Showing banner when issue with Weaviate

app.py

@@ -9,7 +9,7 @@ from app_features import (convert_seconds, generate_prompt_series, search_result
                           expand_content)
 from retrieval_evaluation import execute_evaluation, calc_hit_rate_scores
 from llama_index.finetuning import EmbeddingQAFinetuneDataset
-from weaviate_interface import WeaviateClient
+
 from openai import BadRequestError
 from reranker import ReRanker
 from loguru import logger 
@@ -147,7 +147,7 @@ def download_model(model_name_or_path, model_local_path):
 
 #%%
 # for streamlit online, we must download the model from google drive
- 
+# because github LFS doesn't work on forked repos
 def check_model(model_name_or_path):
     
     model_name = model_name_or_path.split('/')[-1] # remove 'sentence-transformers'
@@ -163,18 +163,27 @@ def check_model(model_name_or_path):
 
 #%% instantiate Weaviate client
 def get_weaviate_client(api_key, url, model_name_or_path, openai_api_key):
-    client = WeaviateClient(api_key, url, 
-                            model_name_or_path=model_name_or_path, 
-                            openai_api_key=openai_api_key)
-    client.display_properties.append('summary')
-    # available_classes = sorted(client.show_classes()) # doesn't work anymore
-    # print(available_classes)
-    available_classes = sorted([c['class'] for c in client.schema.get()['classes']])
-    # print(available_classes)
-    # st.write(f"Available classes: {available_classes}")
-    # st.write(f"Available classes type: {type(available_classes)}")
-    logger.info(available_classes)
-    return client, available_classes
+    try:
+        client = WeaviateClient(api_key, url, 
+                                model_name_or_path=model_name_or_path, 
+                                openai_api_key=openai_api_key)
+    except Exception:
+        # client not available, wrong key, expired free sandbox etc
+        return None, None
+    
+    try:
+        client.display_properties.append('summary')
+        # available_classes = sorted(client.show_classes()) # doesn't work anymore
+        # print(available_classes)
+        available_classes = sorted([c['class'] for c in client.schema.get()['classes']])
+        # print(available_classes)
+        # st.write(f"Available classes: {available_classes}")
+        # st.write(f"Available classes type: {type(available_classes)}")
+        logger.info(available_classes)
+        return client, available_classes
+    
+    except Exception:
+        return client, []
 
 
 ##############
@@ -260,56 +269,60 @@ def main():
                     st.write("Finetuning not available on Streamlit online because of space limitations")
         
         check_model(model_name_or_path)
-        try:
-            client, available_classes = get_weaviate_client(Wapi_key, url, model_name_or_path, openai_api_key)       
-            print(available_classes)
-        
-        except Exception as e:
-            # Weaviate doesn't know this model, maybe we're just finetuning a model
-            st.sidebar.write(f"Model unknown to Weaviate")
-            st.stop()
-                               
-        start_class = 'Impact_theory_all_mpnet_base_v2_finetuned'  
-
-        class_name = st.selectbox(
-            label='Class Name:', 
-            options=available_classes, 
-            index=available_classes.index(start_class), 
-            placeholder='Select Class Name' 
-            )
+        client, available_classes = get_weaviate_client(Wapi_key, url, model_name_or_path, openai_api_key)       
+        print("Available classes:", available_classes)
         
