
+
+#Endpoints
+#Root endpoints
+@app.get("/")
+def root():
+    return {"API": "Hail API 2020"}
+    
+def geocode_address(address):
+
+    try:
+        address2 = address.replace(' ', '+').replace(',', '%2C')
+        df = pd.read_json(
+            f'https://geocoding.geo.census.gov/geocoder/locations/onelineaddress?address={address2}&benchmark=2020&format=json')
+        results = df.iloc[:1, 0][0][0]['coordinates']
+        lat, lon = results['y'], results['x']
+    except:
+        geolocator = Nominatim(user_agent='GTA Lookup')
+        geocode = RateLimiter(geolocator.geocode, min_delay_seconds=2)
+        location = geolocator.geocode(address)
+        lat, lon = location.latitude, location.longitude
+        
+    return lat, lon
+
+def get_hail_data(address, start_date, end_date, radius_miles, get_max):
+
+    start_date = pd.Timestamp(str(start_date)).strftime('%Y%m%d')
+    end_date = pd.Timestamp(str(end_date)).strftime('%Y%m%d')
+    date_years = pd.date_range(start=start_date, end=end_date, freq='M')
+    date_range_days = pd.date_range(start_date, end_date)
+    years = list(set([d.year for d in date_years]))
+
+    if len(years) == 0:
+        years = [pd.Timestamp(start_date).year]
+        
+    # Geocode Address
+    lat, lon= geocode_address(address)
+    
+    # Convert Lat Lon to row & col on Array
+    transform = pickle.load(open('Data/transform_mrms.pkl', 'rb'))
+    
+    row, col = rasterio.transform.rowcol(transform, lon, lat)
+
+    files = [
+        'Data/2023_hail.h5',
+        'Data/2022_hail.h5',
+        'Data/2021_hail.h5',
+        'Data/2020_hail.h5'
+    ]
+
+    files_choosen = [i for i in files if any(i for j in years if str(j) in i)]
+
+    # Query and Collect H5 Data
+    all_data = []
+    all_dates = []
+    for file in files_choosen:
+        with h5py.File(file, 'r') as f:
+            # Get Dates from H5
+            dates = f['dates'][:]
+            date_idx = np.where((dates >= int(start_date))
+                                & (dates <= int(end_date)))[0]
+
+            # Select Data by Date and Radius
+            dates = dates[date_idx]
+            data = f['hail'][date_idx, row-radius_miles:row +
+                             radius_miles+1, col-radius_miles:col+radius_miles+1]
+
+            all_data.append(data)
+            all_dates.append(dates)
+
+    data_all = np.vstack(all_data)
+    dates_all = np.concatenate(all_dates)
+
+    # Convert to Inches
+    data_mat = np.where(data_all < 0, 0, data_all)*0.0393701
+
+    # Get Radius of Data
+    disk_mask = np.where(disk(radius_miles) == 1, True, False)
+    data_mat = np.where(disk_mask, data_mat, -1).round(3)
+
+    # Process to DataFrame
+    # Find Max of Data
+    if get_max == True:
+        data_max = np.max(data_mat, axis=(1, 2))
+        df_data = pd.DataFrame({'Date': dates_all,
+                               'Hail_max': data_max})
+    # Get all Data
+    else:
+        data_all = list(data_mat)
+        df_data = pd.DataFrame({'Date': dates_all,
+                               'Hail_all': data_all})
+
+    df_data['Date'] = pd.to_datetime(df_data['Date'], format='%Y%m%d')
+    df_data = df_data.set_index('Date')
+
+    df_data = df_data.reindex(date_range_days, fill_value=0).reset_index().rename(
+        columns={'index': 'Date'})
+    df_data['Date'] = df_data['Date'].dt.strftime('%Y-%m-%d')
+
+    return df_data
+
+
+@app.get('/Hail_Docker_Data')
+async def predict(address: str, start_date: str, end_date: str, radius_miles: int, get_max: bool):
+
+    try:
+        results = get_hail_data(address, start_date,
+                                end_date, radius_miles, get_max)
+    except:
+        results = pd.DataFrame({'Date': ['error'], 'Hail_max': ['error']})
+
+    return results.to_json()
+    

requirements.txt

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+fastapi[all]
+uvicorn[standard]
+beautifulsoup4
+numpy
+pandas
+rasterio
+h5py
+scikit-image
+geopy