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app.py

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+import pandas as pd
+import gradio as gr
+import joblib
+
+
+le=joblib.load('le_col.pkl')
+scale=joblib.load('std_col.pkl')
+lr=joblib.load('model.pkl')
+
+le_col=['Prand']
+std_col=['year','miles','condition']
+
+def predicion_car_price(y,m,c,p):
+    try:
+        input_data=pd.DataFrame({
+            'year':[y],
+            'miles':[m],
+            'condition':[c],
+            'Prand':[p]
+        })
+        for col in le_col:
+            input_data[col]=le[col].transform(input_data[col])
+        input_data[std_col]=scale.transform(input_data[std_col])
+        prediction=lr.predict(input_data)
+        return prediction[0]
+    except Exception as e:
+        return str(e)
+gr.Interface(
+    inputs=[
+        gr.Number(label='year'),
+        gr.Number(label='miles'),
+        gr.Number(label='condition'),
+        gr.Dropdown([
+    "Toyota", "Mercedes-Benz", "Ford", "Honda", "BMW", "Chevrolet", "Nissan", "Kia",
+    "Subaru", "Jeep", "Audi", "Volkswagen", "Hyundai", "Lexus", "Land", "Dodge", "Acura",
+    "Mazda", "Ram", "Volvo", "Porsche", "INFINITI", "Cadillac", "Chrysler", "GMC", "Alfa",
+    "Jaguar", "MINI", "Maserati", "Buick", "Lincoln", "Mitsubishi", "FIAT", "Scion",
+    "Aston", "Genesis", "Karma", "McLaren", "Rolls-Royce", "Bentley", "Pontiac", "Saturn"],label='Prand')
+    ],
+    fn=predicion_car_price,
+    outputs=gr.Textbox(label='Prediction'),
+    title='Predictin_Car_Price'
+).launch()

le_col.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:46f49f9f3e3dacf84fc7fa20a1939d53a924228be88e35e739baaaf40c63f1e2
+size 1077

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f2b53d3c5ee7c3a1b704245817e74bc63e76fd5e9f9dc38556aecd70d80167cd
+size 952

requirements.txt

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+pandas
+numpy==1.23.5
+scikit-learn==1.2.2
+joblib
+gradio

std_col.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ede2e423a303b80e17f056d3fdb88780eb6e69a0f17347b4af2be4b9402e2803
+size 1007