Update app.py

app.py

@@ -1,517 +1,211 @@
-import streamlit as st
-import pandas as pd
-
-uploaded_file = st.file_uploader("Choose product file", type="csv")
-
-if uploaded_file:
-    #df = pd.read_excel(uploaded_file)
-    df = pd.read_csv(uploaded_file, encoding='utf8')
-    #st.dataframe(df)
-
-uploaded_file2 = st.file_uploader("Choose inventory file", type="csv")
-
-if uploaded_file2:
-    #df2 = pd.read_excel(uploaded_file2)
-    df2 = pd.read_csv(uploaded_file2, encoding='utf8')
-    
-    #st.dataframe(df2)
-    
-    #st.table(df2)
-
-def ConvertCitrus(df,df2):
-    # Load pandas
-    #import re as re
-    import RemoveHTMLtags as RHT
-    #INPUT FILE
-
-    
-
-    #df.to_excel('C:/Users/15572890/Desktop/I+D/MarksCsvConversion/Validation2/products_export_1.xlsx',index=False)
-    #df2.to_excel('C:/Users/15572890/Desktop/I+D/MarksCsvConversion/Validation2/inventory_export_1.xlsx',index=False)
-
-    tagsp=str('<style type=')+str('"')+str('"')+str('text/css')+str('"')+str('"')+str('><!--')
-    tags_list = ['<p class=','"p1"', 'data-mce-fragment="1">,','<b data-mce-fragment="1">','<i data-mce-fragment="1">','<p>' ,'</p>' , '<p*>',
-                  '<ul>','</ul>',
-                  '</i>','</b>','</p>','</br>',
-                  '<li>','</li>',
-                  '<br>',
-                  '<strong>','</strong>',
-                  '<span*>','</span>', '"utf-8"','UTF-8',
-                  '<a href*>','</a>','<meta charset=utf-8>',';;',
-                  '<em>','</em>','"','<meta charset=','utf-8>','<p>','<p','data-mce-fragment=1',';','<style type=','<style type=','><!--','text/css','<style type=\"\"text/css\"\"><!--','--></style>','td {border: 1px solid #ccc','}br {mso-data-placement:same-cell','}','>']
-
-
-
-
-    #def remove_html_tags(text):
-    #    """Remove html tags from a string"""
-    #    import re
-    #    clean = re.compile('<.*?>')
-    #    return re.sub(clean, '', text)
-    #for tag in tags_list:
-    ##      df['overview_copy'] = df['overview_copy'].str.replace(tag, '')
-    #   df.replace(to_replace=tag, value='', regex=True, inplace=True)
-
-    for index, row in df.iterrows():
-        df.iloc[index,2]=RHT.remove_tags(str(df.iloc[index,2]))
-
-    print(df.iloc[:,2])
-
-    #df.to_excel('C:/Users/15572890/Desktop/I+D/MarksCsvConversion/Validation2/products_export_1-nohtml.xlsx')
-
-    #df.fillna('', inplace=True)
-    df.iloc[:,2] = pd.Series(df.iloc[:,2],dtype="string")
-    print(df.iloc[:,2].dtype)
-    #s = pd.Series(['a', 'b', 'c'], dtype="string")
-    #s.dtype
-
-    #CONVERT FORMATS
-
-    #Column A(0) – Ignore
-    #Column B(1) “Title” > Column B(1) “Product Name”
-    #Column C(2) – Ignore
-    #Column D(3) “Vendor” > Column K(10) “Brand”
-    #Column F(5) “Custom Product Type” > Column AF(31) “Short Description”
-    #Column J(9) “Option1 Value” > Column I(8) “Size 1”
-    #Column L(11) “Option2 Value” > Column H(7) > Colour
-    #Column M(12) - Ignore
-    #Column N(13) “Option 3 Value” > Column A(0) “Style Number”
-    #1.	Problems in Column N.  Some codes do not stay as a number when the Citrus Lime csv is re-opened (8.05652E+12 instead of 8056516179091) The saved csv keeps turning this column back to “general’ format column when I re-open it, even after I save it as number column. The upload must keep this as a number formatted column.
- 
-    #Column O(14) - Ignore
-    #Column P(15) “Variant Grams” > Column AE (30) “Weight (grams)”
-    #Column R(17) “Variant Inventory  Qty” > Column R (17) “Stock Count”. THIS IS THE KEY TO THE DAILY UPLOAD
-    #Column U(20) “Variant Price” > Column F (5) “Unit MSRP”
-
-    #Column Y > C&D
-    #################################################################################################
-    temp_cols=df.columns.tolist()
-    new_cols=temp_cols.copy()
-    new_cols[1]=temp_cols[1]
-
-    new_cols[17]=temp_cols[17]
-
-    #################################################################################################
-    #THERE IS NO EXISTING COLUMN ON THE SHOPIFY EXPORT TO DIRECTLY PROVIDE DATA FOR COLUMN E ON THE CITRUS LIME CSV (which is the wholesale price ex VAT to the retailer). However – Column U “ Variant Price” can provide the information for Column E with the following formula:
- 
-    #((Column U/1.2)/1.6)*0.96
- 
-    #Column Y “Variant Barcode” > Column C “Vendor SKU” (2) (and D "UPC/EAN" (3)??)
- 
-    #There are 2 problems with converting Column Y to Column C.  
-    #2.	Shopify exports the UPC data and adds an apostrophe. This fails the SIM process. We need to get data without the apostrophe.
-    #3.	Vendor SKU.  The CSV file keeps switching the data to a non-number eg 8056516178308 shows as 8.05652E+12. The saved csv keeps turning this column to “general’ format column when I re-open it, even after I save it as number column. The upload must keep this as a number formatted column.
- 
-    #This is where it gets complicated…
- 
-    #Shopify exports the image file as https:// links in an odd way. Instead of attributing image 1, image 2, and image 3 etc in dedicated and separate columns, it spreads them across the sizes for the related product in the same column (Column Z “Image Src”). Column AA in the Shopify export csv just shows the image position instead.  We need to find a solution. We need to be able to provide https// image links in separate columns for each product and size. For example, if a product has 3 images, these need to be converted into Citrus Lime CSV columns Column Z “Image 1”, Column AA “Image 2”, Column AB “Image 3”, Column AC “Image 4” etc.
