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

st.write(f"Debug: {type(st.secrets['google_nlp'])} - {st.secrets['google_nlp']}")
st.write(f"Debug: {type(st.secrets['google_nlp']['credentials'])} - {st.secrets['google_nlp']['credentials']}")


def sample_analyze_entities(text_content):
    # Directly read the JSON credentials from st.secrets
    service_account_info = st.secrets["google_nlp"]["credentials"]

    # If the credentials are already in dict form, no need to load from JSON
    if not isinstance(service_account_info, dict):
        service_account_info = json.loads(service_account_info)

    # Create credentials
    credentials = service_account.Credentials.from_service_account_info(
        service_account_info, scopes=["https://www.googleapis.com/auth/cloud-platform"]
    )

    # Initialize the LanguageServiceClient with the credentials
    client = language_v1.LanguageServiceClient(credentials=credentials)
    
# Your provided function

    client = language_v1.LanguageServiceClient()

    type_ = language_v1.Document.Type.PLAIN_TEXT
    language = "en"
    document = {"content": text_content, "type_": type_, "language": language}
    encoding_type = language_v1.EncodingType.UTF8

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

    for entity in response.entities:
        st.write(f"Representative name for the entity: {entity.name}")
        st.write(f"Entity type: {language_v1.Entity.Type(entity.type_).name}")
        st.write(f"Salience score: {entity.salience}")

        for metadata_name, metadata_value in entity.metadata.items():
            st.write(f"{metadata_name}: {metadata_value}")

        for mention in entity.mentions:
            st.write(f"Mention text: {mention.text.content}")
            st.write(f"Mention type: {language_v1.EntityMention.Type(mention.type_).name}")

    st.write(f"Language of the text: {response.language}")

# Streamlit UI
st.title('Google Cloud NLP Entity Analyzer')
user_input = st.text_area('Enter text to analyze', '')

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