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import json
from google.oauth2 import service_account
from google.cloud import language_v1
import streamlit as st
# Header and intro
st.write("## Introduction to the Knowledge Graph API")
st.write("---")
st.write("""
The Google Knowledge Graph API reveals entity information related to a keyword, that Google knows about.
This information can be very useful for SEO – discovering related topics and what Google believes is relevant.
It can also help when trying to claim/win a Knowledge Graph box on search results.
The API requires a high level of technical understanding, so this tool creates a simple public interface, with the ability to export data into spreadsheets.
""")
def sample_analyze_entities(text_content):
# Parse the JSON string to a dictionary
service_account_info = json.loads(st.secrets["google_nlp"])
# Create credentials
credentials = service_account.Credentials.from_service_account_info(
service_account_info, scopes=["https://www.googleapis.com/auth/cloud-platform"]
)
# Initialize the LanguageServiceClient with the credentials
client = language_v1.LanguageServiceClient(credentials=credentials)
# NLP analysis
type_ = language_v1.Document.Type.PLAIN_TEXT
language = "en"
document = {"content": text_content, "type_": type_, "language": language}
encoding_type = language_v1.EncodingType.UTF8
response = client.analyze_entities(request={"document": document, "encoding_type": encoding_type})
# Create an empty list to hold the results
entities_list = []
for entity in response.entities:
# Create a dictionary to hold individual entity details
entity_details = {
"Name": entity.name,
"Type": language_v1.Entity.Type(entity.type_).name,
"Salience Score": entity.salience,
"Metadata": [],
"Mentions": []
}
for metadata_name, metadata_value in entity.metadata.items():
entity_details["Metadata"].append({metadata_name: metadata_value})
for mention in entity.mentions:
entity_details["Mentions"].append({
"Text": mention.text.content,
"Type": language_v1.EntityMention.Type(mention.type_).name
})
# Append the dictionary to the list
entities_list.append(entity_details)
# Use Streamlit to display the results
st.write("### Analyzed Entities")
for entity in entities_list:
st.write(f"**Name**: {entity['Name']}")
st.write(f"**Type**: {entity['Type']}")
st.write(f"**Salience Score**: {entity['Salience Score']}")
if entity["Metadata"]:
st.write("**Metadata**: ")
st.json(entity["Metadata"])
if entity["Mentions"]:
st.write("**Mentions**: ")
st.json(entity["Mentions"])
st.write(f"### Language of the text: {response.language}")
# User input for text analysis
user_input = st.text_area("Enter text to analyze")
if st.button("Analyze"):
sample_analyze_entities(user_input)
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