-    #new_cols[4]=((temp_cols[20]/1.2)/1.96)*0.96
-
-    #Column C “Body (HTML)” > Column AG “Long Description” (32)
-
-
-    df_copy=df[new_cols].copy(deep=True)
-    print("SKU")
-    print(df.iloc[:,24])
-
-    local_df = df.copy(deep=True)
-
-    df_copy.iloc[:,0]=local_df.iloc[:,13].copy(deep=True)
-    df_copy.iloc[:,5]=local_df.iloc[:,20].copy(deep=True)
-    df_copy.iloc[:,7]=local_df.iloc[:,11].copy(deep=True)
-    #24 is variant Bar code
-    df_copy.iloc[:,2]=local_df.iloc[:,24].copy(deep=True)
-
-    df_copy.iloc[:,8]=local_df.iloc[:,9].copy(deep=True)
-    df_copy.iloc[:,10]=local_df.iloc[:,3].copy(deep=True)
-    df_copy.rename(columns={df_copy.columns[10]: 'Brand'},inplace=True)
-    df_copy.columns.values[10] = 'Brand'
-
-    df_copy.iloc[:,30]=local_df.iloc[:,15].copy(deep=True)
-    df_copy.iloc[:,31]=local_df.iloc[:,5].copy(deep=True)
-    df_copy.iloc[:,32]=local_df.iloc[:,2].copy(deep=True)
-
-    df_copy.rename(columns={df_copy.columns[8]: 'Size 1'},inplace=True)
-
-    print(list(df_copy.columns.values))
-
-    #WE CONVERT COLUMN 20 to numeric (in case it's read as string)
-    df_copy.iloc[:,20] = df_copy.iloc[:,20].astype(float)
-
-    df_copy.iloc[:,4]=(((df_copy.iloc[:,20]/1.2)/1.96)*0.96)
-    from babel.numbers import format_currency
-    df_copy.iloc[:,4] = df_copy.iloc[:,4].apply(lambda x: format_currency(x, currency="GBP", locale="en_GB"))
-    df_copy.iloc[:,5] = df_copy.iloc[:,5].apply(lambda x: format_currency(x, currency="GBP", locale="en_GB"))
-
-    print(((df_copy.iloc[:,20]/1.2)/1.96)*0.96)
-    #df_copy.iloc[:,2]=df_copy.iloc[:,2].str.replace("'","")
-    df_copy.iloc[:,2] = df_copy.iloc[:,2].astype(str).str.replace("'","")
-
-
-    #df_copy.iloc[:,24]=df_copy.iloc[:,24].str.replace("'","")
-    df_copy.iloc[:,24] = df_copy.iloc[:,24].astype(str).str.replace("'","")
-
-    print("SKU")
-    print(df_copy.iloc[:,2])
-
-
-
-
-
-
-    #rename specific column names
-
-    #df_copy.rename(columns = {'Variant Inventory Qty':'Stock Count','Variant Grams' : 'Weight (grams)'}, inplace = True)
-
-    #df_copy.rename(columns = {'Option2 Value':'Colour','Option1 Value' : 'Size 1'}, inplace = True)
-
-    #df_copy.rename(columns = {'Vendor':'Brand','Title' : 'Product Name'}, inplace = True)
-    #df_copy.rename(columns = {'Body (HTML)':'Long Description'}, inplace = True)
-
-    #df_copy.rename(columns={df_copy.columns[4]: 'Unit Cost'},inplace=True)
-
-
-    print(list(df_copy.columns.values))
-
-
-    #df_copy.rename(columns={df_copy.columns[31]: 'Short Description'},inplace=True)
-    #df_copy.rename(columns={df_copy.columns[2]: 'Vendor SKU'},inplace=True)
-    df_copy.rename(columns={df_copy.columns[6]: 'Colour Code (Simple Colour)'},inplace=True)
-    ##IN COLUMN H (6), WE HAVE SOME TAGS AND WE WANT TO GET THE TAG "MEN, WOMEN, LADY OR BOTH (UNISEX)"
-    #WE ARE GETTING THAT INFO BEFORE REMOVING DATA FROM 6
-    for index, row in df_copy.iterrows():
-        if index==0:
-            print(row['Colour Code (Simple Colour)'])
-        if " mens" in str(row['Colour Code (Simple Colour)']):
-            if " womens" in str(row['Colour Code (Simple Colour)']):
-                df_copy.iloc[index,12]="Unisex"
-            else:
-                df_copy.iloc[index,12]="Mens"
-            
-        if " womens" in str(row['Colour Code (Simple Colour)']):
-            if " mens" in str(row['Colour Code (Simple Colour)']):
-                df_copy.iloc[index,12]="Unisex"
-            else:
-                df_copy.iloc[index,12]="Womens"
-        if " ladys" in str(row['Colour Code (Simple Colour)']):
-                df_copy.iloc[index,12]="Ladys"
-        if index==0:
-            print(row[12])
-    print(df_copy.iloc[:,12])
-
-
-
-    df_copy.iloc[:,6] = ""
-    #Style Number	Product Name	Vendor SKU	UPC/EAN	Unit Cost	Unit MSRP 	Colour Code (Simple Colour)	Colour	
-    df_copy.rename(columns={df_copy.columns[0]: 'Style Number'},inplace=True)
-    df_copy.rename(columns={df_copy.columns[1]: 'Product Name'},inplace=True)
-    df_copy.rename(columns={df_copy.columns[2]: 'Vendor SKU'},inplace=True)
-    df_copy.rename(columns={df_copy.columns[3]: 'UPC/EAN'},inplace=True)
-    df_copy.rename(columns={df_copy.columns[4]: 'Unit Cost'},inplace=True)
-    df_copy.rename(columns={df_copy.columns[5]: 'Unit MSRP'},inplace=True)
-    df_copy.rename(columns={df_copy.columns[6]: 'Colour Code (Simple Colour)'},inplace=True)
-    print(df_copy.columns[6])
-    df_copy.rename(columns={df_copy.columns[7]: 'Colour'},inplace=True)
-    #Size 1	Size 2	Brand 	Year or Season	Gender	Manufacturer Part Code	Other Barcode	VAT	Pack Qty	
-    df_copy.rename(columns={df_copy.columns[8]: 'Size 1'},inplace=True)
-    df_copy.rename(columns={df_copy.columns[9]: 'Size 2'},inplace=True)
-    df_copy.rename(columns={df_copy.columns[10]: 'Brand'},inplace=True)
-    df_copy.rename(columns={df_copy.columns[11]: 'Year of Season'},inplace=True)
-    df_copy.rename(columns={df_copy.columns[12]: 'Gender'},inplace=True)
-    df_copy.rename(columns={df_copy.columns[13]: 'Manufacturer Part Code'},inplace=True)
-    df_copy.rename(columns={df_copy.columns[14]: 'Other Bar Code'},inplace=True)
-    df_copy.rename(columns={df_copy.columns[15]: 'VAT'},inplace=True)
-    df_copy.rename(columns={df_copy.columns[16]: 'Pack Qty'},inplace=True)
-    #Stock Count	Price Band 1	Price Band 2 	IE VAT	Unit Cost in Euros	MSRP in Euros	
-    df_copy.rename(columns={df_copy.columns[17]: 'Stock Count'},inplace=True)
-    df_copy.rename(columns={df_copy.columns[18]: 'Price Band 1'},inplace=True)
