second commit

app.py

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+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)
+    st.write(f"The prediction is: {result}")

random_forest_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:43902bbf9e7016896e0327d1018ff15a0894e961cc0dcb6536b51244395792e5
+size 95801

requirements.txt

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+gradio==4.39.0
+joblib==1.4.0
+scikit_learn==1.3.0