Update app.py

app.py

@@ -1,19 +1,20 @@
 import json
+import streamlit as st
 from google.oauth2 import service_account
 from google.cloud import language_v1
-import streamlit as st
 
 # 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.
+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):
+def sample_analyze_entities(text_content, your_query=""):
     # Parse the JSON string to a dictionary
     service_account_info = json.loads(st.secrets["google_nlp"])
 
@@ -58,25 +59,34 @@ def sample_analyze_entities(text_content):
         # Append the dictionary to the list
         entities_list.append(entity_details)
 
-    # Use Streamlit to display the results
-    st.write("### Analyzed Entities")
-    for entity in entities_list:
-        st.write(f"**Name**: {entity['Name']}")
-        st.write(f"**Type**: {entity['Type']}")
-        st.write(f"**Salience Score**: {entity['Salience Score']}")
+    # 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("----")
 
-        if entity["Metadata"]:
-            st.write("**Metadata**: ")
-            st.json(entity["Metadata"])
+    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)}")
+        st.write(f"### {entity.get('Name', 'N/A')}")
+        st.write(f"**Entity Type:** {entity.get('Type', 'N/A')}")
 
-        if entity["Mentions"]:
-            st.write("**Mentions**: ")
-            st.json(entity["Mentions"])
+        if entity.get('Metadata'):
+            st.write("**Metadata:**")
+            st.json(entity['Metadata'])
 
+        if entity.get('Mentions'):
+            st.write("**Mentions:**")
+            st.json(entity['Mentions'])
+
+        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)")
+
 if st.button("Analyze"):
-    sample_analyze_entities(user_input)
+    sample_analyze_entities(user_input, your_query)