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 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)
columns=('col %d' % i for i in range(20)))
st.dataframe(dataframe.style.highlight_max(axis=0))
dataframe1 = pd.DataFrame(np.random.randn(10, 20),
columns=('col %d' % i for i in range(20)))
st.table(dataframe1)
elif pag_selec == 'Graph':
st.title('Testes com Grafos')
nodes = []
edges = []
nodes.append(
Node(
id="Spiderman",
label="Peter Parker",
size=400,
svg=
"http://marvel-force-chart.surge.sh/marvel_force_chart_img/top_spiderman.png"
)) # includes **kwargs
nodes.append(
Node(
id="Captain_Marvel",
size=400,
svg=
"http://marvel-force-chart.surge.sh/marvel_force_chart_img/top_captainmarvel.png"
))
nodes.append(
Node(
id="Thor",
size=400,
svg=
with st.spinner("Train decision tree classifier"):
clf = tree.DecisionTreeClassifier()
clf = clf.fit(iris.data, iris.target)
dot_data = tree.export_graphviz(clf,
out_file=None,
feature_names=iris.feature_names)
G = pgv.AGraph().from_string(dot_data)
nodes = []
edges = []
for node in G.nodes():
nodes.append(Node(id=node,
label=node.attr['label'].split('\\n')[0],
symbolType='square'))
for edge in G.edges():
edges.append(
Edge(source=edge[0],
target=edge[1],
type="STRAIGHT")
)
layout = st.sidebar.selectbox('layout',['dot',
'neato',
'circo',
'fdp',
'sfdp'])
rankdir = st.sidebar.selectbox("rankdir", ['BT', 'TB', 'LR', 'RL'])
# First create a graph using the Barabasi-Albert model
n = 2000
m = 2
G = nx.generators.barabasi_albert_graph(n, m)
# Then find the node with the largest degree;
# This node's egonet will be the focus of this example.
node_and_degree = G.degree()
most_connected_node = sorted(G.degree, key=lambda x: x[1], reverse=True)[0]
degree = G.degree(most_connected_node)
# Create egonet for the focal node
hub_ego = nx.ego_graph(G, most_connected_node)
# Now create the equivalent Node and Edge lists
nodes = [Node(id=i, label=str(i), size=200) for i in hub_ego.nodes]
edges = [
Edge(source=i, target=j, type="CURVE_SMOOTH") for (i, j) in G.edges
if i in hub_ego.nodes and j in hub_ego.nodes
]
config = Config(
width=500,
height=500,
directed=True,
nodeHighlightBehavior=False,
highlightColor="#F7A7A6", # or "blue"
collapsible=False,
node={'labelProperty': 'label'},
# **kwargs e.g. node_size=1000 or node_color="blue"
)
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))
# karate_club_graph.py
# An example of basic and common interoperability between
# streamlit-agraph and networkx.
#
# Use the following command to launch the app
# streamlit run <path-to-script>.py
import networkx as nx
from streamlit_agraph import agraph, Node, Edge, Config
# Generate the networkx implementation of Zachary's Karate Club graph
# (https://en.wikipedia.org/wiki/Zachary%27s_karate_club)
G = nx.karate_club_graph()
# Create the equivalent Node and Edge lists
nodes = [Node(id=i, label=str(i), size=200) for i in range(len(G.nodes))]
edges = [Edge(source=i, target=j, type="CURVE_SMOOTH") for (i, j) in G.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)
subgraph = nx.subgraph(G, subset)
node_and_degree = subgraph.degree()
nx.set_node_attributes(subgraph,
values=dict(subgraph.degree),
name='degree')
if ego_:
most_connected_node = sorted(subgraph.degree,
key=lambda x: x[1],
reverse=True)[0][0]
subgraph = nx.ego_graph(subgraph, most_connected_node)
# Now create the equivalent Node and Edge lists
nodes = [
Node(id=n[0], label=str(n[1]['label']), size=(2 + n[1]['degree'])**3)
for n in subgraph.nodes(data=True)
]
edges = [
Edge(source=e[0],
target=e[1],
labelProperty="",
label=e[2]['relation'],
type=edge_style) for e in subgraph.edges(data=True)
if e[0] in subgraph.nodes and e[1] in subgraph.nodes
]
config = Config(
width=600,
height=600,
#graphviz_layout=layout,
f = open('patientInfection.json')
print('check')
trp_data = f.read()
print(type(trp_data))
temp_data = json.loads(trp_data)
temp_data = temp_data[0]
print(type(temp_data))
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)