fix(downgrade_transformers): Downgrade transformers version to bypass the errors

src/graph/state_vector_nodes.py

@@ -48,8 +48,9 @@ class question_model:
         #print(state.get('seed_question').lower())
         predict_input = self.tokenizer(
             text=state.get('seed_question').lower(),
+            max_length=512,
             truncation=True,
-            padding=True,
+            padding='max_length',
             return_tensors="pt")
         #print(predict_input)
         with torch.no_grad():

src/streamlit_app.py

@@ -15,6 +15,8 @@ import re
 import os
 import torch
 device=torch.get_default_device()
+torch.classes.__path__ = []
+
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 class StreamlitConfigUI:
 
@@ -82,6 +84,9 @@ class LoadStreamlitUI:
             self.user_controls['UN SDG Country']= st.selectbox("Choose a country (start typing to search):",options=[""] + self.unsdg_countries)
             if self.user_controls['selected_usecase']=='DeepRishSearch':
                 self.user_controls["TAVILY_API_KEY"] = st.session_state["TAVILY_API_KEY"]=st.text_input("Tavily API Key",type="password") 
+                if not self.user_controls["TAVILY_API_KEY"]:
+                    st.warning("⚠️ Please enter a Tavily API key to proceed. Don't have? refer : https://www.tavily.com/")
+
         return self.user_controls
 def display_result_on_ui( state_graph,mode="Question Refining Mode:"):
 	if mode =="Question Refining Mode:":
@@ -99,8 +104,8 @@ if __name__=='__main__':
     user_input=ui.load_streamlit_ui()
     LLM_Selection=ModelSelection(user_input)
     if user_input["GENAI_API_KEY"]:llm=LLM_Selection.setup_llm_model()
-    loaded_tokenizer = AutoTokenizer.from_pretrained('src/train_bert/topic_classifier_model')
-    loaded_model = AutoModelForSequenceClassification.from_pretrained('src/train_bert/topic_classifier_model',device_map=device)
+    loaded_tokenizer = AutoTokenizer.from_pretrained('src/train_bert/topic_classifier_model_test')
+    loaded_model = AutoModelForSequenceClassification.from_pretrained('src/train_bert/topic_classifier_model_test',device_map=device)#.to_empty(device=device)
     df_keys=pd.read_csv('src/train_bert/training_data/Keyword_Patterns.csv')
 	
     if not user_input: