fixed issue in backend

app.py

@@ -152,8 +152,6 @@ if 'secrets' in st.secrets:
 
     hf_token = st.secrets['secrets']['LLAMA2_ENDPOINT_HF_TOKEN_chris']
     hf_endpoint = st.secrets['secrets']['LLAMA2_ENDPOINT_UPLIMIT']
-    model_token_id = st.secrets['secrets']['MODAL_TOKEN_ID']
-    modal_token_secret = st.secrets['secrets']['MODAL_TOKEN_SECRET']
 
 else :
     # st.write("Loading secrets for Huggingface")
@@ -164,8 +162,6 @@ else :
 
     hf_token = st.secrets['LLAMA2_ENDPOINT_HF_TOKEN_chris']
     hf_endpoint = st.secrets['LLAMA2_ENDPOINT_UPLIMIT']
-    model_token_id = st.secrets['MODAL_TOKEN_ID']
-    modal_token_secret = st.secrets['MODAL_TOKEN_SECRET']
 
 # else:
 #     # if we want to use env file
@@ -183,7 +179,8 @@ model_default = 'models/finetuned-all-mpnet-base-v2-300' if we_are_not_online \
 
 available_models = ['sentence-transformers/all-mpnet-base-v2',
                     'sentence-transformers/all-MiniLM-L6-v2', 
-                    'models/finetuned-all-mpnet-base-v2-300']
+                    'models/finetuned-all-mpnet-base-v2-300',
+                    'sentence-transformers/all-MiniLM-L12-v2']
     
 #%%
 models_urls = {'models/finetuned-all-mpnet-base-v2-300': "https://drive.google.com/drive/folders/1asJ37-AUv5nytLtH6hp6_bVV3_cZOXfj"}
@@ -307,8 +304,10 @@ def main():
                 st.text("Running OFFLINE")
         st.write("----------")
 
-        alpha_input = st.slider(label='Alpha',min_value=0.00, max_value=1.00, value=0.40, step=0.05)
-        retrieval_limit = st.slider(label='Hybrid Search Results', min_value=10, max_value=300, value=10, step=10)
+        hubrid_search = st.toggle('Hybrid Search', True)
+        if hubrid_search:
+            alpha_input = st.slider(label='Alpha',min_value=0.00, max_value=1.00, value=0.40, step=0.05)
+            retrieval_limit = st.slider(label='Hybrid Search Results', min_value=10, max_value=300, value=10, step=10)
 
         hybrid_filter = st.toggle('Filter Guest', True) # i.e. look only at guests' data
         
@@ -322,7 +321,7 @@ def main():
         
         rag_it = st.toggle('RAG it', True)
         if rag_it:
-            st.sidebar.write(f"Using LLM '{model_nameGPT}'")
+            st.write(f"Using LLM '{model_nameGPT}'")
             llm_temperature = st.slider(label='LLM T˚', min_value=0.0, max_value=2.0, value=0.01, step=0.10 )
         
         model_name_or_path = st.selectbox(label='Model Name:', options=available_models, 
@@ -336,29 +335,35 @@ def main():
                 from finetune_backend import finetune 
                 if 'finetuned' in model_name_or_path:
                     st.write("Model already finetuned")
-                elif model_name_or_path.startswith("models/"):
-                    st.write("Sentence Transformers models only!")
+                elif "models/" in model_name_or_path:
+                    st.write("sentence-transformers models only!")
                 else:
-                    try:
-                        if 'finetuned' in finetune_model:
-                            st.write("Model already finetuned")
-                        else:
-                            model_path = finetune(model_name_or_path, savemodel=True, outpath='models')
-                            if model_path is not None:
-                                if finetune_model.split('/')[-1] not in model_path:
-                                    st.write(model_path)  # a warning from finetuning in this case
-                                elif model_path not in available_models:
-                                    # finetuning generated a model, let's add it
-                                    available_models.append(model_path)
-                                    st.write("Model saved!")
-                    except Exception:
-                        st.write("Model not found on HF or error")
+                    # try:
+                    if 'finetuned' in model_name_or_path:
+                        st.write("Model already finetuned")
+                    else:
+                        model_path = finetune(model_name_or_path, savemodel=True, outpath='models')
+                        st.write(f"model_path returned = {model_path}")
+                        if model_path is not None:
+                            if model_name_or_path.split('/')[-1] not in model_path:
+                                st.write(model_path)  # a warning from finetuning in this case
+                            elif model_path not in available_models:
+                                # finetuning generated a model, let's add it
+                                available_models.append(model_path)
+                                st.write("Model saved!")
+                    # except Exception:
+                    #     st.write("Model not found on HF or error")
         else:
             st.write("Finetuning not available on Streamlit online because of space limitations")
         
         model_name_or_path = check_model(model_name_or_path)
-        client, available_classes = get_weaviate_client(Wapi_key, url, model_name_or_path, openai_api_key)       
-                                    
+        try:
+            client, available_classes = get_weaviate_client(Wapi_key, url, model_name_or_path, openai_api_key)       
+        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(
@@ -406,6 +411,9 @@ def main():
 
     st.write("\u21D0 Open the sidebar to change Search settings \n ")  # https://home.unicode.org also 21E0, 21B0  B2 D0
     
+    if not hubrid_search:
+        st.stop()
+        
     col1, _ = st.columns([3,7])
     with col1:
         guest = st.selectbox('Select A Guest', 

backend.py

@@ -100,7 +100,8 @@ def encode_content_splits(content_splits,
 )
 def finetune(training_path='./data/training_data_300.json', 
              valid_path='./data/validation_data_100.json', 
-             model_id=None):
+             model_id=None,
+             ignore_existing=False):
 
     import os
     print("Data:", os.listdir('/root/data'))
@@ -170,7 +171,7 @@ def finetune(training_path='./data/training_data_300.json',
         zippedio = bytes_buffer
         
         return zippedio
-    except:
+    except Exception:
         return "Finetuning failed"