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 generate Google Search link for MID
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 serialize metadata
def serialize_entity_metadata(metadata):
    return {k: str(v) for k, v in metadata.items()}

# Count Google Entities (those with /g/ or /m/ mids)
def count_google_entities(entities):
    return sum(
        1 for entity in entities
        if 'mid' in entity.metadata and ('/g/' in entity.metadata['mid'] or '/m/' in entity.metadata['mid'])
    )

# Export all entities, regardless of mid
def export_entities(entities):
    entity_list = []
    for entity in entities:
        metadata = serialize_entity_metadata(entity.metadata) if entity.metadata else {}
        mid = metadata.get('mid', '')
        entity_info = {
            "Name": entity.name,
            "Type": language_v1.Entity.Type(entity.type_).name,
            "Salience Score": entity.salience,
            "MID": mid,
            "Metadata": metadata,
            "Mentions": [mention.text.content for mention in entity.mentions]
        }
        entity_list.append(entity_info)

    if not entity_list:
        st.write("No entities found to export.")
        return

    df = pd.DataFrame(entity_list)
    st.download_button(label="Export Entities as CSV", data=df.to_csv(index=False), file_name="entities.csv", mime="text/csv")

    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
st.sidebar.title("About This Tool")
st.sidebar.markdown("This tool uses Google Cloud Natural Language API to identify entities.")
st.sidebar.markdown("### How to Use")
st.sidebar.markdown("""
1. **Enter text** in the box below.
2. **Click Analyze** to detect entities.
3. **Export** results to CSV or JSON.
""")

# Header
st.title("Google Cloud NLP Entity Analyzer")
st.write("Analyze text and extract all entities, including those without Google metadata (MID).")

# NLP Analysis Logic
def 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})
    entities = response.entities

    total_entities = len(entities)
    google_entities = count_google_entities(entities)

    if google_entities == 0:
        st.markdown(f"### Found {total_entities} entities — no Google-linked (MID) entities found.")
    else:
        st.markdown(f"### Found {total_entities} entities — {google_entities} Google-linked entities with MID.")

    st.write("---")

    for i, entity in enumerate(entities):
        st.write(f"**Entity {i+1} of {total_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:.4f}")

        if entity.metadata:
            st.write("**Metadata:**")
            st.json(entity.metadata)

            if 'mid' in entity.metadata and ('/g/' in entity.metadata['mid'] or '/m/' in entity.metadata['mid']):
                query_knowledge_graph(entity.metadata['mid'])
        else:
            st.write("_No metadata available_")

        if entity.mentions:
            st.write(f"**Mentions ({len(entity.mentions)}):**")
            st.write([mention.text.content for mention in entity.mentions])

        st.write("---")

    export_entities(entities)

# Text Input
user_input = st.text_area("Enter text to analyze")

if st.button("Analyze"):
    if user_input.strip():
        analyze_entities(user_input)
    else:
        st.warning("Please enter some text before clicking Analyze.")