Update app.py

app.py

@@ -21,18 +21,11 @@ The API requires a high level of technical understanding, so this tool creates a
 """)
 
 def sample_analyze_entities(text_content, your_query=""):
-    # Parse the JSON string to a dictionary
     service_account_info = json.loads(st.secrets["google_nlp"])
-
-    # Create credentials
     credentials = service_account.Credentials.from_service_account_info(
         service_account_info, scopes=["https://www.googleapis.com/auth/cloud-platform"]
     )
-
-    # Initialize the LanguageServiceClient with the credentials
     client = language_v1.LanguageServiceClient(credentials=credentials)
-
-    # NLP analysis
     type_ = language_v1.Document.Type.PLAIN_TEXT
     language = "en"
     document = {"content": text_content, "type_": type_, "language": language}
@@ -40,57 +33,35 @@ def sample_analyze_entities(text_content, your_query=""):
 
     response = client.analyze_entities(request={"document": document, "encoding_type": encoding_type})
 
-    # Create an empty list to hold the results
     entities_list = []
+    for entity in response.entities:
+        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)
 
-for entity in response.entities:
-    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": [],
-            "Mentions": []
-        }
-
-        for metadata_name, metadata_value in entity.metadata.items():
-            entity_details["Metadata"].append({metadata_name: metadata_value})
-
-        for mention in entity.mentions:
-            entity_details["Mentions"].append({
-                "Text": mention.text.content,
-                "Type": language_v1.EntityMention.Type(mention.type_).name
-            })
-
-        entities_list.append(entity_details)  # Append the dictionary to the list
-
-
-    # Streamlit UI
     if your_query:
         st.write(f"### We found {len(entities_list)} results for your query of **{your_query}**")
     else:
         st.write("### We found results for 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)}")
-        
-        # Display all key-value pairs in the entity dictionary
         for key, value in entity.items():
             if value:
                 st.write(f"**{key}:**")
-                if isinstance(value, (list, dict)):
-                    try:
-                        st.json(value)
-                    except Exception as e:
-                        st.write(f"Error while displaying JSON: {e}")
-                else:
-                    st.write(value)
-        
-        st.write("----")  # Line separator
-        st.write(" ")  # Extra line
+                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}")