Update app.py

app.py

@@ -9,7 +9,6 @@ entity_types_to_show = [
 ]
 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")
@@ -44,38 +43,39 @@ def sample_analyze_entities(text_content, your_query=""):
     # Create an empty list to hold the results
     entities_list = []
 
-# ... (rest of your code above)
-
     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)
-
-# ... (rest of your code below)
+            # Your existing code for creating and populating the entity_details dictionary...
 
+            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}**")
+        st.write(f"### We found {len(entities_list)} results for your query of **{your_query}**")
     else:
-        st.write(f"We found {len(entities_list)} results for your query")
-
-    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']}")
-        if entity["Metadata"]:
-            st.write("**Metadata**: ")
-            st.json(entity["Metadata"])
-        if entity["Mentions"]:
-            st.write("**Mentions**: ")
-            st.json(entity["Mentions"])
+        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("----")
+
+    st.write(f"### Language of the text: {response.language}")
 
 # User input for text analysis
 user_input = st.text_area("Enter text to analyze")