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import json |
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import streamlit as st |
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from google.oauth2 import service_account |
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from google.cloud import language_v1 |
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selected_types = st.multiselect('Select entity types to show:', entity_types_to_show) |
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st.title("Google Cloud NLP Entity Analyzer") |
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st.write("## Introduction to the Knowledge Graph API") |
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st.write("---") |
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st.write(""" |
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The Google Knowledge Graph API reveals entity information related to a keyword, that Google knows about. |
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This information can be very useful for SEO – discovering related topics and what Google believes is relevant. |
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It can also help when trying to claim/win a Knowledge Graph box on search results. |
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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. |
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""") |
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def sample_analyze_entities(text_content, your_query=""): |
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service_account_info = json.loads(st.secrets["google_nlp"]) |
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credentials = service_account.Credentials.from_service_account_info( |
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service_account_info, scopes=["https://www.googleapis.com/auth/cloud-platform"] |
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) |
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client = language_v1.LanguageServiceClient(credentials=credentials) |
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type_ = language_v1.Document.Type.PLAIN_TEXT |
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language = "en" |
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document = {"content": text_content, "type_": type_, "language": language} |
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encoding_type = language_v1.EncodingType.UTF8 |
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response = client.analyze_entities(request={"document": document, "encoding_type": encoding_type}) |
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entities_list = [] |
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for entity in response.entities: |
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entity_type_name = language_v1.Entity.Type(entity.type_).name |
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if entity_type_name in ["PERSON", "LOCATION", "ORGANIZATION", "EVENT"]: |
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entity_details = { |
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"Name": entity.name, |
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"Type": entity_type_name, |
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"Salience Score": entity.salience, |
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"Metadata": entity.metadata, |
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"Mentions": [mention.text.content for mention in entity.mentions] |
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} |
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entities_list.append(entity_details) |
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if your_query: |
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st.write(f"### We found {len(entities_list)} results for your query of **{your_query}**") |
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else: |
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st.write("### We found results for your query") |
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st.write("----") |
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for i, entity in enumerate(entities_list): |
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st.write(f"Relevance Score: {entity.get('Salience Score', 'N/A')} \t {i+1} of {len(entities_list)}") |
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for key, value in entity.items(): |
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if value: |
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st.write(f"**{key}:**") |
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st.write(value) |
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st.write("----") |
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st.write(f"### Language of the text: {response.language}") |
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user_input = st.text_area("Enter text to analyze") |
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your_query = st.text_input("Enter your query (optional)") |
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if st.button("Analyze"): |
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sample_analyze_entities(user_input, your_query) |
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