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)