def app():
nodes = []
edges = []
nodes.append(Node(id="Spiderman", label="Spid", size=400))
nodes.append(Node(id="Captain_Marvel", label="marvel", size=400))
edges.append(
Edge(source="Captain_Marvel", target="Spiderman", label="friend_of"))
# nodes = [Node(id=n['id'], label=n['name']) for n in json_nodes] # add value to payload
# edges = [Edge(source=e['sourceId'], target=e['targetId']) for e in json_edges]
config = Config(
width=1600,
height=1000,
directed=True,
nodeHighlightBehavior=True,
highlightColor="#F7A7A6",
collapsible=True,
node={'labelProperty': 'label'}, # config labelProperty
link={
'labelProperty': 'label',
'renderLabel': True
})
return_value = agraph(nodes=nodes, edges=edges, config=config)
def app():
footer()
st.title("Graph Example")
st.sidebar.title("Welcome")
filename = file_selector()
st.write('You selected `%s`' % filename)
# sparql_endpoint = st.sidebar.text_input("SPARQL ENDPOINT: ", "http://dbpedia.org/sparql")
query_type = st.sidebar.selectbox("Quey Tpye: ", ["Person", "Inspirationals"], key="second") #rdfs:Resource , "Company", "Location"
# resource_name = st.sidebar.text_input("Quey Tpye: ", "Barack_Obama" )
config = Config(height=500, width=700, nodeHighlightBehavior=True, highlightColor="#F7A7A6", directed=True,
collapsible=True)
if query_type == "Person":
st.subheader("Person (Date of birth and place)")
persons = list(get_people())
st.write(len(persons))
chosen_person = st.sidebar.selectbox("Choose a Person: ", persons)
nodes, edges = do_query(chosen_person)
agraph(nodes, edges, config)
if query_type=="FOAF":
st.subheader("Inspirationals")
with st.spinner("Loading data"):
store = get_inspired()
st.write(len(store.getNodes()))
st.success("Done")
agraph(list(store.getNodes()), (store.getEdges() ), config)
def app():
st.subheader("Person (Date of birth and place)")
persons = list(get_people())
st.write(len(persons))
chosen_person = st.sidebar.selectbox("Choose a Person: ", persons)
config = Config(height=500, width=700, nodeHighlightBehavior=True,
highlightColor="#F7A7A6", directed=True, collapsible=True)
if chosen_person != "":
store, abstract = get_people(chosen_person)
def plot_graph2(graph):
nodes = [Node(id=i, label=str(i), size=200) for i in range(len(graph.nodes))]
edges = [Edge(source=i, target=j, type="CURVE_SMOOTH") for (i, j) in graph.edges]
config = Config(width=500,
height=500,
directed=True,
nodeHighlightBehavior=True,
highlightColor="#F7A7A6",
collapsible=True,
node={'labelProperty': 'label'},
link={'labelProperty': 'label', 'renderLabel': True}
)
return_value = agraph(nodes=nodes,
edges=edges,
config=config)
def app():
persState = PersService.state()
st.subheader("Person (Date of birth and place)")
st.markdown("---")
if not persState.loaded:
st.write("Please load data in the Data Management Page first.")
node_names = []
else:
store = persState.store
algos = persState.algos
node_names = persState.node_names
persons = list(PersService.get_people())
st.write(len(persons))
chosen_person = st.sidebar.selectbox("Choose a Person: ", persons)
config = Config(height=500, width=700, nodeHighlightBehavior=True,
highlightColor="#F7A7A6", directed=True, collapsible=True)
if chosen_person != "":
store, abstract = PersService.load_persons(chosen_person)
def app():
footer()
st.title("Graph Example")
st.sidebar.title("Welcome")
query_type = st.sidebar.selectbox(
"Query Tpye: ", ["Inspirationals", "Marvel"]
) # could add more stuff here later on or add other endpoints in the sidebar.
