def home(request):
q = '''match(m:Movie) where m.release_date contains "''' + str(
currdate) + '''" return count(m)'''
n = pd.DataFrame(driver.session().run(q))[0][0]
print(n)
if n > 0 and n <= 6:
b = 0
elif n > 6:
a = range(0, n - 6)
b = random.choice(a)
else:
c = 1000
a = range(0, c - 6)
b = random.choice(a)
query = '''match(m:Movie) where m.release_date contains "''' + str(
currdate) + '''" with m skip ''' + str(b) + ''' limit 6
match(a)-[:ACTED_IN]->(m:Movie)<-[:DIRECTED]-(d) with m, collect(distinct a.name) as actors, collect(distinct d.name) as directors
WITH REDUCE(mergedString = "",word IN actors | mergedString+word+',') as actorsString, REDUCE(mergedString = "",word IN directors | mergedString+word+',') as directorsString, m
RETURN toString(ID(m)) as id, m.imdb_id as imdb_id, m.title as Title, LEFT(actorsString,SIZE(actorsString)-1) as Actors, LEFT(directorsString,SIZE(directorsString)-1) as Directors, m.overview as overview, m.Poster as Poster'''
movie_frame = pd.DataFrame(driver.session().run(query))
movie_frame.columns = [
'id', 'imdb_id', 'Title', 'Actors', 'Directors', 'overview', 'Poster'
]
movie_list = list(movie_frame.head(6).apply(set_movie, axis=1))
print(movie_list)
title = "Top 5 movies"
search_type, search_key = 'All', ''
if request.method == 'POST':
search_type = request.POST['search_type']
search_key = request.POST['search_key']
if len(search_key) > 0:
title = 'Search results for {} in category {}'.format(
search_key, search_type)
movie_list = search_movies(search_type, search_key)
context = {
'movies': movie_list,
'title': title,
'search_key': search_key,
'search_type': search_type,
'radios': 'All Title Actors Directors'.split()
}
return render(request, 'neo4j/search.html', context)
#print("Showing {} results".format(len(movie_list)))
context = {
'movies': movie_list,
'title': title,
'search_key': search_key,
'search_type': search_type,
'radios': 'All Title Actors Directors'.split()
}
#print(context)
return render(request, 'neo4j/index.html', context)
def recommendation_algo_three(key):
query = '''MATCH (m:Movie)
WHERE m.imdb_id = "''' + str(key) + '''"
WITH m, split(m.rec_item, ' | ') AS wsa
unwind wsa[1..] as w1
with w1
match(a )-[:ACTED_IN]->(rec:Movie{title: w1})<-[:DIRECTED]-(d )
with distinct rec, collect(distinct a.name) as actors, collect(distinct d.name) as directors
WITH REDUCE(mergedString = "",word IN actors | mergedString+word+',') as actorsString, REDUCE(mergedString = "",word IN directors | mergedString+word+',') as directorsString, rec
RETURN toString(ID(rec)) as id, rec.imdb_id as imdb_id, rec.title as Title, LEFT(actorsString,SIZE(actorsString)-1) as Actors, LEFT(directorsString,SIZE(directorsString)-1) as Directors, rec.overview as overview, rec.Poster as Poster limit 6'''
#print(mask)
movie_frame = pd.DataFrame(driver.session().run(query))
print(
'@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@'
)
print(len(movie_frame))
if (len(movie_frame) == 0):
return ('Empty')
#movie_frame = pd.DataFrame(driver.session().run(query))
movie_frame.columns = [
'id', 'imdb_id', 'Title', 'Actors', 'Directors', 'overview', 'Poster'
]
movie_list = list(movie_frame.head(6).apply(set_movie, axis=1))
return movie_list
def detail(request, key):
query = '''match(p1:Product)<-[:HasProduct]-(c:Category)
where p1.ProductId = "''' + str(key) + '''"
RETURN toString(p1.ProductId) as id, p1.ProductName as ProductName, p1.Manufacturer as Manufacturer, p1.Style as Style, p1.Finish as Finish, c.Category as Category,p1.Price as Price, p1.poster as Poster limit 6'''
product_frame = pd.DataFrame(driver.session().run(query))
print('%%%%%%%')
product_frame.columns = [
'ProductId', 'ProductName', 'Manufacturer', 'Style', 'Finish',
'Category', 'Price', 'Poster'
]
print(product_frame)
product_frame.index = [0]
context = {
