Update app.py

app.py

@@ -1,52 +1,42 @@
+!pip install streamlit
+
 import streamlit as st
-import pickle
-import numpy as np
 import pandas as pd
-import seaborn
-
-with open("weather_classification_model.pkl", "rb") as model_file:
-    model = pickle.load(model_file)
-
-
-with open("label_encoder.pkl", "rb") as encoder_file:
-    label_encoder = pickle.load(encoder_file)
-
-
+import pickle
 
+# Load the saved model
+with open('weather_classifier_model.pkl', 'rb') as file:
+    loaded_model = pickle.load(file)
 
+# Streamlit app
 st.title("🌦️ Weather Prediction App")
 
-st.write("Enter weather details to predict the weather type.")
-
-
-Temperature = st.number_input("Temperature (°C)", min_value=-50, max_value=50, value=20)
-Humidity = st.number_input("Humidity (%)", min_value=0, max_value=100, value=50)
-Wind_Speed = st.number_input("Wind Speed (km/h)", min_value=0, max_value=150, value=10)
-Precipitation = st.number_input("Precipitation (%)", min_value=0, max_value=100, value=10)
-Cloud_Cover = st.selectbox("Cloud Cover", ["Clear", "Partial", "Overcast"])
-UV_Index = st.number_input("UV Index", min_value=0, max_value=15, value=5)
-Season = st.selectbox("Season", ["Winter", "Spring", "Summer", "Fall"])
-Visibility = st.number_input("Visibility (km)", min_value=0, max_value=50, value=10)
-Location = st.selectbox("Location", ["inland", "mountain", "coastal"])
-
-
-categorical_features = {"Cloud Cover": Cloud_Cover, "Season": Season, "Location": Location}
-for feature, value in categorical_features.items():
-    categorical_features[feature] = label_encoder.transform([value])[0]
-
-
-input_data = np.array([[Temperature, Humidity, Wind_Speed, Precipitation,
-                        categorical_features["Cloud Cover"], UV_Index, 
-                        categorical_features["Season"], Visibility, 
-                        categorical_features["Location"]]])
-
-
-input_data_scaled = scaler.transform(input_data)
-
-
-if st.button("Predict Weather Type"):
-    prediction_numeric = model.predict(input_data_scaled)[0]
-    predicted_label = label_encoder.inverse_transform([prediction_numeric])[0]
-    
-    st.success(f"🌤️ Predicted Weather Type: **{predicted_label}**")
-
+# Input features
+temperature = st.number_input("Temperature (°C)")
+humidity = st.number_input("Humidity (%)")
+wind_speed = st.number_input("Wind Speed (km/h)")
+cloud_cover = st.selectbox("Cloud Cover", ["Clear", "Scattered Clouds", "Partly Cloudy", "Mostly Cloudy", "Overcast"])
+precipitation = st.number_input("Precipitation (%)")
+uv_index = st.number_input("UV Index")
+visibility = st.number_input("Visibility (km)")
+season = st.selectbox("Season", ["Spring", "Summer", "Autumn", "Winter"])
+location = st.selectbox("Location", ["Sydney", "Melbourne", "Brisbane", "Perth", "Adelaide", "Canberra", "Darwin", "Hobart"]) # Update with your locations
+
+
+# Create input DataFrame
+input_data = pd.DataFrame({
+    "Temperature": [temperature],
+    "Humidity": [humidity],
+    "Wind Speed": [wind_speed],
+    "Cloud Cover": [cloud_cover],
+    "Precipitation (%)": [precipitation],
+    "UV Index": [uv_index],
+    "Visibility (km)": [visibility],
+    "Season": [season],
+    "Location": [location]
+})
+
+# Make prediction
+if st.button("Predict"):
+    prediction = loaded_model.predict(input_data)[0]
+    st.success(f"Predicted Weather Type: {prediction}")