app.py

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

# Function for querying Google Knowledge Graph API
def query_google_knowledge_graph(api_key, entity_name):
    query = entity_name
    service_url = "https://kgsearch.googleapis.com/v1/entities:search"
    params = {
        'query': query,
        'limit': 1,
        'indent': True,
        'key': api_key,
    }
    response = requests.get(service_url, params=params)
    return response.json()

# Header and intro
st.title("Google Cloud NLP Entity Analyzer")
st.write("## Introduction to the Knowledge Graph API")
st.write("---")
# ... (your intro text here)

def sample_analyze_entities(text_content, your_query=""):
    api_key = json.loads(st.secrets["google_nlp"])  # The key is the same for both APIs
    credentials = service_account.Credentials.from_service_account_info(
        api_key, scopes=["https://www.googleapis.com/auth/cloud-platform"]
    )
    client = language_v1.LanguageServiceClient(credentials=credentials)

    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})


    # ... (rest of your NLP code)
    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("----")
    for i, entity in enumerate(entities_list):
        # ... (your existing entity display code)

        # Query Google Knowledge Graph API for each entity
        kg_info = query_google_knowledge_graph(api_key, entity['Name'])
        st.write("### Google Knowledge Graph Information")
        st.json(kg_info)  # Display the JSON response

        st.write("----")

# 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)