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

# Function to query Google's Knowledge Graph API
# Function to query Google's Knowledge Graph API
def query_knowledge_graph(entity_id):
    try:
        google_search_link = f"https://www.google.com/search?kgmid={entity_id}"
        st.markdown(f'[Open in Google Search]({google_search_link})', unsafe_allow_html=True)
    except Exception as e:
        st.write(f"An error occurred: {e}")


# Function to count entities with 'mid' that contains '/g/' or '/m/' in their metadata
def count_entities(entities):
    count = 0
    for entity in entities:
        metadata = entity.metadata
        if 'mid' in metadata and ('/g/' in metadata['mid'] or '/m/' in metadata['mid']):
            count += 1
    return count

# 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.
""")

# 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")
st.write("Entity salience scores are always relative to the analysed text.")

def sample_analyze_entities(text_content):
    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})
    
    # Count the entities with 'mid' and either '/g/' or '/m/' in their metadata
    entity_count = count_entities(response.entities)

    if entity_count == 0:
        st.markdown(f"# We found {len(response.entities)} entities - but found no Google Entities")
        st.write("---")
    elif entity_count == 1:
        st.markdown(f"# We found {len(response.entities)} entities - and found 1 Google Entity")
        st.write("---")
    else:
        st.markdown(f"# We found {len(response.entities)} entities - and found {entity_count} Google 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 'mid' in entity.metadata and ('/g/' in entity.metadata['mid'] or '/m/' in entity.metadata['mid']):
                entity_id = entity.metadata['mid']
                query_knowledge_graph(entity_id)

        if entity.mentions:
            mention_count = len(entity.mentions)
            plural = "s" if mention_count > 1 else ""
            st.write(f"Mentions: {mention_count} mention{plural}")
            st.write("Raw Array:")
            st.write(entity.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")
#user_input = st.text_area("Enter text to analyze", max_chars=5000)

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