config = Config(height=600,
width=700,
nodeHighlightBehavior=True,
highlightColor="#F7A7A6",
directed=True,
collapsible=True)
if query_type == "Inspirationals":
st.subheader("Inspirationals")
with st.spinner("Loading data"):
store = get_inspired()
st.write("Nodes loaded: " + str(len(store.getNodes())))
st.success("Done")
agraph(list(store.getNodes()), (store.getEdges()), config)
if query_type == "Marvel":
#based on http://marvel-force-chart.surge.sh/
with open("./marvel.json", encoding="utf8") as f:
marvel_file = json.loads(f.read())
marvel_store = TripleStore()
for sub_graph in marvel_file["children"]:
marvel_store.add_triple(marvel_file["name"],
"has_subgroup",
sub_graph["name"],
picture=marvel_file["img"])
for node in sub_graph["children"]:
node1 = node["hero"]
link = "blongs_to"
node2 = sub_graph["name"]
pic = node["img"]
marvel_store.add_triple(node1, link, node2, picture=pic)
agraph(list(marvel_store.getNodes()), (marvel_store.getEdges()),
config)
def app():
df = load_data(path)
st.title("Open Knowledge Graph Visualization")
heads = st.multiselect('Choose heads', df["head"].unique())
threshold = st.slider('Set confidence threshold',
0.,
df["confidence"].max(),
value=0.0000001)
store = subset_data(df, heads, threshold)
config = Config(height=600,
width=700,
nodeHighlightBehavior=True,
highlightColor="#F7A7A6",
directed=True,
collapsible=True,
link={"renderLabel": True})
st.write("Nodes loaded: " + str(len(store.getNodes())))
agraph(list(store.getNodes()), (store.getEdges()), config)
graph.replace_filters(triplet_filters=triplet_filters,
group_filters=group_filters)
return graph
# Debug check to see if filters are added correctly
if st.button("Print input to terminal"):
print(filters.values())
print(group_filters.values())
# Construct the knowledge graph (time consuming part)
graph = create_graph_with_filters(list(filters.values()),
list(group_filters.values()))
# Get the nodes
nodes = set([triplet.head for triplet in graph.filtered_triplets])
# Choose nodes to plot
st.write("N. nodes loaded: " + str(len(nodes)))
heads = st.multiselect("Choose nodes to plot", list(nodes))
# convert selected nodes to streamlit graph store
store = beliefgraph_to_triplestore(graph, heads)
config = Config(height=600,
width=700,
nodeHighlightBehavior=True,
highlightColor="#F7A7A6",
directed=True,
collapsible=True,
link={"renderLabel": True})
agraph(list(store.getNodes()), (store.getEdges()), config)
edges.append(
Edge(source="Captain_Marvel",
label="Amiga de ",
target="Spiderman",
type="CURVE_SMOOTH")) # includes **kwargs
edges.append(
Edge(source="Captain_Marvel",
label="Conhece o",
target="Thor",
type="CURVE_SMOOTH")) # includes **kwargs
edges.append(Edge(source="Thor", label="Inimigo de",
target="Thanos")) # includes **kwargs
config = Config(
width=500,
height=500,
directed=True,
nodeHighlightBehavior=True,
highlightColor="#F7A7A6", # or "blue"
collapsible=True,
node={'labelProperty': 'label'},
link={
'labelProperty': 'label',
'renderLabel': True
}
# **kwargs e.g. node_size=1000 or node_color="blue"
)
return_value = agraph(nodes=nodes, edges=edges, config=config)
# session_state.checkboxed = True
=======
if not inspState.loaded:
st.write("Please load data in the Data Management Page first.")
