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| import json | |
| import streamlit as st | |
| from google.oauth2 import service_account | |
| from google.cloud import language_v1 | |
| # ... (sidebar and headers are the same) | |
| def sample_analyze_entities(text_content, your_query=""): | |
| # ... (NLP setup is the same) | |
| entities_list = [] | |
| for entity in response.entities: | |
| entity_details = { | |
| "Name": entity.name, | |
| "Type": language_v1.Entity.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("----") | |
| st.write("----") | |
| for i, entity in enumerate(entities_list): | |
| st.write(f"Entity {i+1} of {len(entities_list)}") | |
| st.write(f"Relevance Score: {round(entity.get('Salience Score', 0) * 100)}%") | |
| st.write(f"Name: {entity.get('Name', 'N/A')}") | |
| st.write(f"Type: {entity.get('Type', 'N/A')}") | |
| st.write(f"Salience Score: {entity.get('Salience Score', 'N/A')}") | |
| metadata = entity.get('Metadata', {}) | |
| if metadata: | |
| st.write("Metadata:") | |
| st.write(metadata) | |
| mentions = entity.get('Mentions', []) | |
| if mentions: | |
| st.write("Mentions:") | |
| st.write(', '.join(mentions)) | |
| 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) | |