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import streamlit as st
import pandas as pd
import pickle
import sklearn
from PIL import Image
@st.cache_resource
def load_model():
model_file = open('./models/weather_prediction_model.pkl', 'rb')
rfc = pickle.load(model_file)
oe = pickle.load(model_file)
sc = pickle.load(model_file)
images = pickle.load(model_file)
model_file.close()
return rfc, oe, sc, images
@st.cache_data
def load_icons():
dz = Image.open("assets/drizzle.png")
rn = Image.open("assets/rain.png")
sn = Image.open("assets/sun.png")
sw = Image.open("assets/snow.png")
fg = Image.open("assets/fog.png")
return dz, rn, sn, sw, fg
@st.cache_data
def get_prediction(input):
if len(input) != 4:
return None
input = pd.DataFrame([input], columns = ['precipitation', 'temp_max', 'temp_min', 'wind'])
X_predict = sc.transform(input)
y_predict = [rfc.predict(X_predict)]
weather_predict = oe.inverse_transform(y_predict)[0]
return weather_predict[0]
# Load the Model
rfc, oe, sc, images = load_model()
# Load the Icons
dz, rn, sn, sw, fg = load_icons()
st.header('Weather Classification', divider='green')
st.markdown("Change the sliders below to see the classification")
# Get input parameters
precipitation = st.slider('Precipitation (mm)', 0.0, 100.0, 0.0, 1.0)
temp_min = st.slider('Minimum Temperature (C)', -10.0, 20.0, 8.0, 1.0)
temp_max = st.slider('Maximum Temperature (C)', -2.0, 40.0, 15.0, 1.0)
wind = st.slider('Wind Speed (m/s)', 0.0, 10.0, 3.0, 1.0)
# Get weather prediction
weather = get_prediction([precipitation, temp_min, temp_max, wind])
if weather == None:
st.warning("Unknown Weather Classification")
else:
icon = Image.open(images[weather])
st.info("Weather Classification:")
st.image(icon, caption = weather, width = 250) |