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import streamlit as st
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
import joblib
def load_model():
# Load the pre-trained model
model = joblib.load('weather_model.joblib')
return model
def predict_weather_conditions(model, input_data):
# Make predictions on the input data
predictions = model.predict(input_data)
return predictions[0]
def main():
# Load the pre-trained model
model = load_model()
# Add a title to your app
st.title("Weather Prediction App")
# Get user input
temp_c = st.slider("Temperature in Celsius", min_value=-10.0, max_value=40.0, value=20.0)
dew_point_temp_c = st.slider("Dew Point Temperature in Celsius", min_value=-10.0, max_value=30.0, value=15.0)
rel_humidity = st.slider("Relative Humidity (%)", min_value=0, max_value=100, value=50)
wind_speed_kmh = st.slider("Wind Speed in km/h", min_value=0, max_value=50, value=10)
visibility_km = st.slider("Visibility in km", min_value=0.1, max_value=50.0, value=10.0)
press_kpa = st.slider("Atmospheric Pressure in kPa", min_value=90.0, max_value=110.0, value=101.0)
# Create a DataFrame with user input
input_data = pd.DataFrame({
'Temp_C': [temp_c],
'Dew Point Temp_C': [dew_point_temp_c],
'Rel Hum_%': [rel_humidity],
'Wind Speed_km/h': [wind_speed_kmh],
'Visibility_km': [visibility_km],
'Press_kPa': [press_kpa],
})
# Make predictions
if st.button("Predict Weather"):
predicted_weather = predict_weather_conditions(model, input_data)
st.success(f"Predicted Weather Condition: {predicted_weather}")
if __name__ == '__main__':
main()
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