Update app.py

app.py

@@ -1,99 +1,99 @@
-import pandas as pd
-from sklearn.model_selection import train_test_split
-from sklearn.linear_model import LinearRegression
-from sklearn.metrics import mean_squared_error, r2_score
-from sklearn.pipeline import Pipeline
-from sklearn.compose import ColumnTransformer
-from sklearn.preprocessing import StandardScaler, OneHotEncoder
-import streamlit as st
-
-df = pd.read_excel("cars.xls")
-x = df.drop("Price", axis=1)
-y = df[["Price"]]
-
-x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=20, random_state=42)
-
-preprocessor = ColumnTransformer(
-    transformers= [
-        ("num", StandardScaler(),["Mileage", "Cylinder", "Liter", "Doors"]),
-        ("cat", OneHotEncoder(), ["Make", "Model", "Trim", "Type"])
-    ]
-)
-
-model = LinearRegression()
-
-pipeline = Pipeline(steps=[
-    ("preprocessor", preprocessor), 
-    ("regressor", model)
-])
-
-pipeline.fit(x_train, y_train)
-pred = pipeline.predict(x_test)
-
-rmse = mean_squared_error(pred, y_test)**.5
-r2 = r2_score(pred, y_test)
-
-def price_pred(make,model,trim,mileage,car_type,cylinder,liter,doors,cruise,sound,leather):
-    input_data = pd.DataFrame({
-        "Make": [make],
-        "Model": [model],
-        "Trim": [trim],
-        "Mileage": [mileage],
-        "Type": [car_type],
-        "Cylinder": [cylinder],
-        "Liter": [liter],
-        "Doors": [doors],
-        "Cruise": [cruise],
-        "Sound": [sound],
-        "Leather": [leather],
-    })
-
-    prediction = pipeline.predict(input_data)[0]
-    return prediction
-
-def main():
-    st.title("MLOps Car Price Prediction :red_car:")
-    st.write("Enter Car Details to predict the price")
-
-    make = st.selectbox("Model", df["Make"].unique())
-    model = st.selectbox("Model", df[df["Make"] == make]["Model"].unique())
-    trim = st.selectbox("Trim", df[(df["Make"] == make) & (df["Model"] == model)]["Trim"].unique())
-    mileage = st.number_input("Mileage", 200, 60000)
-    car_type = st.selectbox("Type", df["Type"].unique())
-    cylinder = st.selectbox("Cylinder", df["Cylinder"].unique())
-    liter = st.number_input("Liter", 1, 6)
-    doors = st.selectbox("Doors", df["Doors"].unique())
-    cruise = st.radio("Cruise", [0,1])
-    sound = st.radio("Sound", [0,1])
-    leather = st.radio("Leather", [0,1])
-
-    if st.button("Predict"):
-        price = price_pred(make,model,trim,mileage,car_type,cylinder,liter,doors,cruise,sound,leather)
-        st.write(price)
-        price = float(price)
-        st.write(f"The predicted price is : ${price:.2f}")
-
-
-if __name__ == "__main__":
-    main()
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-    
-    # make = st.selectbox("Make", df["Make"].unique())
-
-    # model = st.selectbox("Model", df[df["Make"]==make]["Model"].unique())
-
-    # trim = st.selectbox("Trim", df[df["Model"]==model]["Trim"].unique())
-
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error, r2_score
+from sklearn.pipeline import Pipeline
+from sklearn.compose import ColumnTransformer
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+import streamlit as st
+
+df = pd.read_excel("./cars.xls")
+x = df.drop("Price", axis=1)
+y = df[["Price"]]
+
+x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=20, random_state=42)
+
+preprocessor = ColumnTransformer(
+    transformers= [
+        ("num", StandardScaler(),["Mileage", "Cylinder", "Liter", "Doors"]),
+        ("cat", OneHotEncoder(), ["Make", "Model", "Trim", "Type"])
+    ]
+)
+
+model = LinearRegression()
+
+pipeline = Pipeline(steps=[
+    ("preprocessor", preprocessor), 
+    ("regressor", model)
+])
+
+pipeline.fit(x_train, y_train)
+pred = pipeline.predict(x_test)
+
+rmse = mean_squared_error(pred, y_test)**.5
+r2 = r2_score(pred, y_test)
+
+def price_pred(make,model,trim,mileage,car_type,cylinder,liter,doors,cruise,sound,leather):
+    input_data = pd.DataFrame({
+        "Make": [make],
+        "Model": [model],
+        "Trim": [trim],
+        "Mileage": [mileage],
+        "Type": [car_type],
+        "Cylinder": [cylinder],
+        "Liter": [liter],
+        "Doors": [doors],
+        "Cruise": [cruise],
+        "Sound": [sound],
+        "Leather": [leather],
+    })
+
+    prediction = pipeline.predict(input_data)[0]
+    return prediction
+
+def main():
+    st.title("MLOps Car Price Prediction :red_car:")
+    st.write("Enter Car Details to predict the price")
+
+    make = st.selectbox("Model", df["Make"].unique())
+    model = st.selectbox("Model", df[df["Make"] == make]["Model"].unique())
+    trim = st.selectbox("Trim", df[(df["Make"] == make) & (df["Model"] == model)]["Trim"].unique())
+    mileage = st.number_input("Mileage", 200, 60000)
+    car_type = st.selectbox("Type", df["Type"].unique())
+    cylinder = st.selectbox("Cylinder", df["Cylinder"].unique())
+    liter = st.number_input("Liter", 1, 6)
+    doors = st.selectbox("Doors", df["Doors"].unique())
+    cruise = st.radio("Cruise", [0,1])
+    sound = st.radio("Sound", [0,1])
+    leather = st.radio("Leather", [0,1])
+
+    if st.button("Predict"):
+        price = price_pred(make,model,trim,mileage,car_type,cylinder,liter,doors,cruise,sound,leather)
+        st.write(price)
+        price = float(price)
+        st.write(f"The predicted price is : ${price:.2f}")
+
+
+if __name__ == "__main__":
+    main()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+    
+    # make = st.selectbox("Make", df["Make"].unique())
+
+    # model = st.selectbox("Model", df[df["Make"]==make]["Model"].unique())
+
+    # trim = st.selectbox("Trim", df[df["Model"]==model]["Trim"].unique())
+