'products': set_product(product_frame.loc[0]),
'algo_one': recommendation_algo_one(key),
'algo_two': recommendation_algo_one(key),
'algo_three': recommendation_algo_three(key),
'search_key': "",
'search_type': 'All',
'radios': 'All Product Manufacturer Category'.split()
}
context['PAGE_NAME'] = context['products'].productname
return render(request, 'neo4j/detail.html', context)
def recommendation_algo_one(key):
query = '''MATCH (m:Movie) WHERE m.imdb_id = "''' + str(key) + '''"
MATCH (m)-[:HAS_GENRE]->(g:Genre)<-[:HAS_GENRE]-(rec:Movie)
WITH m, rec, COUNT(*) AS gs
OPTIONAL MATCH (m)<-[:HAS_MOVIES]-(pc:productionCompany)-[: HAS_MOVIES ]->(rec)
WITH m, rec, gs, COUNT(pc) AS PC
OPTIONAL MATCH (m)<-[: HAS_MOVIES ]-(cn:collectionName)-[: HAS_MOVIES ]->(rec)
WITH m, rec, gs, COUNT(cn) AS CN, PC
with rec, (gs)+(3*PC)+(3*CN) AS score
ORDER BY score DESC
match(a)-[:ACTED_IN]->(rec:Movie)<-[:DIRECTED]-(d)
with rec, collect(distinct a.name) as actors, collect(distinct d.name) as directors
WITH REDUCE(mergedString = "",word IN actors | mergedString+word+',') as actorsString, REDUCE(mergedString = "",word IN directors | mergedString+word+',') as directorsString, rec
RETURN toString(ID(rec)) as id, rec.imdb_id as imdb_id, rec.title as Title, LEFT(actorsString,SIZE(actorsString)-1) as Actors, LEFT(directorsString,SIZE(directorsString)-1) as Directors, rec.overview as overview, rec.Poster as Poster limit 6'''
#print(mask)
movie_frame = pd.DataFrame(driver.session().run(query))
print(
'8888888888888888888888888888888888888888888888888888888888888888888888888888888888'
)
print(len(movie_frame))
#movie_frame = pd.DataFrame(driver.session().run(query))
if (len(movie_frame) == 0):
return ('Empty')
movie_frame.columns = [
'id', 'imdb_id', 'Title', 'Actors', 'Directors', 'overview', 'Poster'
]
movie_list = list(movie_frame.head(6).apply(set_movie, axis=1))
return movie_list
def detail(request, key):
query = '''match(m:Movie) where m.imdb_id = "''' + str(key) + '''"
match(a)-[:ACTED_IN]->(m:Movie)<-[:DIRECTED]-(d) with m, collect(distinct a.name) as actors, collect(distinct d.name) as directors
WITH REDUCE(mergedString = "",word IN actors | mergedString+word+',') as actorsString, REDUCE(mergedString = "",word IN directors | mergedString+word+',') as directorsString, m
RETURN toString(ID(m)) as id, m.imdb_id as imdb_id, m.title as Title, LEFT(actorsString,SIZE(actorsString)-1) as Actors, LEFT(directorsString,SIZE(directorsString)-1) as Directors, m.overview as overview, m.Poster as Poster'''
movie_frame = pd.DataFrame(driver.session().run(query))
movie_frame.columns = [
'id', 'imdb_id', 'Title', 'Actors', 'Directors', 'overview', 'Poster'
]
print(
"#########################################################################"
)
print(len(movie_frame))
print(movie_frame)
movie_frame.index = [0]
context = {
'movie': set_movie(movie_frame.loc[0]),
'algo_one': recommendation_algo_one(key),
'algo_two': recommendation_algo_two(key),
'algo_three': recommendation_algo_three(key),
'search_key': "",
'search_type': 'All',
'radios': 'All Title Actors Directors'.split()
}
context['PAGE_NAME'] = context['movie'].title
if context['algo_one'] == 'Empty' and context[
'algo_two'] != 'Empty' and context['algo_three'] != 'Empty':
return render(request, 'neo4j/genreerror.html', context)
if context['algo_two'] == 'Empty' and context[
'algo_one'] != 'Empty' and context['algo_three'] != 'Empty':
return render(request, 'neo4j/nlperror.html', context)
if context['algo_three'] == 'Empty' and context[
'algo_one'] != 'Empty' and context['algo_two'] != 'Empty':
return render(request, 'neo4j/itemerror.html', context)
if context['algo_one'] == 'Empty' and context[
'algo_two'] == 'Empty' and context['algo_three'] != 'Empty':
return render(request, 'neo4j/genrenlperror.html', context)
if context['algo_one'] == 'Empty' and context[
