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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) | |