Update app.py

app.py

@@ -1,25 +1,25 @@
-import joblib
-import streamlit as st
-
-# Load the model
-model = joblib.load(r'random_forest_model.joblib')
-
-# Define the prediction function
-def prediction(first, second, third, forth):
-    prediction = model.predict([[first, second, third, forth]])
-    label = ['Not Affordable', 'Affordable']
-    return label[prediction[0]]
-
-# Streamlit interface
-st.title("Affordability Prediction")
-
-# Create sliders for input
-capex = st.slider("CAPEX (RM mil)", min_value=0, max_value=1000)
-opex = st.slider("OPEX (RM mil)", min_value=0, max_value=1000)
-performance = st.slider("Performance", min_value=0, max_value=5)
-revenue = st.slider("Revenue (RM mil)", min_value=0, max_value=1000)
-
-# Button to make the prediction
-if st.button("Predict"):
-    result = prediction(capex, opex, performance, revenue)
+import joblib
+import streamlit as st
+
+# Load the model
+model = joblib.load(r'random_forest_model.joblib')
+
+# Define the prediction function
+def prediction(first, second, third, forth):
+    prediction = model.predict([[first, second, third, forth]])
+    label = ['Not Affordable', 'Affordable']
+    return label[prediction[0]]
+
+# Streamlit interface
+st.title("Affordability Prediction")
+
+# Create sliders for input
+capex = st.slider("CAPEX (RM mil)", min_value=0, max_value=100)
+opex = st.slider("OPEX (RM mil)", min_value=0, max_value=100)
+performance = st.slider("Performance", min_value=0, max_value=5)
+revenue = st.slider("Revenue (RM mil)", min_value=0, max_value=100)
+
+# Button to make the prediction
+if st.button("Predict"):
+    result = prediction(capex, opex, performance, revenue)
     st.write(f"The prediction is: {result}")