File size: 2,675 Bytes
a2110a1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | # Import necessary modules
import streamlit as st
import streamlit.components.v1 as components # For embedding custom HTML
from generate_knowledge_graph import generate_knowledge_graph
# Set up Streamlit page configuration
st.set_page_config(
page_icon=None,
layout="wide", # Use wide layout for better graph display
initial_sidebar_state="auto",
menu_items=None
)
# Set the title of the app
st.title("Knowledge Graph From Text")
# Sidebar section for user input method
st.sidebar.title("Input document")
input_method = st.sidebar.radio(
"Choose an input method:",
["Upload txt", "Input text"], # Options for uploading a file or manually inputting text
)
# Case 1: User chooses to upload a .txt file
if input_method == "Upload txt":
# File uploader widget in the sidebar
uploaded_file = st.sidebar.file_uploader(label="Upload file", type=["txt"])
if uploaded_file is not None:
# Read the uploaded file content and decode it as UTF-8 text
text = uploaded_file.read().decode("utf-8")
# Button to generate the knowledge graph
if st.sidebar.button("Generate Knowledge Graph"):
with st.spinner("Generating knowledge graph..."):
# Call the function to generate the graph from the text
net = generate_knowledge_graph(text)
st.success("Knowledge graph generated successfully!")
# Save the graph to an HTML file
output_file = "knowledge_graph.html"
net.save_graph(output_file)
# Open the HTML file and display it within the Streamlit app
HtmlFile = open(output_file, 'r', encoding='utf-8')
components.html(HtmlFile.read(), height=1000)
# Case 2: User chooses to directly input text
else:
# Text area for manual input
text = st.sidebar.text_area("Input text", height=300)
if text: # Check if the text area is not empty
if st.sidebar.button("Generate Knowledge Graph"):
with st.spinner("Generating knowledge graph..."):
# Call the function to generate the graph from the input text
net = generate_knowledge_graph(text)
st.success("Knowledge graph generated successfully!")
# Save the graph to an HTML file
output_file = "knowledge_graph.html"
net.save_graph(output_file)
# Open the HTML file and display it within the Streamlit app
HtmlFile = open(output_file, 'r', encoding='utf-8')
components.html(HtmlFile.read(), height=1000) |