-    df_copy.rename(columns={df_copy.columns[19]: 'Price Band 2'},inplace=True)
-    df_copy.rename(columns={df_copy.columns[20]: 'IE VAT'},inplace=True)
-    df_copy.rename(columns={df_copy.columns[21]: 'Unit Cost in Euros'},inplace=True)
-    df_copy.rename(columns={df_copy.columns[22]: 'MSRP in Euros'},inplace=True)
-    #Commodity Codes	Country of Origin	Image (multiple images can be added in separate columns if available)	
-    df_copy.rename(columns={df_copy.columns[23]: 'Commodity Codes'},inplace=True)
-    df_copy.rename(columns={df_copy.columns[24]: 'Country of Origin'},inplace=True)
-    #Weight	Short Description	Long Description	Video Link		
-    df_copy.rename(columns={df_copy.columns[30]: 'Weight'},inplace=True)
-    df_copy.rename(columns={df_copy.columns[31]: 'Short Description'},inplace=True)
-    df_copy.rename(columns={df_copy.columns[32]: 'Long Description'},inplace=True)
-    df_copy.rename(columns={df_copy.columns[33]: 'Video Link'},inplace=True)
-
-
-
-
-
-
-
-    df_copy.iloc[:,9] = ""
-
-    df_copy.iloc[:,13] = ""
-
-    df_copy.iloc[:,14] = ""
-
-    df_copy.iloc[:,16] = ""
-
-    df_copy.iloc[:,18] = ""
-
-    df_copy.iloc[:,19] = ""
-
-    df_copy.iloc[:,20] = ""
-
-    df_copy.iloc[:,21] = ""
-
-    df_copy.iloc[:,22] = ""
-    #df_copy.rename(columns={df_copy.columns[26]: 'Weight (Grams)'},inplace=True)
-
-    #df_copy.iloc[:,26] = ""
-
-    df_copy.iloc[:,33] = ""
-
-
-
-    #df_copy.iloc[:,5] = " "
-    df_copy.iloc[:,15] = "20"
-
-    print(list(df_copy.columns.values))
-
-    #Column Y in the export and this code should go into both Columns C and D in the conversion with the titles “Vendor SKU” and  “UPC/EAN” It is replicated for a complicated reason that I won’t explain here, but Column Y in the export should go into both Column C and D in the conversion
-    df_copy.iloc[:,3] = df_copy.iloc[:,2]
-    df_copy.columns.values[10] = 'Brand'
-    df_copy.iloc[:,11] = ""
-    df_copy.iloc[:,22] = ""
-    #df_copy.rename(columns={df_copy.columns[30]: 'Weight (Grams)'},inplace=True)
-
-
-    print("SKU")
-    print(df_copy.iloc[:,2])
-
-
-    #DATA COMING FROM THE OTHER CSV FILE
-
-    df_copy.iloc[:,23] = ""
-
-
-    df_copy.iloc[:,24] = ""
-
-    #WARNING: HEADER IS IN SECOND ROW. WE DONT HAVE INTO ACCOUNT FIRST ROW
-    #df2 = pd.read_excel('C:/Users/15572890/Desktop/I+D/MarksCsvConversion/inventory_export_12.xlsx',engine="openpyxl", header=1)
-
-
-    #WE HAVE TO REORDER COLUMNS COO and HS Code in df2 in order to match the index order of df 
-    #list1=df_copy.set_index('Vendor SKU').T.to_dict('list')
-    #print(list1)
-    new_index=df['Variant SKU']
-    boolean = df['Variant SKU'].duplicated().any()
-    #print(boolean)
-    boolean = df2['SKU'].duplicated().any()
-    #print(boolean)
-    duplicateRows2 = df2[df2.duplicated(['SKU'],keep = False)]
-    #print(duplicateRows2['SKU'])
-
-    duplicateRows = df[df.duplicated(['Variant SKU'],keep = False)]
-    #print(duplicateRows)
-    #print(duplicateRows['Variant SKU'])
-    #print(new_index)
-    df2=df2.set_index('SKU')
-    #print(df2)
-    #i=df2.index
-    #for x in i:
-    #    print(x)
-    df2.reindex(new_index)
-    #i=df2.index
-    #for x in i:
-    #    print(x)
-    #print(df2)
-    #print(df2.index)
-    #df3 = pd.DataFrame(students, index=['a', 'b', 'c', 'd', 'e'])
-    #print("Original DataFrame: ")
-    #print(df)
-
-
-
-
-
-
-
-
-    print("TERMINE")
-
-    df_copy.iloc[:,24] = df2.loc[:,'COO']
-    df_copy.iloc[:,23] = df2.loc[:,'HS Code']
-
-    df_copy['Commodity Codes']=df2['HS Code'].values
-    df_copy['Country of Origin']=df2['COO'].values
-
-
-    #print(df2.loc[:,'COO'])
-    #print(df2.loc[:,'HS Code'])
-    #print(df_copy.iloc[:,24])
-    #print(df_copy.iloc[:,23])
-    print("SKU")
-    print(df_copy.iloc[:,2])
-
-
-
-    #WE COMPLETE THE DATAFRMAE WITH DUMMY COLUMNS TILL THE MAXIMUM DESIRED NUMBER
-    header_list=[]
-    for i in range(49,58):
-        #df.insert(i, "Dummy", [], True)
-        header_list.append(str(i))
-        df_copy[str(i)]=''
-
-
-
-    column_indices=[]
-    for i in range(0,24):
-        column_indices.append(34+i)
-
-    #Tech Specs	Size Chart	Geometry Chart	Frame	Rear Shock	Fork
-    #Headset	Stem	Handlebar	Bar Tape / Grip	Brakes Levers	Brake Calipers	Tyres	Wheels	Front Derailleur	
-    #Rear Derailleur	Shift Levers	Chain	Cassette	Chainset	Bottom Bracket	Pedals	Saddle	Seatpost
-
-    old_names = df_copy.columns[column_indices]
-    new_names = ['Tech Specs','Size Chart','Geometry Chart','Frame',	'Rear Shock',	'Fork',	'Headset',	'Stem',	'Handlebar',	'Bar Tape / Grip',	'Brakes Levers',	'Brake Calipers',	'Tyres',	'Wheels',	'Front Derailleur',	'Rear Derailleur',	'Shift Levers'	,'Chain'	,'Cassette'	,'Chainset'	,'Bottom Bracket',	'Pedals',	'Saddle',	'Seatpost']
-    old_names = df_copy.columns[column_indices]
-    df_copy.rename(columns=dict(zip(old_names, new_names)), inplace=True)
-
-
-    df_copy.iloc[:,34:58]=''
-
-
-    print("SKUf")
-    print(df_copy.iloc[:,2])
-    #print(df_copy.iloc[:,3])
-
-    ## Rename all columns with list
-    #cols = ['Courses','Courses_Fee','Courses_Duration']
-    #df_copy.columns = cols
-    #print(df.columns)
-
-
-    ###################
-    #PUT IMAGES IN A SIGNLE ROW: WE LOOK FOR IMAGES COMING FROM COMMON NAMES
-    #Shopify exports the image file as https:// links in an odd way. Instead of attributing image 1, image 2, and image 3 etc in dedicated 
-    #and separate columns, it spreads them across the sizes for the related product in the same column (Column Z “Image Src”). 
