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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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def sample_analyze_entities(text_content, your_query=""): |
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entities_list = [] |
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for entity in response.entities: |
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entity_details = { |
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"Name": entity.name, |
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"Type": language_v1.Entity.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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st.write("----") |
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for i, entity in enumerate(entities_list): |
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st.write(f"Entity {i+1} of {len(entities_list)}") |
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st.write(f"Relevance Score: {round(entity.get('Salience Score', 0) * 100)}%") |
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st.write(f"Name: {entity.get('Name', 'N/A')}") |
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st.write(f"Type: {entity.get('Type', 'N/A')}") |
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st.write(f"Salience Score: {entity.get('Salience Score', 'N/A')}") |
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metadata = entity.get('Metadata', {}) |
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if metadata: |
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st.write("Metadata:") |
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st.write(metadata) |
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mentions = entity.get('Mentions', []) |
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if mentions: |
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st.write("Mentions:") |
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st.write(', '.join(mentions)) |
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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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