Update app.py

app.py

@@ -43,10 +43,44 @@ def sample_analyze_entities(text_content):
 
     st.write(f"Language of the text: {response.language}")
 
-# Streamlit UI
-st.title('Google Cloud NLP Entity Analyzer')
-user_input = st.text_area('Enter text to analyze', '')
+    # Create an empty list to hold the results
+    entities_list = []
 
-if st.button('Analyze'):
-    if user_input:
-        sample_analyze_entities(user_input)
+    for entity in response.entities:
+        # Create a dictionary to hold individual entity details
+        entity_details = {
+            "Name": entity.name,
+            "Type": language_v1.Entity.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
+            })
+
+        # 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']}")
+        
+        if entity["Metadata"]:
+            st.write("**Metadata**: ")
+            st.json(entity["Metadata"])
+        
+        if entity["Mentions"]:
+            st.write("**Mentions**: ")
+            st.json(entity["Mentions"])
+
+    st.write(f"### Language of the text: {response.language}")