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MarkStreamlit.py

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+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 = pd.read_csv('C:/Users/15572890/Desktop/I+D/MarksCsvConversion/Validation2/products_export_1 21-10-22.csv', encoding='utf8')
+
+
+    #df2 = pd.read_csv('C:/Users/15572890/Desktop/I+D/MarksCsvConversion/Validation2/inventory_export_1 21-10-22.csv', encoding='utf8')
+    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.csv',index=False, encoding='utf-8')
+    df_copy.to_csv('C:/Users/15572890/Desktop/I+D/MarksCsvConversion/Validation2/OCCHIO-Cycle-Data-File_st.csv',index=False, encoding='utf_8_sig')
+
+
+if uploaded_file and uploaded_file2:
+    ConvertCitrus(df,df2)
+

RemoveHTMLtags.py

@@ -0,0 +1,34 @@
+# Import Module
+from bs4 import BeautifulSoup
+  
+# HTML Document
+HTML_DOC = """
+              <html>
+                <head>
+                    <title> Geeksforgeeks </title>
+                    <style>.call {background-color:black;} </style>
+                    <script>getit</script>
+                </head>
+                <body>
+                    is a
+                    <div>Computer Science portal.</div>
+                </body>
+              </html>
+            """
+  
+# Function to remove tags
+def remove_tags(html):
+  
+    # parse html content
+    soup = BeautifulSoup(html, "html.parser")
+  
+    for data in soup(['style', 'script']):
+        # Remove tags
+        data.decompose()
+  
+    # return data by retrieving the tag content
+    return ' '.join(soup.stripped_strings)
+  
+  
+# Print the extracted data
+print(remove_tags(HTML_DOC))