'algo_two'] != 'Empty' and context['algo_three'] == 'Empty':
return render(request, 'neo4j/genreitemerror.html', context)
if context['algo_one'] != 'Empty' and context[
'algo_two'] == 'Empty' and context['algo_three'] == 'Empty':
return render(request, 'neo4j/nlpitemerror.html', context)
if context['algo_one'] == 'Empty' and context[
'algo_two'] == 'Empty' and context['algo_three'] == 'Empty':
return render(request, 'neo4j/all.html', context)
return render(request, 'neo4j/detail.html', context)
def search_products(search_type, search_key):
if search_type == 'All':
query = '''match(p:Product)<-[:HasProduct]-(c)
where toLower(p.ProductName) contains toLower("''' + str(
search_key
) + '''") or toLower(p.Manufacturer) contains toLower("''' + str(
search_key
) + '''") or toLower(p.Style) contains toLower("''' + str(
search_key
) + '''") or toLower(p.Finish) contains toLower("''' + str(
search_key
) + '''") or toLower(c.Category) contains toLower("''' + str(
search_key) + '''")
RETURN toString(p.ProductId) as id, p.ProductName as ProductName, p.Manufacturer as Manufacturer, p.Style as Style, p.Finish as Finish, c.Category as Category, p.Price as Price, p.poster as Poster limit 12'''
elif search_type == 'Product':
query = '''match(p:Product)<-[:HasProduct]-(c)
where toLower(p.ProductName) contains toLower("''' + str(
search_key) + '''")
RETURN toString(p.ProductId) as id, p.ProductName as ProductName, p.Manufacturer as Manufacturer, p.Style as Style, p.Finish as Finish, c.Category as Category, p.Price as Price, p.poster as Poster limit 12'''
elif search_type == 'Manufacturer':
query = '''match(p:Product)<-[:HasProduct]-(c)
where toLower(p.Manufacturer) contains toLower("''' + str(
search_key) + '''")
RETURN toString(p.ProductId) as id, p.ProductName as ProductName, p.Manufacturer as Manufacturer, p.Style as Style, p.Finish as Finish, c.Category as Category, p.Price as Price, p.poster as Poster limit 12'''
elif search_type == 'Category':
query = '''match(p:Product)<-[:HasProduct]-(c)
where toLower(c.Category) contains toLower("''' + str(
search_key) + '''")
RETURN toString(p.ProductId) as id, p.ProductName as ProductName, p.Manufacturer as Manufacturer, p.Style as Style, p.Finish as Finish, c.Category as Category, p.Price as Price, p.poster as Poster limit 12'''
try:
product_frame = pd.DataFrame(driver.session().run(query))
print('%%%%%%%')
product_frame.columns = [
'ProductId', 'ProductName', 'Manufacturer', 'Style', 'Finish',
'Category', 'Price', 'Poster'
]
print(product_frame)
product_list = list(product_frame.apply(set_product, axis=1))
return product_list
except:
return []
def home(request):
query = '''match(h:CategoryRollUp)
with collect(h) as v
unwind v as c
match(c)-[:HasCategory]->(d)-[:HasProduct]->(p)
with c,collect(p) as k
with c,k
unwind k[..1] as l
return c.CategoryRollUp,l.poster'''
categoryrollup_frame = pd.DataFrame(driver.session().run(query))
categoryrollup_frame.columns = ['CategoryRollUp', 'Poster']
categoryrollup_list = list(
categoryrollup_frame.head(10).apply(set_categoryrollup, axis=1))
#print(categoryrollup_frame)
title = "Top 5 products"
search_type, search_key = 'All', ''
if request.method == 'POST':
search_type = request.POST['search_type']
search_key = request.POST['search_key']
if len(search_key) > 0:
title = 'Search results for {} in category {}'.format(
search_key, search_type)
product_list = search_products(search_type, search_key)
print('######################################################')
print(product_list)
context = {
'products': product_list,
'title': title,
'search_key': search_key,
'search_type': search_type,
'radios': 'All Product Manufacturer Category'.split()
}
return render(request, 'neo4j/search.html', context)
#print("Showing {} results".format(len(movie_list)))
context = {