-    #Column AA in the Shopify export csv just shows the image position instead.  We need to find a solution. 
-    #We need to be able to provide https// image links in separate columns for each product and size. For example, if a product has 3 images, 
-    #these need to be converted into Citrus Lime CSV columns Column Z “Image 1”, Column AA “Image 2”, Column AB “Image 3”, Column AC “Image 4” 
-    #etc
-    ####################
-    #region imagesRow2Column
-    #We get the list of rows with NAN data in Product Name column (same product name but different sizes (XS, XL...). Each of these rows has a image scr link
-    list_col=df_copy.loc[pd.isna(df_copy.loc[:,'Product Name']), :].index
-    images=df_copy.loc[list_col,'Image Src']
-    list_end=[]
-    for row in df_copy.index:
-        #NotNA gets rows where Product Name column has a name in it (first image and row where we should add the images)
-        if pd.notna(df_copy.loc[row,'Product Name']):
-            #print(df_copy.loc[row,'Product Name'])
-            rowNotNa=row
-            i=1
-            #j=1
-            list_img=[]  
-            #WE INCLUDE IN THE LIST THE FIRST IMAGE
-            list_img.append(df_copy.loc[row,'Image Src'])
-            while pd.isna(df_copy.loc[row+i,'Product Name']) and row+i<len(df_copy.index)-1:
-                #WE ADD THE REST OF THE IMAGES (FOLLOWING ROWS)
-                if "http" in str(df_copy.loc[row+i,'Image Src']):
-                    list_img.append(df_copy.loc[row+i,'Image Src'])      
-                i=i+1
-            list_end.append(list_img)
-
-    #IN list_end WE HAVE ALL OF THE IMAGES FOR EACH PRODUCT NAME
-    index_nonnan=df_copy.loc[pd.notna(df_copy.loc[:,'Product Name']), :].index
-    max=0
-    for i in range(len(list_end)):
-        if max<len(list_end[i]):
-            max=len(list_end[i])
-    print("SKUf")
-    print(df_copy.iloc[:,2])
-
-    #WE CHANGE THE COLUMN NAME OF THE COLUMNS WHERE THERE ARE IMAGES: EACH COLUMN IS CALLED "Image x"
-    #We first delete old values in the Image columns
-    for j in range(max):
-            df_copy.iloc[:,25+j]=''
-
-    counter=0
-    for index in index_nonnan:   
-        for j in range(len(list_end[counter])):
-       
-        
-            if list_end[counter][j]!='nan':
-                df_copy.iloc[index,25+j]=list_end[counter][j]
-                df_copy.rename(columns={df_copy.columns[25+j]: 'Image'+str(j+1)},inplace=True)
-
-        counter=counter+1
-    print("SKUf")
-    print(df_copy.iloc[:,2])
-    #WE HAVE TO FILL NAN ROWS (SAME PRODUCT BUT DIFFERENT SIZES) WITH THE SAME IMAGES THAT IN NON NAN ROWS (MAIN PRODUCT-SIZE)
-    listImages=[None] * max
-    list1=[None] * max
-    list2=[None] * max
-    list3=[None] * max
-    list4=[None] * max
-    list5=[None] * max
-    for index, row in df_copy.iterrows():
-        #NotNA gets rows where Product Name column has a name in it (first image and row where we should add the images)
-        #print(df_copy.iloc[index,1])
-        if pd.notna(df_copy.iloc[index,1]):
-            for j in range(0,max):
-                listImages[j]=str((df_copy.iloc[index,25+j]))
-                #list1[j]=str((df_copy.iloc[index,1+j]))
-                #list2[j]=str((df_copy.iloc[index,10+j]))
-                #list3[j]=str((df_copy.iloc[index,12+j]))
-                #list4[j]=str((df_copy.iloc[index,31+j]))
-                #list5[j]=str((df_copy.iloc[index,32+j]))
-                list1[j]=str((df_copy.iloc[index,1]))
-                list2[j]=str((df_copy.iloc[index,10]))
-                list3[j]=str((df_copy.iloc[index,12]))
-                list4[j]=str((df_copy.iloc[index,31]))
-                list5[j]=str((df_copy.iloc[index,32]))
-            
-        else:
-            for j in range(0,max):
-                df_copy.iloc[index,25+j]=listImages[j]
-                #df_copy.iloc[index,1+j]=list1[j]
-                #df_copy.iloc[index,10+j]=list2[j]
-                #df_copy.iloc[index,12+j]=list3[j]
-                #df_copy.iloc[index,31+j]=list4[j]
-                #df_copy.iloc[index,32+j]=list5[j]
-                df_copy.iloc[index,1]=list1[j]
-                df_copy.iloc[index,10]=list2[j]
-                df_copy.iloc[index,12]=list3[j]
-                df_copy.iloc[index,31]=list4[j]
-                df_copy.iloc[index,32]=list5[j]
-
-    #endregion
-
-    print("SKUf")
-    print(df_copy.iloc[:,2])
-    #print(df_copy.iloc[:,3])
-
-    ###################################################################################
