Update app.py

app.py

@@ -7,14 +7,15 @@ from pycaret.regression import *
 cvr_saved = load_model('pred_cvr')
 
 def predict_cvr(xyz_campaign_id, gender, age, Impressions, Clicks, 
-                Total_Conversion, interest): #สร้าง function predict_cvr โดยภายใน function คือ ส่วนของ input data
-  path = "KAG_conversion_data.csv" #Import development ไฟล์ที่เป็น .csv
-  df = pd.read_csv(path) #อ่านไฟล์ csv
-  df.drop(["ad_id", "fb_campaign_id", "Spent","Approved_Conversion"],axis=1, inplace = True) #drop columns ทิ้ง
-  df = pd.DataFrame.from_dict({'xyz_campaign_id': [xyz_campaign_id], 'gender': [gender], 'age': [age], 'Impressions': [Impressions], 'Clicks': [Clicks], 'Total_Conversion': [Total_Conversion], 'interest': [interest]}) #แปลงเป็น dataframe
-  df["xyz_campaign_id"].replace({916:"campaign_a",936:"campaign_b",1178:"campaign_c"}, inplace=True) #แทนที่ด้วยชื่อ campaign
-  pred = cvr_saved.predict(df).tolist()[0] #เมื่่อถูกทำนายแล้ว มันจะส่งกลับคืนค่าเข้าไปใน pred
-  return 'Conversion Rate : '+str(pred) #function predict_cvr จะส่ง output ออกมาเป็น "Conversion Rate : pred"
+                Total_Conversion, interest): 
+  path = "KAG_conversion_data.csv" 
+  df = pd.read_csv(path) 
+  df.drop(["ad_id", "fb_campaign_id", "Spent","Approved_Conversion"],axis=1, inplace = True) 
+  df = pd.DataFrame.from_dict({'xyz_campaign_id': [xyz_campaign_id], 'gender': [gender], 'age': [age], 'Impressions': [Impressions], 
+  'Clicks': [Clicks], 'Total_Conversion': [Total_Conversion], 'interest': [interest]}) 
+  df["xyz_campaign_id"].replace({916:"campaign_a",936:"campaign_b",1178:"campaign_c"}, inplace=True) 
+  pred = cvr_saved.predict(df).tolist()[0] 
+  return 'Conversion Rate : '+str(pred) 
   
 xyz_campaign_id = gr.inputs.Dropdown(['campaign_a', 'campaign_b', 'campaign_c'], label="xyz_campaign_id -> an ID associated with each ad campaign of XYZ company")
 gender = gr.inputs.Dropdown(['M', 'F'], label = "gender -> gender of the person to whom the add is shown")