node_names = []
>>>>>>> 6f6a9624da414e66674de21213f6c8f387db8519
else:
store = inspState.store
algos = inspState.algos
node_names = inspState.node_names
if len(node_names) > 0:
chosen_person_a = st.sidebar.selectbox("Choose Person A: ", node_names, key="p1")
chosen_person_b = st.sidebar.selectbox("Choose Person B: ", node_names, key="p2")
algo_type = st.sidebar.selectbox("Algo ", ["", "Shortest Path", "Community", "PageRank"], key="second")
<<<<<<< HEAD
config = Config(height=500, width=700, nodeHighlightBehavior=True,
highlightColor="#F7A7A6", directed=True, collapsible=True)
=======
config = Config(height=500,
width=700,
nodeHighlightBehavior=True,
highlightColor="#F7A7A6",
directed=True,
collapsible=True,
link={'labelProperty': 'label', 'renderLabel': True}
)
>>>>>>> 6f6a9624da414e66674de21213f6c8f387db8519
if algo_type == "Shortest Path":
if chosen_person_a != "" and chosen_person_b != "" and chosen_person_a != chosen_person_b:
analysis_results = algos.shortest_path(chosen_person_a, chosen_person_b)
sp_store = TripleStore()
if len(analysis_results) > 0:
'circo',
'fdp',
'sfdp'])
rankdir = st.sidebar.selectbox("rankdir", ['BT', 'TB', 'LR', 'RL'])
ranksep = st.sidebar.slider("ranksep",min_value=0, max_value=10)
nodesep = st.sidebar.slider("nodesep",min_value=0, max_value=10)
config = Config(width=2000,
height=1000,
graphviz_layout=layout,
graphviz_config={"rankdir": rankdir, "ranksep": ranksep, "nodesep": nodesep},
directed=True,
nodeHighlightBehavior=True,
highlightColor="#F7A7A6",
collapsible=True,
node={'labelProperty':'label'},
link={'labelProperty': 'label', 'renderLabel': True},
maxZoom=2,
minZoom=0.1,
staticGraphWithDragAndDrop=False,
staticGraph=False,
initialZoom=1
)
return_value = agraph(nodes=nodes,
edges=edges,
config=config)
def main():
"""Semi Automated ML App with Streamlit """
activities = ["Matrics Monitored", "Data Visualization"]
choice = st.sidebar.selectbox("Select Activities", activities)
if choice == 'Matrics Monitored':
st.title("Social Media Computing Assignment")
df = pd.read_csv('audience_size.csv')
# df
fig = px.bar(df,
x="company",
y="count",
color="type",
title="Audience Size")
st.plotly_chart(fig)
if choice == 'Data Visualization':
activities = [
"Audience Country Spread", "Campaign Categories",
"Favourite And Retweet Count", "Frequency Tweets", "Word Map",
"Network Graph"
]
choice = st.sidebar.selectbox("Select Activities", activities)
if choice == 'Audience Country Spread':
st.title("Audience Country Spread")
brand = ["Omega", "Rolex", "Daniel Wellington", "SwatchUS"]
brand_choice = st.sidebar.selectbox("Select Brand", brand)
if brand_choice == 'Omega':
omega_loc = pd.read_csv('omega_loc.csv')
omega_country = px.bar(omega_loc,
x='Followers(%)',
y='Country',
title='Omega - Audience country spread',
text='Followers(%)',
orientation='h')
st.plotly_chart(omega_country)
if brand_choice == 'Rolex':
rolex_loc = pd.read_csv('rolex_loc.csv')
rolex_country = px.bar(rolex_loc,
x='Followers(%)',
y='Country',
title='Rolex - Audience country spread',
text='Followers(%)',
orientation='h')
st.plotly_chart(rolex_country)
if brand_choice == 'Daniel Wellington':