-    #df_copy.to_excel('C:/Users/15572890/Desktop/I+D/MarksCsvConversion/Validation2/OCCHIO-Cycle-Data-File_st.xlsx',index=False)
-
-
-
-    #df_copy.to_csv('C:/Users/15572890/Desktop/I+D/MarksCsvConversion/Validation2/OCCHIO-Cycle-Data-File_st.csv',index=False, encoding='utf_8_sig')
-    return df_copy
-    
-    
-def convert_df(df):
-   return df.to_csv(index=False).encode('utf_8_sig')
-
-if uploaded_file and uploaded_file2:
-    df3=ConvertCitrus(df,df2)
-
-
-
-    csv = convert_df(df3)
-    
-    st.download_button(
-       "Press to Download",
-       csv,
-       "file.csv",
-       "text/csv",
-       key='download-csv'
-    )
+import streamlit as st #line:1
+import pandas as pd #line:2
+uploaded_file =st .file_uploader ("Choose product file",type ="csv")#line:4
+if uploaded_file :#line:6
+    df =pd .read_csv (uploaded_file ,encoding ='utf8')#line:8
+uploaded_file2 =st .file_uploader ("Choose inventory file",type ="csv")#line:11
+if uploaded_file2 :#line:13
+    df2 =pd .read_csv (uploaded_file2 ,encoding ='utf8')#line:15
+def ConvertCitrus (O00O000OOOO0O00O0 ,OOO0O0O0OOO0O00O0 ):#line:21
+    import RemoveHTMLtags as RHT #line:24
+    O0OOOOOO00O000OO0 =str ('<style type=')+str ('"')+str ('"')+str ('text/css')+str ('"')+str ('"')+str ('><!--')#line:32
+    OOOO00O00000O00O0 =['<p class=','"p1"','data-mce-fragment="1">,','<b data-mce-fragment="1">','<i data-mce-fragment="1">','<p>','</p>','<p*>','<ul>','</ul>','</i>','</b>','</p>','</br>','<li>','</li>','<br>','<strong>','</strong>','<span*>','</span>','"utf-8"','UTF-8','<a href*>','</a>','<meta charset=utf-8>',';;','<em>','</em>','"','<meta charset=','utf-8>','<p>','<p','data-mce-fragment=1',';','<style type=','<style type=','><!--','text/css','<style type=\"\"text/css\"\"><!--','--></style>','td {border: 1px solid #ccc','}br {mso-data-placement:same-cell','}','>']#line:41
+    for O0OO0OOOOO0O0O000 ,O0O0O00O00OO00OO0 in O00O000OOOO0O00O0 .iterrows ():#line:55
+        O00O000OOOO0O00O0 .iloc [O0OO0OOOOO0O0O000 ,2 ]=RHT .remove_tags (str (O00O000OOOO0O00O0 .iloc [O0OO0OOOOO0O0O000 ,2 ]))#line:56
+    print (O00O000OOOO0O00O0 .iloc [:,2 ])#line:58
+    O00O000OOOO0O00O0 .iloc [:,2 ]=pd .Series (O00O000OOOO0O00O0 .iloc [:,2 ],dtype ="string")#line:63
+    print (O00O000OOOO0O00O0 .iloc [:,2 ].dtype )#line:64
+    O0000OOOOO000000O =O00O000OOOO0O00O0 .columns .tolist ()#line:88
+    O0000OO0OOO0OOO0O =O0000OOOOO000000O .copy ()#line:89
+    O0000OO0OOO0OOO0O [1 ]=O0000OOOOO000000O [1 ]#line:90
+    O0000OO0OOO0OOO0O [17 ]=O0000OOOOO000000O [17 ]#line:92
+    OO00O0000O0OO0000 =O00O000OOOO0O00O0 [O0000OO0OOO0OOO0O ].copy (deep =True )#line:113
+    print ("SKU")#line:114
+    print (O00O000OOOO0O00O0 .iloc [:,24 ])#line:115
+    OO0OO000O0OO0000O =O00O000OOOO0O00O0 .copy (deep =True )#line:117
+    OO00O0000O0OO0000 .iloc [:,0 ]=OO0OO000O0OO0000O .iloc [:,13 ].copy (deep =True )#line:119
+    OO00O0000O0OO0000 .iloc [:,5 ]=OO0OO000O0OO0000O .iloc [:,20 ].copy (deep =True )#line:120
+    OO00O0000O0OO0000 .iloc [:,7 ]=OO0OO000O0OO0000O .iloc [:,11 ].copy (deep =True )#line:121
+    OO00O0000O0OO0000 .iloc [:,2 ]=OO0OO000O0OO0000O .iloc [:,24 ].copy (deep =True )#line:123
+    OO00O0000O0OO0000 .iloc [:,8 ]=OO0OO000O0OO0000O .iloc [:,9 ].copy (deep =True )#line:125
+    OO00O0000O0OO0000 .iloc [:,10 ]=OO0OO000O0OO0000O .iloc [:,3 ].copy (deep =True )#line:126
+    OO00O0000O0OO0000 .rename (columns ={OO00O0000O0OO0000 .columns [10 ]:'Brand'},inplace =True )#line:127
+    OO00O0000O0OO0000 .columns .values [10 ]='Brand'#line:128
+    OO00O0000O0OO0000 .iloc [:,30 ]=OO0OO000O0OO0000O .iloc [:,15 ].copy (deep =True )#line:130
+    OO00O0000O0OO0000 .iloc [:,31 ]=OO0OO000O0OO0000O .iloc [:,5 ].copy (deep =True )#line:131
+    OO00O0000O0OO0000 .iloc [:,32 ]=OO0OO000O0OO0000O .iloc [:,2 ].copy (deep =True )#line:132
+    OO00O0000O0OO0000 .rename (columns ={OO00O0000O0OO0000 .columns [8 ]:'Size 1'},inplace =True )#line:134
+    print (list (OO00O0000O0OO0000 .columns .values ))#line:136
+    OO00O0000O0OO0000 .iloc [:,20 ]=OO00O0000O0OO0000 .iloc [:,20 ].astype (float )#line:139
+    OO00O0000O0OO0000 .iloc [:,4 ]=(((OO00O0000O0OO0000 .iloc [:,20 ]/1.2 )/1.96 )*0.96 )#line:141
+    from babel .numbers import format_currency #line:142
+    OO00O0000O0OO0000 .iloc [:,4 ]=OO00O0000O0OO0000 .iloc [:,4 ].apply (lambda O0OOO0O00OOO0OO0O :format_currency (O0OOO0O00OOO0OO0O ,currency ="GBP",locale ="en_GB"))#line:143
+    OO00O0000O0OO0000 .iloc [:,5 ]=OO00O0000O0OO0000 .iloc [:,5 ].apply (lambda O000O0OO000O0000O :format_currency (O000O0OO000O0000O ,currency ="GBP",locale ="en_GB"))#line:144
