app.py

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

# 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 filter entities with "mid" in their metadata
def filter_entities_with_mid(entities):
    return [entity for entity in entities if 'mid' in entity.metadata]

# Function to serialize entity metadata
def serialize_entity_metadata(metadata):
    return {k: str(v) for k, v in metadata.items()}

# Function to export entities as a JSON or CSV file
def export_entities(entities):
    entity_list = []
    for entity in entities:
        entity_info = {
            "Name": entity.name,
            "Type": language_v1.Entity.Type(entity.type_).name,
            "Salience Score": entity.salience,
            "Metadata": serialize_entity_metadata(entity.metadata),
            "Mentions": [mention.text.content for mention in entity.mentions]
        }
        entity_list.append(entity_info)
    
    # Convert to DataFrame for easier export as CSV
    df = pd.DataFrame(entity_list)
    
    # Export as CSV
    csv = df.to_csv(index=False)
    st.download_button(label="Export Entities as CSV", data=csv, file_name="entities.csv", mime="text/csv")
    
    # Export as JSON
    json_data = json.dumps(entity_list, indent=2)
    st.download_button(label="Export Entities as JSON", data=json_data, file_name="entities.json", mime="application/json")

# 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.
5. **Export Entities**: Export the entities as JSON or CSV.
""")

# 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 analyzed 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})
    
    # Filter entities that have a "mid" in their metadata
    entities_with_mid = filter_entities_with_mid(response.entities)
    
    st.markdown(f"# We found {len(entities_with_mid)} entities with 'mid' in their metadata")
    st.write("---")
    
    for i, entity in enumerate(entities_with_mid):
        st.write(f"Entity {i+1} of {len(entities_with_mid)}")
        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:
                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("---")

    # Add the export functionality
    export_entities(entities_with_mid)

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

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