Upload app.py

app.py

@@ -0,0 +1,47 @@
+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()