DanielWellington_loc = pd.read_csv('itisdw_loc.csv')
DanielWellington_country = px.bar(
DanielWellington_loc,
x='Followers(%)',
y='Country',
title='Daniel Wellington - Audience country spread',
text='Followers(%)',
orientation='h')
st.plotly_chart(DanielWellington_country)
if brand_choice == 'SwatchUS':
swatch_loc = pd.read_csv('swatch_loc.csv')
swatch_country = px.bar(
swatch_loc,
x='Followers(%)',
y='Country',
title='SwatchUS - Audience country spread',
text='Followers(%)',
orientation='h')
st.plotly_chart(swatch_country)
if choice == 'Campaign Categories':
st.title("Campaign Categories")
brand = ["Omega", "Rolex", "Daniel Wellington", "SwatchUS"]
brand_choice = st.sidebar.selectbox("Select Brand", brand)
if brand_choice == 'Omega':
company = "omega"
camp_eng = pd.read_csv('campaign_engagement.csv')
camp_eng = camp_eng.query('company == @company')
fig = px.bar(camp_eng,
x="campaign",
y="avg",
title="Average Campaign Engagement")
st.plotly_chart(fig)
fig = px.bar(camp_eng,
x="campaign",
y="count",
title="Total Tweeted Campaign")
st.plotly_chart(fig)
fig = px.bar(camp_eng,
x="campaign",
y="rate",
title="Total Campaign Engagement Rate")
st.plotly_chart(fig)
if brand_choice == 'Rolex':
company = "rolex"
camp_eng = pd.read_csv('campaign_engagement.csv')
camp_eng = camp_eng.query('company == @company')
fig = px.bar(camp_eng,
x="campaign",
y="avg",
title="Average Campaign Engagement")
st.plotly_chart(fig)
fig = px.bar(camp_eng,
x="campaign",
y="count",
title="Total Tweeted Campaign")
st.plotly_chart(fig)
fig = px.bar(camp_eng,
x="campaign",
y="rate",
title="Total Campaign Engagement Rate")
st.plotly_chart(fig)
if brand_choice == 'Daniel Wellington':
company = "itisdw"
camp_eng = pd.read_csv('campaign_engagement.csv')
camp_eng = camp_eng.query('company == @company')
fig = px.bar(camp_eng,
x="campaign",
y="avg",
title="Average Campaign Engagement")
st.plotly_chart(fig)
fig = px.bar(camp_eng,
x="campaign",
y="count",
title="Total Tweeted Campaign")
st.plotly_chart(fig)
fig = px.bar(camp_eng,
x="campaign",
y="rate",
title="Total Campaign Engagement Rate")
st.plotly_chart(fig)
if brand_choice == 'SwatchUS':
company = "swatch"
camp_eng = pd.read_csv('campaign_engagement.csv')
camp_eng = camp_eng.query('company == @company')
fig = px.bar(camp_eng,
x="campaign",
y="avg",
title="Average Campaign Engagement")
st.plotly_chart(fig)
fig = px.bar(camp_eng,
x="campaign",
y="count",
title="Total Tweeted Campaign")
st.plotly_chart(fig)
fig = px.bar(camp_eng,
x="campaign",
y="rate",
title="Total Campaign Engagement Rate")
st.plotly_chart(fig)
if choice == 'Favourite And Retweet Count':
st.title("Favourite Count")
omega_camp = pd.read_csv('omega_camp.csv')
omega_camp = px.bar(omega_camp,
x='campaign',
y='likes_sum',
title='Omega Favourite Count')
st.plotly_chart(omega_camp)
rolex_camp = pd.read_csv('rolex_camp.csv')
rolex_camp = px.bar(rolex_camp,
x='campaign',
y='likes_sum',
title='Rolex Favourite Count')
st.plotly_chart(rolex_camp)
itisdw_camp = pd.read_csv('itisdw_camp.csv')
itisdw_camp = px.bar(itisdw_camp,
x='campaign',
y='likes_sum',
title='Daniel Wellington Favourite Count')
st.plotly_chart(itisdw_camp)