-        st.write("----------")
-        
-        if we_are_not_online:
-            c1,c2 = st.columns([8,1])
-            with c1:
-                show_metrics = st.button('Show Metrics on Golden set')
-                if show_metrics:
-                    # we must add it because the hybrid search toggle could hide it
-                    alpha_input2 = st.slider(label='Alpha',min_value=0.00, max_value=1.00, value=0.40, step=0.05, key=2)
-                
-                    # _, center, _ = st.columns([3, 5, 3])
-                    # with center:
-                    #     st.text("Metrics")
-                    with c2:
-                        with st.spinner(''):
-                            metrics = execute_evaluation(golden_dataset, class_name, client, alpha=alpha_input2)
-                    with c1:
-                        kw_hit_rate = metrics['kw_hit_rate']
-                        kw_mrr = metrics['kw_mrr']
-                        hybrid_hit_rate = metrics['hybrid_hit_rate']
-                        vector_hit_rate = metrics['vector_hit_rate']
-                        vector_mrr = metrics['vector_mrr']
-                        total_misses = metrics['total_misses']
-                        
-                        st.text(f"KW hit rate: {kw_hit_rate}")
-                        st.text(f"Vector hit rate: {vector_hit_rate}")
-                        st.text(f"Hybrid hit rate: {hybrid_hit_rate}")
-                        st.text(f"Hybrid MRR: {vector_mrr}")
-                        st.text(f"Total misses: {total_misses}")
+        if client is None:
+            # maybe the free sandbox has expired, or the api key is wrong
+            st.sidebar.write(f"Weaviate sandbox not accessible or expired")
+            # st.stop()
 
-        st.write("----------")
+        elif available_classes:                               
+            start_class = 'Impact_theory_all_mpnet_base_v2_finetuned'  
 
+            class_name = st.selectbox(
+                label='Class Name:', 
+                options=available_classes, 
+                index=available_classes.index(start_class), 
+                placeholder='Select Class Name' 
+                )
+            
+            st.write("----------")
+            
+            if we_are_not_online:
+                c1,c2 = st.columns([8,1])
+                with c1:
+                    show_metrics = st.button('Show Metrics on Golden set')
+                    if show_metrics:
+                        # we must add it because the hybrid search toggle could hide it
+                        alpha_input2 = st.slider(label='Alpha',min_value=0.00, max_value=1.00, value=0.40, step=0.05, key=2)
+                    
+                        # _, center, _ = st.columns([3, 5, 3])
+                        # with center:
+                        #     st.text("Metrics")
+                        with c2:
+                            with st.spinner(''):
+                                metrics = execute_evaluation(golden_dataset, class_name, client, alpha=alpha_input2)
+                        with c1:
+                            kw_hit_rate = metrics['kw_hit_rate']
+                            kw_mrr = metrics['kw_mrr']
+                            hybrid_hit_rate = metrics['hybrid_hit_rate']
+                            vector_hit_rate = metrics['vector_hit_rate']
+                            vector_mrr = metrics['vector_mrr']
+                            total_misses = metrics['total_misses']
+                            
+                            st.text(f"KW hit rate: {kw_hit_rate}")
+                            st.text(f"Vector hit rate: {vector_hit_rate}")
+                            st.text(f"Hybrid hit rate: {hybrid_hit_rate}")
+                            st.text(f"Hybrid MRR: {vector_mrr}")
+                            st.text(f"Total misses: {total_misses}")
+
+            st.write("----------")
+        else:
+            # Weaviate doesn't know this model, maybe we're just finetuning a model
+            st.sidebar.write(f"Model Unknown to Weaviate")
+            
+            
     st.title("Chat with the Impact Theory podcasts!")
     # st.image('./assets/impact-theory-logo.png', width=400)
     st.image('assets/it_tom_bilyeu.png', use_column_width=True)
@@ -317,9 +330,15 @@ def main():
     st.write('\n')
     # st.stop()
     
-
     st.write("\u21D0 Open the sidebar to change Search settings \n ")  # https://home.unicode.org also 21E0, 21B0  B2 D0
     
+    if client is None:
+        st.write("Weaviate sandbox not accessible or expired!!! Stopping execution!")
+        st.stop()
+    elif not available_classes:
+        # we have to stop here, to exit the 'with st.sidebar' block and display the banner at least
+        st.stop()
+
     if not hybrid_search:
         st.stop()
         

backend.py

@@ -9,7 +9,6 @@ from typing import List, Dict, Tuple, Union, Callable
 #     remote_path="./data",
 # )
 
-
 stub = modal.Stub("vector-search-project")
 vector_search = modal.Image.debian_slim().pip_install(
     "sentence_transformers==2.2.2", "llama_index==0.9.6.post1", "angle_emb==0.1.5"