+    print (((OO00O0000O0OO0000 .iloc [:,20 ]/1.2 )/1.96 )*0.96 )#line:146
+    OO00O0000O0OO0000 .iloc [:,2 ]=OO00O0000O0OO0000 .iloc [:,2 ].astype (str ).str .replace ("'","")#line:148
+    OO00O0000O0OO0000 .iloc [:,24 ]=OO00O0000O0OO0000 .iloc [:,24 ].astype (str ).str .replace ("'","")#line:152
+    print ("SKU")#line:154
+    print (OO00O0000O0OO0000 .iloc [:,2 ])#line:155
+    print (list (OO00O0000O0OO0000 .columns .values ))#line:174
+    OO00O0000O0OO0000 .rename (columns ={OO00O0000O0OO0000 .columns [6 ]:'Colour Code (Simple Colour)'},inplace =True )#line:179
+    for O0OO0OOOOO0O0O000 ,O0O0O00O00OO00OO0 in OO00O0000O0OO0000 .iterrows ():#line:182
+        if O0OO0OOOOO0O0O000 ==0 :#line:183
+            print (O0O0O00O00OO00OO0 ['Colour Code (Simple Colour)'])#line:184
+        if " mens"in str (O0O0O00O00OO00OO0 ['Colour Code (Simple Colour)']):#line:185
+            if " womens"in str (O0O0O00O00OO00OO0 ['Colour Code (Simple Colour)']):#line:186
+                OO00O0000O0OO0000 .iloc [O0OO0OOOOO0O0O000 ,12 ]="Unisex"#line:187
+            else :#line:188
+                OO00O0000O0OO0000 .iloc [O0OO0OOOOO0O0O000 ,12 ]="Mens"#line:189
+        if " womens"in str (O0O0O00O00OO00OO0 ['Colour Code (Simple Colour)']):#line:191
+            if " mens"in str (O0O0O00O00OO00OO0 ['Colour Code (Simple Colour)']):#line:192
+                OO00O0000O0OO0000 .iloc [O0OO0OOOOO0O0O000 ,12 ]="Unisex"#line:193
+            else :#line:194
+                OO00O0000O0OO0000 .iloc [O0OO0OOOOO0O0O000 ,12 ]="Womens"#line:195
+        if " ladys"in str (O0O0O00O00OO00OO0 ['Colour Code (Simple Colour)']):#line:196
+                OO00O0000O0OO0000 .iloc [O0OO0OOOOO0O0O000 ,12 ]="Ladys"#line:197
+        if O0OO0OOOOO0O0O000 ==0 :#line:198
+            print (O0O0O00O00OO00OO0 [12 ])#line:199
+    print (OO00O0000O0OO0000 .iloc [:,12 ])#line:200
+    OO00O0000O0OO0000 .iloc [:,6 ]=""#line:204
+    OO00O0000O0OO0000 .rename (columns ={OO00O0000O0OO0000 .columns [0 ]:'Style Number'},inplace =True )#line:206
+    OO00O0000O0OO0000 .rename (columns ={OO00O0000O0OO0000 .columns [1 ]:'Product Name'},inplace =True )#line:207
+    OO00O0000O0OO0000 .rename (columns ={OO00O0000O0OO0000 .columns [2 ]:'Vendor SKU'},inplace =True )#line:208
+    OO00O0000O0OO0000 .rename (columns ={OO00O0000O0OO0000 .columns [3 ]:'UPC/EAN'},inplace =True )#line:209
+    OO00O0000O0OO0000 .rename (columns ={OO00O0000O0OO0000 .columns [4 ]:'Unit Cost'},inplace =True )#line:210
+    OO00O0000O0OO0000 .rename (columns ={OO00O0000O0OO0000 .columns [5 ]:'Unit MSRP'},inplace =True )#line:211
+    OO00O0000O0OO0000 .rename (columns ={OO00O0000O0OO0000 .columns [6 ]:'Colour Code (Simple Colour)'},inplace =True )#line:212
+    print (OO00O0000O0OO0000 .columns [6 ])#line:213
+    OO00O0000O0OO0000 .rename (columns ={OO00O0000O0OO0000 .columns [7 ]:'Colour'},inplace =True )#line:214
+    OO00O0000O0OO0000 .rename (columns ={OO00O0000O0OO0000 .columns [8 ]:'Size 1'},inplace =True )#line:216
+    OO00O0000O0OO0000 .rename (columns ={OO00O0000O0OO0000 .columns [9 ]:'Size 2'},inplace =True )#line:217
+    OO00O0000O0OO0000 .rename (columns ={OO00O0000O0OO0000 .columns [10 ]:'Brand'},inplace =True )#line:218
+    OO00O0000O0OO0000 .rename (columns ={OO00O0000O0OO0000 .columns [11 ]:'Year of Season'},inplace =True )#line:219
+    OO00O0000O0OO0000 .rename (columns ={OO00O0000O0OO0000 .columns [12 ]:'Gender'},inplace =True )#line:220
+    OO00O0000O0OO0000 .rename (columns ={OO00O0000O0OO0000 .columns [13 ]:'Manufacturer Part Code'},inplace =True )#line:221
+    OO00O0000O0OO0000 .rename (columns ={OO00O0000O0OO0000 .columns [14 ]:'Other Bar Code'},inplace =True )#line:222
+    OO00O0000O0OO0000 .rename (columns ={OO00O0000O0OO0000 .columns [15 ]:'VAT'},inplace =True )#line:223
+    OO00O0000O0OO0000 .rename (columns ={OO00O0000O0OO0000 .columns [16 ]:'Pack Qty'},inplace =True )#line:224
+    OO00O0000O0OO0000 .rename (columns ={OO00O0000O0OO0000 .columns [17 ]:'Stock Count'},inplace =True )#line:226
+    OO00O0000O0OO0000 .rename (columns ={OO00O0000O0OO0000 .columns [18 ]:'Price Band 1'},inplace =True )#line:227
+    OO00O0000O0OO0000 .rename (columns ={OO00O0000O0OO0000 .columns [19 ]:'Price Band 2'},inplace =True )#line:228
+    OO00O0000O0OO0000 .rename (columns ={OO00O0000O0OO0000 .columns [20 ]:'IE VAT'},inplace =True )#line:229
+    OO00O0000O0OO0000 .rename (columns ={OO00O0000O0OO0000 .columns [21 ]:'Unit Cost in Euros'},inplace =True )#line:230
+    OO00O0000O0OO0000 .rename (columns ={OO00O0000O0OO0000 .columns [22 ]:'MSRP in Euros'},inplace =True )#line:231
