import json
import streamlit as st
from google.oauth2 import service_account
from google.cloud import language_v1

# Sidebar content
st.sidebar.title("About This Tool")
st.sidebar.markdown("This tool leverages Google's NLP technology for entity analysis.")
st.sidebar.markdown("### Step-by-Step Guide")
st.sidebar.markdown("""
1. **Open the Tool**: Navigate to the URL where the tool is hosted.
2. **User Input**: Enter the text you want to analyze.
3. **Analyze**: Click the 'Analyze' button.
4. **View Results**: See the identified entities and their details.

To do: 
https://www.linkedin.com/pulse/seo-content-writing-how-optimize-entity-salience-emmanuel-dan-awoh/
""")

# Header and intro
st.title("Google Cloud NLP Entity Analyzer")
st.write("This tool analyzes text to identify entities such as people, locations, organizations, and events.")

def sample_analyze_entities(text_content):
    # Assuming service_account_info is set in your Streamlit secrets
    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)
    document = {"content": text_content, "type_": language_v1.Document.Type.PLAIN_TEXT, "language": "en"}
    encoding_type = language_v1.EncodingType.UTF8

    response = client.analyze_entities(request={"document": document, "encoding_type": encoding_type})

    st.write(f"### We found {len(response.entities)} entities")
    st.write("---")

    for i, entity in enumerate(response.entities):
        st.write(f"Entity {i+1} of {len(response.entities)}")
        st.write(f"Name: {entity.name}")
        st.write(f"Type: {language_v1.Entity.Type(entity.type_).name}")
        st.write(f"Salience Score: {entity.salience}")
        
        if entity.metadata:
            st.write("Metadata:")
            st.write(entity.metadata)

        if entity.mentions:
            st.write("Mentions:")
            st.write(', '.join([mention.text.content for mention in entity.mentions]))
        
        st.write("---")

# User input for text analysis
user_input = st.text_area("Enter text to analyze", max_chars=2500)

if st.button("Analyze"):
    if user_input:
        sample_analyze_entities(user_input)