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app (1).py

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+import joblib
+import pandas as pd
+import streamlit as st 
+
+
+model = joblib.load('model.joblib')
+unique_values = joblib.load('unique_values.joblib')
+    
+unique_mainroad =  unique_values["mainroad"]
+unique_guestroom   =  unique_values["guestroom"]
+unique_basement =  unique_values["basement"]
+unique_hotwaterheating =  unique_values["hotwaterheating"]
+unique_airconditioning =  unique_values["airconditioning"]
+unique_prefarea =  unique_values["prefarea"]
+
+def main():
+    st.title("furnishingstatus Predit")
+
+    with st.form("questionaire"):
+        price = st.slider("price", min_value=1750000, max_value=13300000)
+        area = st.slider("area", min_value=1650, max_value=16200)
+        bedrooms = st.slider("bedrooms", min_value=1, max_value=6)
+        bathrooms = st.slider("bathrooms", min_value=1, max_value=5)
+        stories = st.slider("stories", min_value=1, max_value=5)
+        mainroad = st.selectbox("mainroad", unique_mainroad)
+        guestroom = st.selectbox("guestroom", unique_guestroom)
+        basement= st.selectbox("basement", unique_basement)
+        hotwaterheating= st.selectbox("hotwaterheating", unique_hotwaterheating)
+        airconditioning= st.selectbox("airconditioning", unique_airconditioning)
+        parking = st.slider("parking",  min_value=1, max_value=3)
+        prefarea = st.selectbox("prefarea", unique_prefarea)
+
+        clicked = st.form_submit_button("Predict income")
+        if clicked:
+            result=model.predict(pd.DataFrame({"price": [price],
+                                               "area": [area],
+                                               "bedrooms": [bedrooms],
+                                               "bathrooms": [bathrooms],
+                                               "stories": [stories],
+                                               "mainroad": [mainroad],
+                                               "guestroom": [guestroom],
+                                               "basement": [basement],
+                                               "hotwaterheating": [hotwaterheating],
+                                               "airconditioning": [airconditioning],
+                                               "parking": [parking],
+                                               "prefarea":[prefarea]}))
+            if result[0] == 0:
+                result_predict = 'furnished'
+            elif result[0] == 1:
+                result_predict = 'semi-furnished'
+            elif result[0] == 2:
+                result_predict = 'unfurnished'
+                
+            st.success(result_predict)
+
+if __name__=='__main__':
+    main()

requirements (1).txt

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+joblib
+pandas
+scikit-learn==1.2.2
+xgboost==1.7.6
+altair<5