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import streamlit as st |
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import json |
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import base64 |
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import requests |
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from io import StringIO |
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from streamlit_agraph import agraph, Node, Edge, Config |
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st.title('Json File Reader') |
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@st.cache_data |
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def get_json(url): |
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js = requests.get(url) |
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data = js.json() |
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return data |
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st.markdown("""Reads the Json file of Comments data extracted from Youtube API & creates graph""") |
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st.sidebar.header('File Upload') |
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your_file = st.sidebar.file_uploader(label="Upload the file here") |
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if your_file is not None: |
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bytes_data = your_file.getvalue() |
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json_data = json.loads(bytes_data) |
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else: |
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st.write("Example api file can be located here") |
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json_data = get_json("https://raw.githubusercontent.com/insightbuilder/python_de_learners_data/main/code_script_notebooks/python_scripts/json_reader/toplevel_comment_zGAkhN1YZXM.json") |
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try: |
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length = len(json_data) |
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if length < 15: |
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indices = st.sidebar.slider("Start n End",0,length,(0,10)) |
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else: |
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indices = st.sidebar.slider("Start n End",0,length,(0,int(length/15))) |
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selected_indices = json_data[indices[0]:indices[1]] |
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nodes = [] |
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edges = [] |
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authors = [] |
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video_id = selected_indices[0]['snippet']['videoId'] |
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nodes.append(Node(id=video_id,lable='Youtube Video', |
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size = 25, symbolType='square')) |
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for data in selected_indices: |
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author = data['snippet']['topLevelComment']['snippet']['authorDisplayName'].split(' ')[0] |
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author_img = data['snippet']['topLevelComment']['snippet']['authorProfileImageUrl'] |
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if author not in authors: |
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nodes.append(Node(id=author, |
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size=25, |
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shape="circularImage", |
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image=author_img) ) |
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authors.append(author) |
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if 'replies' in data: |
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replies = data['replies']['comments'] |
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for reply in replies: |
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reply_author = reply['snippet']['authorDisplayName'].split(' ')[0] |
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reply_author_img = reply['snippet']['authorProfileImageUrl'] |
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if reply_author not in authors: |
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nodes.append(Node(id=reply_author, |
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size=15, |
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shape="circularImage", |
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image=reply_author_img) ) |
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authors.append(reply_author) |
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edges.append( Edge(source=reply_author, |
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target=author, |
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type="CURVE_SMOOTH")) |
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edges.append(Edge(source=author, target=video_id,type="CURVE_SMOOTH")) |
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config = Config(width=750, |
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height=950, |
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directed=True, |
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physics=False, |
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hierarchical=False, |
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node={'labelProperty':'label','renderLabel':True}) |
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return_value = agraph(nodes = nodes, edges = edges, config = config) |
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except Exception as e: |
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st.write(e) |
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st.markdown("Provided Json is not Youtube API data. Unable to Parse") |
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""" |
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import streamlit as st |
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from py2neo import Graph, Node, Relationship |
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from scripts.viz import draw |
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# Initialize a Neo4j graph instance |
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graph = Graph() |
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graph.delete_all() |
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# Create nodes and relationships |
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nicole = Node("Person", name="Nicole", age=24) |
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drew = Node("Person", name="Drew", age=20) |
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mtdew = Node("Drink", name="Mountain Dew", calories=9000) |
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cokezero = Node("Drink", name="Coke Zero", calories=0) |
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coke = Node("Manufacturer", name="Coca Cola") |
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pepsi = Node("Manufacturer", name="Pepsi") |
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graph.create(nicole | drew | mtdew | cokezero | coke | pepsi) |
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graph.create(Relationship(nicole, "LIKES", cokezero)) |
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graph.create(Relationship(nicole, "LIKES", mtdew)) |
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graph.create(Relationship(drew, "LIKES", mtdew)) |
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graph.create(Relationship(coke, "MAKES", cokezero)) |
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graph.create(Relationship(pepsi, "MAKES", mtdew)) |
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# Streamlit interface |
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st.title("Py2neo Application with Streamlit") |
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# Display graph visualization using Py2neo's draw function |
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st.subheader("Graph Visualization") |
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options = {"Person": "name", "Drink": "name", "Manufacturer": "name"} |
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draw(graph, options) |
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# Display node and relationship information |
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st.subheader("Node and Relationship Information") |
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st.write("Nodes:") |
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for node in [nicole, drew, mtdew, cokezero, coke, pepsi]: |
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st.write(f"{node.labels}: {node}") |
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st.write("Relationships:") |
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for relationship in graph.match(rel_type="LIKES"): |
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st.write(relationship) |
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st.write("Manufacturers:") |
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for manufacturer in graph.match(rel_type="MAKES"): |
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st.write(manufacturer) |
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import streamlit as st |
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import json |
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import base64 |
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import requests |
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from io import StringIO |
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from streamlit_agraph import agraph, Node, Edge, Config |
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st.title('Json File Reader') |
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@st.cache_data |
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def get_json(url): |
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js = requests.get(url) |
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data = js.json() |
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return data |
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st.sidebar.header('File Upload') |
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your_file = st.sidebar.file_uploader(label="Upload the file here") |
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if your_file is not None: |
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bytes_data = your_file.getvalue() |
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json_data = json.loads(bytes_data) |
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else: |
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st.write("Example api file can be located here") |
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json_data = get_json("https://raw.githubusercontent.com/insightbuilder/python_de_learners_data/main/code_script_notebooks/python_scripts/json_reader/toplevel_comment_zGAkhN1YZXM.json") |
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try: |
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length = len(json_data) |
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if length < 15: |
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indices = st.sidebar.slider("Start n End",0,length,(0,10)) |
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else: |
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indices = st.sidebar.slider("Start n End",0,length,(0,int(length/15))) |
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selected_indices = json_data[indices[0]:indices[1]] |
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#st.write(selected_indices) |
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#creating the graph of the connection |
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nodes = [] |
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edges = [] |
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authors = [] |
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video_id = selected_indices[0]['snippet']['videoId'] |
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nodes.append(Node(id=video_id,lable='Youtube Video', |
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size = 25, symbolType='square')) |
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for data in selected_indices: |
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author = data['snippet']['topLevelComment']['snippet']['authorDisplayName'].split(' ')[0] |
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author_img = data['snippet']['topLevelComment']['snippet']['authorProfileImageUrl'] |
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if author not in authors: |
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nodes.append(Node(id=author, |
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size=25, |
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shape="circularImage", |
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image=author_img) ) |
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authors.append(author) |
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if 'replies' in data: |
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replies = data['replies']['comments'] |
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for reply in replies: |
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reply_author = reply['snippet']['authorDisplayName'].split(' ')[0] |
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reply_author_img = reply['snippet']['authorProfileImageUrl'] |
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if reply_author not in authors: |
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nodes.append(Node(id=reply_author, |
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size=15, |
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shape="circularImage", |
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image=reply_author_img) ) |
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authors.append(reply_author) |
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edges.append( Edge(source=reply_author, |
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target=author, |
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type="CURVE_SMOOTH")) |
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edges.append(Edge(source=author, target=video_id,type="CURVE_SMOOTH")) |
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#st.write(authors) |
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config = Config(width=750, |
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height=950, |
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directed=True, |
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physics=False, |
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hierarchical=False, |
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node={'labelProperty':'label','renderLabel':True}) |
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return_value = agraph(nodes = nodes, edges = edges, config = config) |
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except Exception as e: |
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st.write(e) |
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st.markdown("Provided Json is not Youtube API data. Unable to Parse") |
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#st.write(json_data) |
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""" |
