Update app.py

app.py

@@ -5,80 +5,54 @@ from google.cloud import language_v1
 
 # Sidebar content
 st.sidebar.title("About This Tool")
-st.sidebar.markdown("### Descriptive Introduction")
-st.sidebar.markdown
+st.sidebar.markdown("This tool leverages Google's NLP technology for entity analysis.")
 st.sidebar.markdown("### Step-by-Step Guide")
 st.sidebar.markdown("""
 1. **Open the Tool**: Navigate to the URL where the tool is hosted.
-2. **User Input**:
-    - **Text to Analyze**: In the text area labeled "Enter text to analyze", paste or type the text you want to analyze.
-    - **Query**: Optionally, you can also enter a specific query in the text input field labeled "Enter your query (optional)". This is for your own reference.
-    - **Analyze**: Click the button labeled "Analyze". The tool will then process the text and perform entity analysis on it.
-3. **View Results**: 
-    - After the analysis is complete, you'll see a section that says, "We found X results for your query of your_query" (or just "We found results for your query" if no query was entered).
-    - Below this, you'll find a line-by-line breakdown of each entity identified in the text.
+2. **User Input**: Enter the text you want to analyze.
+3. **Analyze**: Click the 'Analyze' button.
+4. **View Results**: See the identified entities and their details.
 """)
 
 # Header and intro
 st.title("Google Cloud NLP Entity Analyzer")
-st.write("""The "Google Cloud NLP Entity Analyzer" is a powerful tool designed to analyze text and identify various types of entities such as people, locations, organizations, and events. Leveraging Google's Natural Language Processing (NLP) technology, this tool provides insights into how Google understands text, which can be particularly useful for Search Engine Optimization (SEO) efforts. It also serves as an interface to the Google Knowledge Graph API, providing additional contextual information about the identified entities.""")
+st.write("This tool analyzes text to identify entities such as people, locations, organizations, and events.")
 
-
-
-def sample_analyze_entities(text_content, your_query=""):
+def sample_analyze_entities(text_content):
+    # Assuming service_account_info is set in your Streamlit secrets
     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}
+    document = {"content": text_content, "type_": language_v1.Document.Type.PLAIN_TEXT, "language": "en"}
     encoding_type = language_v1.EncodingType.UTF8
 
     response = client.analyze_entities(request={"document": document, "encoding_type": encoding_type})
 
-    entities_list = []
-    for entity in response.entities:
-        entity_details = {
-            "Name": entity.name,
-            "Type": language_v1.Entity.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)
-
-    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(f"### We found {len(response.entities)} entities")
+    st.write("---")
 
-    st.write("----")
-    for i, entity in enumerate(entities_list):
-        st.write(f"Entity {i+1} of {len(entities_list)}")
-        st.write(f"Relevance Score: {round(entity.get('Salience Score', 0) * 100)}%")
-        st.write(f"Name: {entity.get('Name', 'N/A')}")
-        st.write(f"Type: {entity.get('Type', 'N/A')}")
-        st.write(f"Salience Score: {entity.get('Salience Score', 'N/A')}")
-
-        metadata = entity.get('Metadata', {})
-        if metadata:
+    for i, entity in enumerate(response.entities):
+        st.write(f"Entity {i+1} of {len(response.entities)}")
+        st.write(f"Name: {entity.name}")
+        st.write(f"Type: {language_v1.Entity.Type(entity.type_).name}")
+        st.write(f"Salience Score: {entity.salience}")
+        
+        if entity.metadata:
             st.write("Metadata:")
-            st.write(metadata)
+            st.write(entity.metadata)
 
-        mentions = entity.get('Mentions', [])
-        if mentions:
+        if entity.mentions:
             st.write("Mentions:")
-            st.write(json.dumps(mentions))
-
-        st.write("----")
-
-    st.write(f"### Language of the text: {response.language}")
+            st.write(', '.join([mention.text.content for mention in entity.mentions]))
+        
+        st.write("---")
 
 # User input for text analysis
-user_input = st.text_area("Enter text to analyze")
-#your_query = st.text_input("Enter your query (optional)")
+user_input = st.text_area("Enter text to analyze", max_chars=2500)
 
 if st.button("Analyze"):
-    sample_analyze_entities(user_input)
+    if user_input:
+        sample_analyze_entities(user_input)