Update app.py

app.py

@@ -4,50 +4,43 @@ from google.oauth2 import service_account
 from google.cloud import language_v1
 import requests
 
-# Adding checkbox options for entity types
-entity_types_to_show = ["PERSON", "ORGANIZATION", "EVENT"]
-selected_types = st.multiselect('Select entity types to show:', entity_types_to_show)
-
 # Function for querying Google Knowledge Graph API
-def query_google_knowledge_graph(entity_name):
-    return {"info": "Knowledge Graph info for {}".format(entity_name)}
+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("---")
-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.
-""")
+# ... (your intro text here)
 
 def sample_analyze_entities(text_content, your_query=""):
-    service_account_info = json.loads(st.secrets["google_nlp"])
+    api_key = json.loads(st.secrets["google_nlp"])  # The key is the same for both APIs
     credentials = service_account.Credentials.from_service_account_info(
-        service_account_info, scopes=["https://www.googleapis.com/auth/cloud-platform"]
+        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_type_name = language_v1.Entity.Type(entity.type_).name
-        if 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)
+        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}**")
@@ -56,21 +49,15 @@ def sample_analyze_entities(text_content, your_query=""):
 
     st.write("----")
     for i, entity in enumerate(entities_list):
-        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}:**")
-                st.write(value)
-                
+        # ... (your existing entity display code)
+
         # Query Google Knowledge Graph API for each entity
-        kg_info = query_google_knowledge_graph(entity['Name'])
+        kg_info = query_google_knowledge_graph(api_key, entity['Name'])
         st.write("### Google Knowledge Graph Information")
-        st.write(kg_info)
+        st.json(kg_info)  # Display the JSON response
 
         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)")