+    OO00O0000O0OO0000 .rename (columns ={OO00O0000O0OO0000 .columns [23 ]:'Commodity Codes'},inplace =True )#line:233
+    OO00O0000O0OO0000 .rename (columns ={OO00O0000O0OO0000 .columns [24 ]:'Country of Origin'},inplace =True )#line:234
+    OO00O0000O0OO0000 .rename (columns ={OO00O0000O0OO0000 .columns [30 ]:'Weight'},inplace =True )#line:236
+    OO00O0000O0OO0000 .rename (columns ={OO00O0000O0OO0000 .columns [31 ]:'Short Description'},inplace =True )#line:237
+    OO00O0000O0OO0000 .rename (columns ={OO00O0000O0OO0000 .columns [32 ]:'Long Description'},inplace =True )#line:238
+    OO00O0000O0OO0000 .rename (columns ={OO00O0000O0OO0000 .columns [33 ]:'Video Link'},inplace =True )#line:239
+    OO00O0000O0OO0000 .iloc [:,9 ]=""#line:247
+    OO00O0000O0OO0000 .iloc [:,13 ]=""#line:249
+    OO00O0000O0OO0000 .iloc [:,14 ]=""#line:251
+    OO00O0000O0OO0000 .iloc [:,16 ]=""#line:253
+    OO00O0000O0OO0000 .iloc [:,18 ]=""#line:255
+    OO00O0000O0OO0000 .iloc [:,19 ]=""#line:257
+    OO00O0000O0OO0000 .iloc [:,20 ]=""#line:259
+    OO00O0000O0OO0000 .iloc [:,21 ]=""#line:261
+    OO00O0000O0OO0000 .iloc [:,22 ]=""#line:263
+    OO00O0000O0OO0000 .iloc [:,33 ]=""#line:268
+    OO00O0000O0OO0000 .iloc [:,15 ]="20"#line:273
+    print (list (OO00O0000O0OO0000 .columns .values ))#line:275
+    OO00O0000O0OO0000 .iloc [:,3 ]=OO00O0000O0OO0000 .iloc [:,2 ]#line:278
+    OO00O0000O0OO0000 .columns .values [10 ]='Brand'#line:279
+    OO00O0000O0OO0000 .iloc [:,11 ]=""#line:280
+    OO00O0000O0OO0000 .iloc [:,22 ]=""#line:281
+    print ("SKU")#line:285
+    print (OO00O0000O0OO0000 .iloc [:,2 ])#line:286
+    OO00O0000O0OO0000 .iloc [:,23 ]=""#line:291
+    OO00O0000O0OO0000 .iloc [:,24 ]=""#line:294
+    OO0OOOO0O0OOO00O0 =O00O000OOOO0O00O0 ['Variant SKU']#line:303
+    OO0O00O00O00OO0O0 =O00O000OOOO0O00O0 ['Variant SKU'].duplicated ().any ()#line:304
+    OO0O00O00O00OO0O0 =OOO0O0O0OOO0O00O0 ['SKU'].duplicated ().any ()#line:306
+    O000OO000O0000O00 =OOO0O0O0OOO0O00O0 [OOO0O0O0OOO0O00O0 .duplicated (['SKU'],keep =False )]#line:308
+    OOOO0OO00O0O00000 =O00O000OOOO0O00O0 [O00O000OOOO0O00O0 .duplicated (['Variant SKU'],keep =False )]#line:311
+    OOO0O0O0OOO0O00O0 =OOO0O0O0OOO0O00O0 .set_index ('SKU')#line:315
+    OOO0O0O0OOO0O00O0 .reindex (OO0OOOO0O0OOO00O0 )#line:320
+    print ("TERMINE")#line:337
+    OO00O0000O0OO0000 .iloc [:,24 ]=OOO0O0O0OOO0O00O0 .loc [:,'COO']#line:339
+    OO00O0000O0OO0000 .iloc [:,23 ]=OOO0O0O0OOO0O00O0 .loc [:,'HS Code']#line:340
+    OO00O0000O0OO0000 ['Commodity Codes']=OOO0O0O0OOO0O00O0 ['HS Code'].values #line:342
+    OO00O0000O0OO0000 ['Country of Origin']=OOO0O0O0OOO0O00O0 ['COO'].values #line:343
+    print ("SKU")#line:350
+    print (OO00O0000O0OO0000 .iloc [:,2 ])#line:351
+    OO00O00OOOO0O00O0 =[]#line:356
+    for OO0OOOO000OO0OO00 in range (49 ,58 ):#line:357
+        OO00O00OOOO0O00O0 .append (str (OO0OOOO000OO0OO00 ))#line:359
+        OO00O0000O0OO0000 [str (OO0OOOO000OO0OO00 )]=''#line:360
+    O0000000OOOOO0O0O =[]#line:364
+    for OO0OOOO000OO0OO00 in range (0 ,24 ):#line:365
+        O0000000OOOOO0O0O .append (34 +OO0OOOO000OO0OO00 )#line:366
+    OOOO0OO0OO0OO0OOO =OO00O0000O0OO0000 .columns [O0000000OOOOO0O0O ]#line:372
+    O000000O00OOO000O =['Tech Specs','Size Chart','Geometry Chart','Frame','Rear Shock','Fork','Headset','Stem','Handlebar','Bar Tape / Grip','Brakes Levers','Brake Calipers','Tyres','Wheels','Front Derailleur','Rear Derailleur','Shift Levers','Chain','Cassette','Chainset','Bottom Bracket','Pedals','Saddle','Seatpost']#line:373
+    OOOO0OO0OO0OO0OOO =OO00O0000O0OO0000 .columns [O0000000OOOOO0O0O ]#line:374
+    OO00O0000O0OO0000 .rename (columns =dict (zip (OOOO0OO0OO0OO0OOO ,O000000O00OOO000O )),inplace =True )#line:375
+    OO00O0000O0OO0000 .iloc [:,34 :58 ]=''#line:378
+    print ("SKUf")#line:381
+    print (OO00O0000O0OO0000 .iloc [:,2 ])#line:382
+    O0O0OO000OO0OO000 =OO00O0000O0OO0000 .loc [pd .isna (OO00O0000O0OO0000 .loc [:,'Product Name']),:].index #line:402
+    O000O00000000O0O0 =OO00O0000O0OO0000 .loc [O0O0OO000OO0OO000 ,'Image Src']#line:403
+    O00O00OO0OO000OOO =[]#line:404
+    for O0O0O00O00OO00OO0 in OO00O0000O0OO0000 .index :#line:405
+        if pd .notna (OO00O0000O0OO0000 .loc [O0O0O00O00OO00OO0 ,'Product Name']):#line:407
+            OO00O0OOOO000O0OO =O0O0O00O00OO00OO0 #line:409
+            OO0OOOO000OO0OO00 =1 #line:410
+            OO000OOOOO0OOOO0O =[]#line:412
+            OO000OOOOO0OOOO0O .append (OO00O0000O0OO0000 .loc [O0O0O00O00OO00OO0 ,'Image Src'])#line:414
