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915ed7e 3187374 915ed7e 3187374 f164601 3187374 915ed7e 3187374 | 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 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 | import altair as alt
import numpy as np
import pandas as pd
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!")
print("hello world")
# 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)
"""
# Welcome to Streamlit!
Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
forums](https://discuss.streamlit.io).
In the meantime, below is an example of what you can do with just a few lines of code:
"""
num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
indices = np.linspace(0, 1, num_points)
theta = 2 * np.pi * num_turns * indices
radius = indices
x = radius * np.cos(theta)
y = radius * np.sin(theta)
df = pd.DataFrame({
"x": x,
"y": y,
"idx": indices,
"rand": np.random.randn(num_points),
})
st.altair_chart(alt.Chart(df, height=700, width=700)
.mark_point(filled=True)
.encode(
x=alt.X("x", axis=None),
y=alt.Y("y", axis=None),
color=alt.Color("idx", legend=None, scale=alt.Scale()),
size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
))
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