def get_subsitute():
name = t1.get()
# Get the index of the item that matches the title
idx = indices[name]
# Get the pairwsie similarity scores of all items with that items
sim_scores = list(enumerate(cosine_sim[idx]))
# Sort the items based on the similarity scores
def get_key(elem):
return elem[1]
sim_scores.sort(key=get_key, reverse=True)
# Get the scores of the 10 most similar items
sim_scores = sim_scores[1:11]
# Get the items indices
item_indices = [i[0] for i in sim_scores]
# Return the top 10 most similar items
#print (type( df_new['name'].iloc[item_indices]))
df = ((df_new.iloc[item_indices].sort_values(by='price')))
pt = Table(frame, dataframe=df)
pt.place(x=0, y=300)
pt.show()
def get_comp():
value = t1.get()
df = (support_data[(support_data['name_x'] == value) |
(support_data['name_y'] == value)].sort_values(
by='support', ascending=False).head(10))
pt = Table(frame, dataframe=df)
pt.place(x=0, y=300)
pt.show()
