Update app.py

app.py

@@ -1,13 +1,63 @@
-import streamlit as st
 import json
 from google.oauth2 import service_account
 from google.cloud import language_v1
+import streamlit as st
 
 def sample_analyze_entities(text_content):
-    # Your existing code here
-    pass  # Remove this line when you add your code back
+    service_account_info = json.loads(st.secrets["google_nlp"])
+    credentials = service_account.Credentials.from_service_account_info(
+        service_account_info, 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})
+
+    # Create an empty list to hold the results
+    entities_list = []
+
+    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"])
+
 
-# Streamlit app
 st.title('Google Cloud NLP Entity Analyzer')
 user_input = st.text_area("Enter text to analyze", "Your text goes here")