Update app.py

app.py

@@ -2,52 +2,49 @@ 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()
+# Adding checkbox options for entity types
+entity_types_to_show = [
+    "UNKNOWN", "PERSON", "LOCATION", "ORGANIZATION", "EVENT", "WORK_OF_ART", "CONSUMER_GOOD", "OTHER"
+]
+selected_types = st.multiselect('Select entity types to show:', entity_types_to_show)
 
 # Header and intro
 st.title("Google Cloud NLP Entity Analyzer")
 st.write("## Introduction to the Knowledge Graph API")
 st.write("---")
-# ... (your intro text here)
+st.write("""
+The Google Knowledge Graph API reveals entity information related to a keyword, that Google knows about.
+This information can be very useful for SEO – discovering related topics and what Google believes is relevant.
+It can also help when trying to claim/win a Knowledge Graph box on search results.
+The API requires a high level of technical understanding, so this tool creates a simple public interface, with the ability to export data into spreadsheets.
+""")
 
 def sample_analyze_entities(text_content, your_query=""):
-    api_key = json.loads(st.secrets["google_nlp"])  # The key is the same for both APIs
+    service_account_info = json.loads(st.secrets["google_nlp"])
     credentials = service_account.Credentials.from_service_account_info(
-        api_key, scopes=["https://www.googleapis.com/auth/cloud-platform"]
+        service_account_info, 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})
 
+    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)
+        entity_type_name = language_v1.Entity.Type(entity.type_).name
+        if not selected_types or entity_type_name in selected_types:
+            entity_details = {
+                "Name": entity.name,
+                "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}**")
@@ -56,15 +53,18 @@ def sample_analyze_entities(text_content, 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(f"Relevance Score: {entity.get('Salience Score', 'N/A')} \t {i+1} of {len(entities_list)}")
+        for key, value in entity.items():
+            if value:
+                st.write(f"**{key}:**")
+                try:
+                    st.json(value)
+                except Exception as e:
+                    st.write(f"Error while displaying JSON: {e}")
         st.write("----")
 
+    st.write(f"### Language of the text: {response.language}")
+
 # User input for text analysis
 user_input = st.text_area("Enter text to analyze")
 your_query = st.text_input("Enter your query (optional)")