Update app.py

app.py

@@ -11,57 +11,56 @@ def isTrue(x) -> bool:
         return x
     return x.strip().lower() == 'true'
 
-with streamlit_analytics.track():
-    def launch_bot():
-        def generate_response(question):
-            response = vq.submit_query(question)
-            return response
-        
-        def generate_streaming_response(question):
-            response = vq.submit_query_streaming(question)
-            return response
-    
-        if 'cfg' not in st.session_state:
-            corpus_ids = str(os.environ['corpus_ids']).split(',')
-            cfg = OmegaConf.create({
-                'customer_id': str(os.environ['customer_id']),
-                'corpus_ids': corpus_ids,
-                'api_key': str(os.environ['api_key']),
-                'title': os.environ['title'],
-                'description': os.environ['description'],
-                'source_data_desc': os.environ['source_data_desc'],
-                'streaming': isTrue(os.environ.get('streaming', False)),
-                'prompt_name': os.environ.get('prompt_name', None),
-                'examples': os.environ.get('examples', None)
-            })
-            st.session_state.cfg = cfg
-            st.session_state.vq = VectaraQuery(cfg.api_key, cfg.customer_id, cfg.corpus_ids, cfg.prompt_name)
-    
-        cfg = st.session_state.cfg
-        vq = st.session_state.vq
-        st.set_page_config(page_title=cfg.title, layout="wide")
-    
-        # left side content
-        with st.sidebar:
-            image = Image.open('Vectara-logo.png')
-            st.markdown(f"## Welcome to {cfg.title}\n\n"
-                        f"This demo uses Retrieval Augmented Generation to ask questions about {cfg.source_data_desc}\n\n")
+def launch_bot():
+    def generate_response(question):
+        response = vq.submit_query(question)
+        return response
     
-            st.markdown("---")
-            st.markdown(
-                "## How this works?\n"
-                "This app was built with [Vectara](https://vectara.com).\n"
-                "Vectara's [Indexing API](https://docs.vectara.com/docs/api-reference/indexing-apis/indexing) was used to ingest the data into a Vectara corpus (or index).\n\n"
-                "This app uses Vectara [Chat API](https://docs.vectara.com/docs/console-ui/vectara-chat-overview) to query the corpus and present the results to you, answering your question.\n\n"
-            )
-            st.markdown("---")
-            st.image(image, width=250)
-    
-        st.markdown(f"<center> <h2> Vectara chat demo: {cfg.title} </h2> </center>", unsafe_allow_html=True)
-        st.markdown(f"<center> <h4> {cfg.description} </h4> </center>", unsafe_allow_html=True)
+    def generate_streaming_response(question):
+        response = vq.submit_query_streaming(question)
+        return response
+
+    if 'cfg' not in st.session_state:
+        corpus_ids = str(os.environ['corpus_ids']).split(',')
+        cfg = OmegaConf.create({
+            'customer_id': str(os.environ['customer_id']),
+            'corpus_ids': corpus_ids,
+            'api_key': str(os.environ['api_key']),
+            'title': os.environ['title'],
+            'description': os.environ['description'],
+            'source_data_desc': os.environ['source_data_desc'],
+            'streaming': isTrue(os.environ.get('streaming', False)),
+            'prompt_name': os.environ.get('prompt_name', None),
+            'examples': os.environ.get('examples', None)
+        })
+        st.session_state.cfg = cfg
+        st.session_state.vq = VectaraQuery(cfg.api_key, cfg.customer_id, cfg.corpus_ids, cfg.prompt_name)
+
+    cfg = st.session_state.cfg
+    vq = st.session_state.vq
+    st.set_page_config(page_title=cfg.title, layout="wide")
+
+    # left side content
+    with st.sidebar:
+        image = Image.open('Vectara-logo.png')
+        st.markdown(f"## Welcome to {cfg.title}\n\n"
+                    f"This demo uses Retrieval Augmented Generation to ask questions about {cfg.source_data_desc}\n\n")
+
+        st.markdown("---")
+        st.markdown(
+            "## How this works?\n"
+            "This app was built with [Vectara](https://vectara.com).\n"
+            "Vectara's [Indexing API](https://docs.vectara.com/docs/api-reference/indexing-apis/indexing) was used to ingest the data into a Vectara corpus (or index).\n\n"
+            "This app uses Vectara [Chat API](https://docs.vectara.com/docs/console-ui/vectara-chat-overview) to query the corpus and present the results to you, answering your question.\n\n"
+        )
+        st.markdown("---")
+        st.image(image, width=250)
+
+    st.markdown(f"<center> <h2> Vectara chat demo: {cfg.title} </h2> </center>", unsafe_allow_html=True)
+    st.markdown(f"<center> <h4> {cfg.description} </h4> </center>", unsafe_allow_html=True)
+
     
-        
-        
+    with streamlit_analytics.track():
         if "messages" not in st.session_state.keys():
             st.session_state.messages = [{"role": "assistant", "content": "How may I help you?"}]