swatch_camp = pd.read_csv('swatch_camp.csv')
swatch_camp = px.bar(swatch_camp,
x='campaign',
y='likes_sum',
title='SwatchUS Favourite Count')
st.plotly_chart(swatch_camp)
st.title("Retweet Count")
omega_camp = pd.read_csv('omega_camp.csv')
omega_camp = px.bar(omega_camp,
x='campaign',
y='retweet_sum',
title='Omega Retweet Count')
st.plotly_chart(omega_camp)
rolex_camp = pd.read_csv('rolex_camp.csv')
rolex_camp = px.bar(rolex_camp,
x='campaign',
y='retweet_sum',
title='Rolex Retweet Count')
st.plotly_chart(rolex_camp)
itisdw_camp = pd.read_csv('itisdw_camp.csv')
itisdw_camp = px.bar(itisdw_camp,
x='campaign',
y='retweet_sum',
title='Daniel Wellington Retweet Count')
st.plotly_chart(itisdw_camp)
swatch_camp = pd.read_csv('swatch_camp.csv')
swatch_camp = px.bar(swatch_camp,
x='campaign',
y='retweet_sum',
title='SwatchUS Retweet Count')
st.plotly_chart(swatch_camp)
if choice == 'Frequency Tweets':
st.title("Frequency Tweet")
brand = ["All", "Omega", "Rolex", "Daniel Wellington", "SwatchUS"]
brand_choice = st.sidebar.selectbox("Select Brand", brand)
if brand_choice == 'Omega':
company = "omega"
tweet_freq = pd.read_csv('tweet_freq.csv')
tweet_freq = tweet_freq.query('company == @company')
fig = px.line(tweet_freq,
x="week",
y="count",
title="Tweet Frequency")
st.plotly_chart(fig)
company = "omega"
eng_freq = pd.read_csv('engagement_rate.csv')
eng_freq = eng_freq.query('company == @company')
fig = px.line(eng_freq,
x="week",
y="count",
title="Engagement rate")
st.plotly_chart(fig)
if brand_choice == 'Rolex':
company = "rolex"
tweet_freq = pd.read_csv('tweet_freq.csv')
tweet_freq = tweet_freq.query('company == @company')
fig = px.line(tweet_freq,
x="week",
y="count",
title="Tweet Frequency")
st.plotly_chart(fig)
company = "rolex"
eng_freq = pd.read_csv('engagement_rate.csv')
eng_freq = eng_freq.query('company == @company')
fig = px.line(eng_freq,
x="week",
y="count",
title="Engagement rate")
st.plotly_chart(fig)
if brand_choice == 'Daniel Wellington':
company = "itisdw"
tweet_freq = pd.read_csv('tweet_freq.csv')
tweet_freq = tweet_freq.query('company == @company')
fig = px.line(tweet_freq,
x="week",
y="count",
title="Tweet Frequency")
st.plotly_chart(fig)
company = "itisdw"
eng_freq = pd.read_csv('engagement_rate.csv')
eng_freq = eng_freq.query('company == @company')
fig = px.line(eng_freq,
x="week",
y="count",
title="Engagement rate")
st.plotly_chart(fig)
if brand_choice == 'SwatchUS':
company = "swatch"
tweet_freq = pd.read_csv('tweet_freq.csv')
tweet_freq = tweet_freq.query('company == @company')
fig = px.line(tweet_freq,
x="week",
y="count",
title="Tweet Frequency")
st.plotly_chart(fig)
company = "swatch"
eng_freq = pd.read_csv('engagement_rate.csv')
eng_freq = eng_freq.query('company == @company')
fig = px.line(eng_freq,
x="week",
y="count",
title="Engagement rate")
st.plotly_chart(fig)
if brand_choice == 'All':
tweet_freq = pd.read_csv('tweet_freq.csv')
fig = px.line(tweet_freq,
x="week",
y="count",
color='company',
title="Tweet Frequency")
st.plotly_chart(fig)
eng_freq = pd.read_csv('engagement_rate.csv')
fig = px.line(eng_freq,
x="week",
y="count",
color='company',
title="Engagement rate")
