Update Script.py

Script.py

@@ -1,30 +1,30 @@
-import pandas as pd
-import numpy as np
-#from matplotlib import pyplot as plt
-#import seaborn as sns
-import sklearn
-from sklearn.preprocessing import RobustScaler, StandardScaler, OneHotEncoder, OrdinalEncoder, PowerTransformer
-from sklearn.compose import ColumnTransformer
-from sklearn.pipeline import Pipeline
-from sklearn.model_selection import train_test_split
-from sklearn.linear_model import LinearRegression
-import pickle
-import streamlit as st
-
-st.image("https://www.innomatics.in/wp-content/uploads/2023/01/Innomatics-Logo1.png")
-st.title("Diamond Price Prediction")
-
-carat = st.number_input("Enter the carat value")
-cut = st.text_input("Enter the cut of the diamond")
-color = st.text_input("Enter the color code of the diamond")
-clarity = st.text_input("Enter the clarity code")
-depth = st.number_input("Enter the depth of the diamond")
-table = st.number_input("Enter the table value")
-x = st.number_input("Enter the length of diamond")
-y = st.number_input("Enter the width of the diamond")
-z = st.number_input("Enter the z of the diamond")
-
-model_1 = pickle.load(open(r"C:\\Users\\Dream\\Downloads\\estimator1.pkl","rb")) #pickle file path
-if st.button("Submit"):
-    result = model_1.predict([[carat,cut,color,clarity,depth,table,x,y,z]])
+import pandas as pd
+import numpy as np
+#from matplotlib import pyplot as plt
+#import seaborn as sns
+import sklearn
+from sklearn.preprocessing import RobustScaler, StandardScaler, OneHotEncoder, OrdinalEncoder, PowerTransformer
+from sklearn.compose import ColumnTransformer
+from sklearn.pipeline import Pipeline
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+import pickle
+import streamlit as st
+
+st.image("https://www.innomatics.in/wp-content/uploads/2023/01/Innomatics-Logo1.png")
+st.title("Diamond Price Prediction")
+
+carat = st.number_input("Enter the carat value")
+cut = st.text_input("Enter the cut of the diamond")
+color = st.text_input("Enter the color code of the diamond")
+clarity = st.text_input("Enter the clarity code")
+depth = st.number_input("Enter the depth of the diamond")
+table = st.number_input("Enter the table value")
+x = st.number_input("Enter the length of diamond")
+y = st.number_input("Enter the width of the diamond")
+z = st.number_input("Enter the z of the diamond")
+
+model_1 = pickle.load(open(r"estimator1.pkl","rb")) #pickle file path
+if st.button("Submit"):
+    result = model_1.predict([[carat,cut,color,clarity,depth,table,x,y,z]])
     st.write(f"The predicted price of the diamond is {result}")