'categoryrollup': categoryrollup_list,
'title': title,
'search_key': search_key,
'search_type': search_type,
'radios': 'All Product Manufacturer Category'.split()
}
#print(context)
return render(request, 'neo4j/index.html', context)
def recommendation_algo_one(key):
query = '''MATCH (p:Product)
where p.ProductId ="''' + str(key) + '''"
WITH p
match(p1:Product{SubCategory:p.SubCategory})<-[:HasProduct]-(c:Category) where p<>p1
with p,p1,c,apoc.text.sorensenDiceSimilarity(p.ProductName, p1.ProductName) as sim order by sim desc
RETURN toString(p1.ProductId) as id, p1.ProductName as ProductName, p1.Manufacturer as Manufacturer, p1.Style as Style, p1.Finish as Finish, c.Category as Category,p1.Price as Price, p1.poster as Poster limit 6'''
product_frame = pd.DataFrame(driver.session().run(query))
if (len(product_frame) == 0):
return ('Empty')
product_frame.columns = [
'ProductId', 'ProductName', 'Manufacturer', 'Style', 'Finish',
'Category', 'Price', 'Poster'
]
product_list = list(product_frame.apply(set_product, axis=1))
return product_list
def recommendation_algo_three(key):
query = '''MATCH (p:Product)
where p.ProductId ="''' + str(key) + '''"
WITH p
match(u)-[x:InteractsWith]->(p) where x.EventScore >= 0.25
with collect (u) as s
unwind s as d
match (d)-[x:InteractsWith]->(p1)<-[:HasProduct]-(c) where x.EventScore >.5
with c, collect (distinct(p1)) as d
unwind d[..6] as p1
return toString(p1.ProductId) as id, p1.ProductName as ProductName, p1.Manufacturer as Manufacturer, p1.Style as Style, p1.Finish as Finish, c.Category as Category,p1.Price as Price, p1.poster as Poster limit 6'''
product_frame = pd.DataFrame(driver.session().run(query))
if (len(product_frame) == 0):
return ('Empty')
product_frame.columns = [
'ProductId', 'ProductName', 'Manufacturer', 'Style', 'Finish',
'Category', 'Price', 'Poster'
]
product_list = list(product_frame.apply(set_product, axis=1))
return product_list
def recommendation_algo_two(key):
query = ''' MATCH (p1:Product)
where p1.ProductId ="''' + str(key) + '''"
MATCH (c:Customer)-[x:InteractsWith]->(p1)
where x.Event = 'PURCHASE'
with collect(c.CustomerId) as ws, p1
unwind ws as ws1
MATCH (c1:Customer{CustomerId: ws1})-[x:InteractsWith]->(p:Product)
where x.Event = 'PURCHASE'
with collect(distinct p) as wsa, p1
MATCH (c2)-[x:InteractsWith]->(p:Product{ProductId: p1.ProductId})
where x.Event = 'PRODUCT VIEW' or x.Event = 'CART ITEM ADDITION' or x.Event = 'WISHLIST'
with collect(distinct id(c2)) as targetviews, wsa, p1
unwind wsa as w2
MATCH (c2)-[x:InteractsWith]->(p:Product{ProductId: w2.ProductId})<-[:HasProduct]-(cat)
where x.Event = 'PRODUCT VIEW' or x.Event = 'CART ITEM ADDITION' or x.Event = 'WISHLIST'
with collect(distinct id(c2)) as listitemviews, w2, targetviews, p1, cat
with p1 , w2, cat, gds.alpha.similarity.jaccard(targetviews, listitemviews) AS similarity
where similarity<1 and similarity>=0.01
with p1,w2, similarity, cat
ORDER BY similarity DESC
with p1, collect([w2,similarity, cat])[..6] as products
unwind products as recommendation
return toString(recommendation[0].ProductId) as ProductId, recommendation[0].ProductName as ProductName, recommendation[0].Manufacturer as Manufacturer, recommendation[0].Style as Style, recommendation[0].Finish as Finish,recommendation[2].Category as Category, recommendation[0].Price as Price, recommendation[0].poster as Poster'''
product_frame = pd.DataFrame(driver.session().run(query))
print(product_frame)
if (len(product_frame) == 0):
return ('Empty')
product_frame.columns = [
'ProductId', 'ProductName', 'Manufacturer', 'Style', 'Finish',
'Category', 'Price', 'Poster'
]
product_list = list(product_frame.apply(set_product, axis=1))
return product_list
def search_movies(search_type, search_key):
'''print("Got params", search_type, search_key)
cols = 'Title Actors Directors overview'.split()
