Update app.py

app.py

@@ -1,66 +1,17 @@
+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):
-    # 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)
-    
-    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"])
+    # Your existing code here
 
+# Streamlit app
+st.title('Google Cloud NLP Entity Analyzer')
+user_input = st.text_area("Enter text to analyze", "Your text goes here")
 
-    st.write(f"### Language of the text: {response.language}")
+if st.button('Analyze'):
+    if user_input:
+        sample_analyze_entities(user_input)
+    else:
+        st.write("Please enter some text to analyze.")