st.plotly_chart(fig)
if choice == 'Word Map':
image_mask = np.array(Image.open("watch.jpg"))
word_cloud = pd.read_csv('word_cloud.csv')
st.title("Word Map")
brand = ["Omega", "Rolex", "Daniel Wellington", "SwatchUS"]
brand_choice = st.sidebar.selectbox("Select Brand", brand)
word = word_cloud.query('(company == @brand_choice)')
camp = word.campaign.to_numpy()
if brand_choice == 'Omega':
camp = [
'all', 'DeVille', 'ValentinesDay',
'OMEGAOfficialTimekeeper'
]
camp_choice = st.sidebar.selectbox("Select Brand", camp)
company = 'omega'
campaign = "all"
word = word_cloud.query(
'(company == @company) & (campaign == @camp_choice)'
)['word'].values[0]
# generate word cloud
wc = WordCloud(background_color="white",
max_words=2000,
mask=image_mask)
wc.generate(word)
# plot the word cloud
plt.figure(figsize=(8, 6), dpi=120)
plt.imshow(wc, interpolation='bilinear')
plt.axis("off")
st.pyplot()
if brand_choice == 'Rolex':
camp = ['all', 'perpetual', 'reloxfamily']
camp_choice = st.sidebar.selectbox("Select Brand", camp)
company = 'rolex'
campaign = "all"
word = word_cloud.query(
'(company == @company) & (campaign == @camp_choice)'
)['word'].values[0]
# generate word cloud
wc = WordCloud(background_color="white",
max_words=2000,
mask=image_mask)
wc.generate(word)
# plot the word cloud
plt.figure(figsize=(8, 6), dpi=120)
plt.imshow(wc, interpolation='bilinear')
plt.axis("off")
st.pyplot()
if brand_choice == 'Daniel Wellington':
camp = ['all', 'dwgiftsoflove', 'danielwellington', 'layzhang']
camp_choice = st.sidebar.selectbox("Select Brand", camp)
company = 'itisdw'
campaign = "all"
word = word_cloud.query(
'(company == @company) & (campaign == @camp_choice)'
)['word'].values[0]
# generate word cloud
wc = WordCloud(background_color="white",
max_words=2000,
mask=image_mask)
wc.generate(word)
# plot the word cloud
plt.figure(figsize=(8, 6), dpi=120)
plt.imshow(wc, interpolation='bilinear')
plt.axis("off")
st.pyplot()
if brand_choice == 'SwatchUS':
camp = [
'all', 'timeiswhatyoumakeofit', 'swatchwithlove',
'swatchmytime'
]
camp_choice = st.sidebar.selectbox("Select Brand", camp)
company = 'swatch'
campaign = "all"
word = word_cloud.query(
'(company == @company) & (campaign == @camp_choice)'
)['word'].values[0]
# generate word cloud
wc = WordCloud(background_color="white",
max_words=2000,
mask=image_mask)
wc.generate(word)
# plot the word cloud
plt.figure(figsize=(8, 6), dpi=120)
plt.imshow(wc, interpolation='bilinear')
plt.axis("off")
st.pyplot()
if choice == 'Network Graph':
st.title("Network Graph")
brand = ["Omega", "Rolex", "Daniel Wellington", "SwatchUS"]
brand_choice = st.sidebar.selectbox("Select Brand", brand)
if brand_choice == 'Omega':
st.title("Graph Example")
config = Config(height=500,
width=700,
nodeHighlightBehavior=True,
highlightColor="#F7A7A6",
directed=True,
collapsible=True)
if query_type == "Inspirationals":
st.subheader("Inspirationals")
with st.spinner("Loading data"):
store = get_inspired()
st.write(len(store.getNodes()))
st.success("Done")
agraph(list(store.getNodes()), (store.getEdges()), config)
if brand_choice == 'Rolex':
company = "rolex"
camp_eng = pd.read_csv('campaign_engagement.csv')
