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| import json | |
| import streamlit as st | |
| from google.oauth2 import service_account | |
| from google.cloud import language_v1 | |
| import requests | |
| # Adding checkbox options for entity types | |
| entity_types_to_show = ["PERSON", "ORGANIZATION", "EVENT"] | |
| selected_types = st.multiselect('Select entity types to show:', entity_types_to_show) | |
| # Function for querying Google Knowledge Graph API | |
| def query_google_knowledge_graph(entity_name): | |
| return {"info": "Knowledge Graph info for {}".format(entity_name)} | |
| # Header and intro | |
| st.title("Google Cloud NLP Entity Analyzer") | |
| 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, your_query=""): | |
| service_account_info = json.loads(st.secrets["google_nlp"]) | |
| credentials = service_account.Credentials.from_service_account_info( | |
| service_account_info, scopes=["https://www.googleapis.com/auth/cloud-platform"] | |
| ) | |
| client = language_v1.LanguageServiceClient(credentials=credentials) | |
| 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}) | |
| entities_list = [] | |
| for entity in response.entities: | |
| entity_type_name = language_v1.Entity.Type(entity.type_).name | |
| if entity_type_name in selected_types: | |
| entity_details = { | |
| "Name": entity.name, | |
| "Type": entity_type_name, | |
| "Salience Score": entity.salience, | |
| "Metadata": entity.metadata, | |
| "Mentions": [mention.text.content for mention in entity.mentions] | |
| } | |
| entities_list.append(entity_details) | |
| if your_query: | |
| st.write(f"### We found {len(entities_list)} results for your query of **{your_query}**") | |
| else: | |
| st.write("### We found results for your query") | |
| st.write("----") | |
| for i, entity in enumerate(entities_list): | |
| st.write(f"Relevance Score: {entity.get('Salience Score', 'N/A')} \t {i+1} of {len(entities_list)}") | |
| for key, value in entity.items(): | |
| if value: | |
| st.write(f"**{key}:**") | |
| st.write(value) | |
| # Query Google Knowledge Graph API for each entity | |
| kg_info = query_google_knowledge_graph(entity['Name']) | |
| st.write("### Google Knowledge Graph Information") | |
| st.write(kg_info) | |
| st.write("----") | |
| st.write(f"### Language of the text: {response.language}") | |
| # User input for text analysis | |
| user_input = st.text_area("Enter text to analyze") | |
| your_query = st.text_input("Enter your query (optional)") | |
| if st.button("Analyze"): | |
| sample_analyze_entities(user_input, your_query) | |