+            while pd .isna (OO00O0000O0OO0000 .loc [O0O0O00O00OO00OO0 +OO0OOOO000OO0OO00 ,'Product Name'])and O0O0O00O00OO00OO0 +OO0OOOO000OO0OO00 <len (OO00O0000O0OO0000 .index )-1 :#line:415
+                if "http"in str (OO00O0000O0OO0000 .loc [O0O0O00O00OO00OO0 +OO0OOOO000OO0OO00 ,'Image Src']):#line:417
+                    OO000OOOOO0OOOO0O .append (OO00O0000O0OO0000 .loc [O0O0O00O00OO00OO0 +OO0OOOO000OO0OO00 ,'Image Src'])#line:418
+                OO0OOOO000OO0OO00 =OO0OOOO000OO0OO00 +1 #line:419
+            O00O00OO0OO000OOO .append (OO000OOOOO0OOOO0O )#line:420
+    O00OOO00O0OOOOOOO =OO00O0000O0OO0000 .loc [pd .notna (OO00O0000O0OO0000 .loc [:,'Product Name']),:].index #line:423
+    OOOOOO00O00OO000O =0 #line:424
+    for OO0OOOO000OO0OO00 in range (len (O00O00OO0OO000OOO )):#line:425
+        if OOOOOO00O00OO000O <len (O00O00OO0OO000OOO [OO0OOOO000OO0OO00 ]):#line:426
+            OOOOOO00O00OO000O =len (O00O00OO0OO000OOO [OO0OOOO000OO0OO00 ])#line:427
+    print ("SKUf")#line:428
+    print (OO00O0000O0OO0000 .iloc [:,2 ])#line:429
+    for OO0O0000OO0O0O0OO in range (OOOOOO00O00OO000O ):#line:433
+            OO00O0000O0OO0000 .iloc [:,25 +OO0O0000OO0O0O0OO ]=''#line:434
+    O00OO0OOOOO0O000O =0 #line:436
+    for O0OO0OOOOO0O0O000 in O00OOO00O0OOOOOOO :#line:437
+        for OO0O0000OO0O0O0OO in range (len (O00O00OO0OO000OOO [O00OO0OOOOO0O000O ])):#line:438
+            if O00O00OO0OO000OOO [O00OO0OOOOO0O000O ][OO0O0000OO0O0O0OO ]!='nan':#line:441
+                OO00O0000O0OO0000 .iloc [O0OO0OOOOO0O0O000 ,25 +OO0O0000OO0O0O0OO ]=O00O00OO0OO000OOO [O00OO0OOOOO0O000O ][OO0O0000OO0O0O0OO ]#line:442
+                OO00O0000O0OO0000 .rename (columns ={OO00O0000O0OO0000 .columns [25 +OO0O0000OO0O0O0OO ]:'Image'+str (OO0O0000OO0O0O0OO +1 )},inplace =True )#line:443
+        O00OO0OOOOO0O000O =O00OO0OOOOO0O000O +1 #line:445
+    print ("SKUf")#line:446
+    print (OO00O0000O0OO0000 .iloc [:,2 ])#line:447
+    OO0OOO000OO0O0O0O =[None ]*OOOOOO00O00OO000O #line:449
+    OOOOO00O0OOOOOOOO =[None ]*OOOOOO00O00OO000O #line:450
+    O0O0O0000O0OO0OOO =[None ]*OOOOOO00O00OO000O #line:451
+    O000O0O0OO00OOO00 =[None ]*OOOOOO00O00OO000O #line:452
+    O0O0000O00O000OO0 =[None ]*OOOOOO00O00OO000O #line:453
+    O0000O0O000O0OO00 =[None ]*OOOOOO00O00OO000O #line:454
+    for O0OO0OOOOO0O0O000 ,O0O0O00O00OO00OO0 in OO00O0000O0OO0000 .iterrows ():#line:455
+        if pd .notna (OO00O0000O0OO0000 .iloc [O0OO0OOOOO0O0O000 ,1 ]):#line:458
+            for OO0O0000OO0O0O0OO in range (0 ,OOOOOO00O00OO000O ):#line:459
+                OO0OOO000OO0O0O0O [OO0O0000OO0O0O0OO ]=str ((OO00O0000O0OO0000 .iloc [O0OO0OOOOO0O0O000 ,25 +OO0O0000OO0O0O0OO ]))#line:460
+                OOOOO00O0OOOOOOOO [OO0O0000OO0O0O0OO ]=str ((OO00O0000O0OO0000 .iloc [O0OO0OOOOO0O0O000 ,1 ]))#line:466
+                O0O0O0000O0OO0OOO [OO0O0000OO0O0O0OO ]=str ((OO00O0000O0OO0000 .iloc [O0OO0OOOOO0O0O000 ,10 ]))#line:467
+                O000O0O0OO00OOO00 [OO0O0000OO0O0O0OO ]=str ((OO00O0000O0OO0000 .iloc [O0OO0OOOOO0O0O000 ,12 ]))#line:468
+                O0O0000O00O000OO0 [OO0O0000OO0O0O0OO ]=str ((OO00O0000O0OO0000 .iloc [O0OO0OOOOO0O0O000 ,31 ]))#line:469
+                O0000O0O000O0OO00 [OO0O0000OO0O0O0OO ]=str ((OO00O0000O0OO0000 .iloc [O0OO0OOOOO0O0O000 ,32 ]))#line:470
+        else :#line:472
+            for OO0O0000OO0O0O0OO in range (0 ,OOOOOO00O00OO000O ):#line:473
+                OO00O0000O0OO0000 .iloc [O0OO0OOOOO0O0O000 ,25 +OO0O0000OO0O0O0OO ]=OO0OOO000OO0O0O0O [OO0O0000OO0O0O0OO ]#line:474
+                OO00O0000O0OO0000 .iloc [O0OO0OOOOO0O0O000 ,1 ]=OOOOO00O0OOOOOOOO [OO0O0000OO0O0O0OO ]#line:480
+                OO00O0000O0OO0000 .iloc [O0OO0OOOOO0O0O000 ,10 ]=O0O0O0000O0OO0OOO [OO0O0000OO0O0O0OO ]#line:481
+                OO00O0000O0OO0000 .iloc [O0OO0OOOOO0O0O000 ,12 ]=O000O0O0OO00OOO00 [OO0O0000OO0O0O0OO ]#line:482
+                OO00O0000O0OO0000 .iloc [O0OO0OOOOO0O0O000 ,31 ]=O0O0000O00O000OO0 [OO0O0000OO0O0O0OO ]#line:483
+                OO00O0000O0OO0000 .iloc [O0OO0OOOOO0O0O000 ,32 ]=O0000O0O000O0OO00 [OO0O0000OO0O0O0OO ]#line:484
+    print ("SKUf")#line:488
+    print (OO00O0000O0OO0000 .iloc [:,2 ])#line:489
+    return OO00O0000O0OO0000 #line:498
+def convert_df (OO00OO0O00O00OOOO ):#line:501
+   return OO00OO0O00O00OOOO .to_csv (index =False ).encode ('utf_8_sig')#line:502
+if uploaded_file and uploaded_file2 :#line:504
+    df3 =ConvertCitrus (df ,df2 )#line:505
+    csv =convert_df (df3 )#line:509
+    st .download_button ("Press to Download",csv ,"file.csv","text/csv",key ='download-csv')