camp_eng = camp_eng.query('company == @company')
fig = px.bar(camp_eng,
x="campaign",
y="avg",
title="Average Campaign Engagement")
st.plotly_chart(fig)
fig = px.bar(camp_eng,
x="campaign",
y="count",
title="Total Tweeted Campaign")
st.plotly_chart(fig)
fig = px.bar(camp_eng,
x="campaign",
y="rate",
title="Total Campaign Engagement Rate")
st.plotly_chart(fig)
if brand_choice == 'Daniel Wellington':
company = "itisdw"
camp_eng = pd.read_csv('campaign_engagement.csv')
camp_eng = camp_eng.query('company == @company')
fig = px.bar(camp_eng,
x="campaign",
y="avg",
title="Average Campaign Engagement")
st.plotly_chart(fig)
fig = px.bar(camp_eng,
x="campaign",
y="count",
title="Total Tweeted Campaign")
st.plotly_chart(fig)
fig = px.bar(camp_eng,
x="campaign",
y="rate",
title="Total Campaign Engagement Rate")
st.plotly_chart(fig)
if brand_choice == 'SwatchUS':
company = "swatch"
camp_eng = pd.read_csv('campaign_engagement.csv')
camp_eng = camp_eng.query('company == @company')
fig = px.bar(camp_eng,
x="campaign",
y="avg",
title="Average Campaign Engagement")
st.plotly_chart(fig)
fig = px.bar(camp_eng,
x="campaign",
y="count",
title="Total Tweeted Campaign")
st.plotly_chart(fig)
fig = px.bar(camp_eng,
x="campaign",
y="rate",
title="Total Campaign Engagement Rate")
st.plotly_chart(fig)
def app():
# Set title and user input elements
st.title("Recipe Recommender Network")
sidebar = st.sidebar
middle, rsidebar = st.beta_columns([3, 1])
sidebar.title("Choose meal type and ingredients")
userIngredients = sidebar.text_input(
"Input ingredients that should be included in returned recipes:")
userInput = sidebar.text_input("Input recipe to use as cluster seed: ")
# create empty lists for holding nodes and edges for network
#net = Network() # place holder for transitioning to pyvis
nodes = []
edges = []
nodeSize = 500
# Bring in the initial data
tmp = pd.read_csv(
r'C:\Users\jdnun\OneDrive\Documents\GT\Spring2021\Project\code\cse6242_project-main\src\cluster_test.csv'
) # Here is where we would access the data from the backend
print('length before filter: ', len(tmp))
# filter data for only recipes that contain the user inputs
if userIngredients != "":
ingredients = userIngredients.split(',')
ingredients = [i.strip() for i in ingredients]
contains = [
tmp["RecipeIngredientParts"].str.contains(i) for i in ingredients
]
#print(ingredients)
tmp = tmp[np.all(contains, axis=0)]
print('length of filtered: ', len(tmp))
if len(tmp) != 0:
edgeList = []
### Used for loading the data directly from the dictionary output from Orion's clustering script
# for i in range(0, len(tmp["c1"]["nodes"])):
# nodes.extend([Node(id=int(tmp["c1"]["nodes"].iloc[i]['RecipeId']), label=tmp["c1"]["nodes"].iloc[i]["Name"],
# size=nodeSize)])
# for j in range(0, len(tmp["c1"]["nodes"])):
# if tmp["c1"]["nodes"].iloc[i]['RecipeId'] != tmp["c1"]["nodes"].iloc[j]['RecipeId'] and int(tmp["c1"]["nodes"].iloc[i]["Ingredient Difference"]) == int(tmp["c1"]["nodes"].iloc[j]["Ingredient Difference"]) \
# and [int(tmp["c1"]["nodes"].iloc[j]['RecipeId']),int(tmp["c1"]["nodes"].iloc[i]['RecipeId'])] not in edgeList:
# edgeList.append([int(tmp["c1"]["nodes"].iloc[i]['RecipeId']),int(tmp["c1"]["nodes"].iloc[j]['RecipeId'])])
# edges.extend([Edge(source=int(tmp["c1"]["nodes"].iloc[i]['RecipeId']), label=int(tmp["c1"]["nodes"].iloc[j]["Ingredient Difference"]),
# target=int(tmp["c1"]["nodes"].iloc[j]["RecipeId"]), type="CURVE_SMOOTH")])
for i in range(0, len(tmp["nodes"])):