if search_type != 'All':
mask = movie_frame[search_type].apply(lambda x: compare(search_key, x))
else:
mask = False
for search_type in cols:
mask = mask | np.array(movie_frame[search_type].apply(lambda x: compare(search_key, x)))
movie_list = movie_frame[mask].apply(set_movie, axis=1)'''
if search_type == 'All':
query = '''match(a)-[:ACTED_IN]->(m:Movie)<-[:DIRECTED]-(d)
where m.title contains "''' + str(
search_key) + '''" or m.overview contains "''' + str(
search_key) + '''" or a.name contains "''' + str(
search_key
) + '''" or d.name contains "''' + str(search_key) + '''"
or toLower(m.title) contains toLower("''' + str(
search_key
) + '''") or toLower(m.overview) contains toLower("''' + str(
search_key
) + '''") or toLower(a.name) contains toLower("''' + str(
search_key
) + '''") or toLower(d.name) contains toLower("''' + str(
search_key) + '''")
with m, collect(distinct a.name) as actors, collect(distinct d.name) as directors
WITH REDUCE(mergedString = "",word IN actors | mergedString+word+',') as actorsString, REDUCE(mergedString = "",word IN directors | mergedString+word+',') as directorsString, m
RETURN toString(ID(m)) as id, m.imdb_id as imdb_id, m.title as Title, LEFT(actorsString,SIZE(actorsString)-1) as Actors, LEFT(directorsString,SIZE(directorsString)-1) as Directors, m.overview as overview, m.Poster as Poster limit 6'''
elif search_type == 'Title':
query = '''match(a)-[:ACTED_IN]->(m:Movie)<-[:DIRECTED]-(d)
where m.title contains "''' + str(
search_key) + '''" or toLower(m.title) contains toLower("''' + str(
search_key) + '''")
with m, collect(distinct a.name) as actors, collect(distinct d.name) as directors
WITH REDUCE(mergedString = "",word IN actors | mergedString+word+',') as actorsString, REDUCE(mergedString = "",word IN directors | mergedString+word+',') as directorsString, m
RETURN toString(ID(m)) as id, m.imdb_id as imdb_id, m.title as Title, LEFT(actorsString,SIZE(actorsString)-1) as Actors, LEFT(directorsString,SIZE(directorsString)-1) as Directors, m.overview as overview, m.Poster as Poster limit 6'''
elif search_type == 'Actors':
query = '''match(a)-[:ACTED_IN]->(m:Movie)<-[:DIRECTED]-(d)
where a.name contains "''' + str(
search_key) + '''" or toLower(a.name) contains toLower("''' + str(
search_key) + '''")
with m, collect(distinct a.name) as actors, collect(distinct d.name) as directors
WITH REDUCE(mergedString = "",word IN actors | mergedString+word+',') as actorsString, REDUCE(mergedString = "",word IN directors | mergedString+word+',') as directorsString, m
RETURN toString(ID(m)) as id, m.imdb_id as imdb_id, m.title as Title, LEFT(actorsString,SIZE(actorsString)-1) as Actors, LEFT(directorsString,SIZE(directorsString)-1) as Directors, m.overview as overview, m.Poster as Poster limit 6'''
elif search_type == 'Directors':
query = '''match(a)-[:ACTED_IN]->(m:Movie)<-[:DIRECTED]-(d)
where d.name contains "''' + str(
search_key) + '''" or toLower(d.name) contains toLower("''' + str(
search_key) + '''")
with m, collect(distinct a.name) as actors, collect(distinct d.name) as directors
WITH REDUCE(mergedString = "",word IN actors | mergedString+word+',') as actorsString, REDUCE(mergedString = "",word IN directors | mergedString+word+',') as directorsString, m
RETURN toString(ID(m)) as id, m.imdb_id as imdb_id, m.title as Title, LEFT(actorsString,SIZE(actorsString)-1) as Actors, LEFT(directorsString,SIZE(directorsString)-1) as Directors, m.overview as overview, m.Poster as Poster limit 6'''
try:
movie_frame = pd.DataFrame(driver.session().run(query))
movie_frame.columns = [
'id', 'imdb_id', 'Title', 'Actors', 'Directors', 'overview',
'Poster'
]
movie_list = list(movie_frame.head(6).apply(set_movie, axis=1))
print(movie_list)
#print(mask)
return movie_list
#[:6] if len(movie_list) > 6 else movie_list
except:
return []