nodes.extend([
Node(id=int(tmp.iloc[i]['RecipeId']),
label=tmp.iloc[i]["Name"],
size=nodeSize)
])
#net.add_node(n_id=int(tmp.iloc[i]['RecipeId']), value=nodeSize, label=tmp.iloc[i]["Name"]) # place holder for transitioning to pyvis
for j in range(0, len(tmp["nodes"])):
if tmp.iloc[i]['RecipeId'] != tmp.iloc[j]['RecipeId'] and int(tmp.iloc[i]["Ingredient Difference"]) == int(tmp.iloc[j]["Ingredient Difference"]) \
and [int(tmp.iloc[j]['RecipeId']),int(tmp.iloc[i]['RecipeId'])] not in edgeList:
edgeList.append([
int(tmp.iloc[i]['RecipeId']),
int(tmp.iloc[j]['RecipeId'])
])
edges.extend([
Edge(source=int(tmp.iloc[i]['RecipeId']),
label=int(tmp.iloc[j]["Ingredient Difference"]),
target=int(tmp.iloc[j]["RecipeId"]),
type="CURVE_SMOOTH")
])
#net.add_edge(int(tmp.iloc[i]['RecipeId']),int(tmp.iloc[j]["RecipeId"]),title=int(tmp.iloc[j]["Ingredient Difference"])) # # place holder for transitioning to pyvis
# diffList = [str(i) for i in set(tmp["c1"]["nodes"]["Ingredient Difference"])]
# diffList.append('All')
# diffList.sort(reverse=True)
# display_category = sidebar.selectbox("Ingredient difference: ",index=0, options = diffList) # could add more stuff here later on or add other endpoints in the sidebar.
# if display_category == "all":
viewEdges = edges
# else:
# viewEdges = [edge for edge in edges if edge.label == display_category]
#mealType = sidebar.selectbox("Meal Type: ", index=0, options = ["Breakfast", "Lunch", "Dinner"]) # Just a place holder could be 'soup', 'salad', or 'italian', 'indian', etc
# set network configuration
config = Config(height=500,
width=700,
nodeHighlightBehavior=True,
highlightColor="#F7A7A6",
directed=False,
collapsible=True,
node={'labelProperty': 'label'},
link={
'labelProperty': 'label',
'renderLabel': False
})
# add network to middle column of streamlit canvas
with middle:
#st.text("Displaying {} cuisine types".format(display_category))
return_value = agraph(nodes=nodes, edges=viewEdges, config=config)
#net.show('testgraph.html') # place holder for transitioning to pyvis
st.text("Showing recipes based on the following ingredients: {}".format(
userIngredients))
print(temp_data["@@edgeSet"][0])
nodes = []
edges = []
for val in temp_data["@@edgeSet"]:
nodes.append(Node(id=val['from_id']))
nodes.append(Node(id=val['to_id']))
edges.append(
Edge(source=val['from_id'],
target=val['to_id'],
labelProperty=val['e_type'],
renderLabel=True))
config = Config(height=500,
width=700,
nodeHighlightBehavior=True,
highlightColor="#F7A7A6",
directed=True,
collapsible=True)
agraph(nodes, edges, config)
elif itemSelected == 'Automatic Data Analysis':
st.title('Automatic Data Analysis')
data = st.file_uploader("Upload a Dataset", type=["csv"])
if data is not None:
df = pd.read_csv(data)
st.dataframe(df)
if st.checkbox("Show Shape"):
st.write(df.shape)