Update app.py

app.py

@@ -13,6 +13,7 @@ from kadiApy_ragchain import KadiApyRagchain
 load_dotenv()
 
 vectorstore_path = "data/vectorstore"
+
 GROQ_API_KEY = os.environ["GROQ_API_KEY"]
 HF_TOKEN = os.environ["HF_Token"]
 
@@ -20,42 +21,47 @@ with open("config.json", "r") as file:
     config = json.load(file)
 
 login(HF_TOKEN)
+hf_api = HfApi()
 
 # Access the values
 LLM_MODEL_NAME = config["llm_model_name"]
 LLM_MODEL_TEMPERATURE = float(config["llm_model_temperature"])
 
+def initialize():
+    global kadiAPY_ragchain
+
+    vectorstore = get_chroma_vectorstore(get_SFR_Code_embedding_model(), vectorstore_path)
+    llm = get_groq_llm(LLM_MODEL_NAME, LLM_MODEL_TEMPERATURE, GROQ_API_KEY)
+
+    kadiAPY_ragchain = KadiApyRagchain(llm, vectorstore)
+
+initialize()
+
 
-# A class to encapsulate the bot logic
-class KadiBot:
-    def __init__(self, hf_token: str, groq_api_key: str, config: dict, vectorstore_path: str):
-        self.vectorstore = get_chroma_vectorstore(get_SFR_Code_embedding_model(), vectorstore_path)
-        self.llm = get_groq_llm(config["llm_model_name"], float(config["llm_model_temperature"]), groq_api_key)
-        self.kadiAPY_ragchain = KadiApyRagchain(self.llm, self.vectorstore)
 
-    def process_query(self, user_query, chat_history):
-        response = self.kadiAPY_ragchain.process_query(user_query, chat_history)
-        chat_history[-1] = (user_query, response)
-        return chat_history
+def bot_kadi(chat_history):
+    user_query = chat_history[-1][0]   
+    response = kadiAPY_ragchain.process_query(user_query, chat_history)
+    chat_history[-1] = (user_query, response)
 
+    return chat_history  
+
+
+import gradio as gr
 
 def add_text_to_chat_history(chat_history, user_input):
+    
     chat_history = chat_history + [(user_input, None)]
     return chat_history, ""
 
-
+    
 def show_history(chat_history):
     return chat_history
-
-
+    
 def reset_all():
     return [], "", ""
-
-
+    
 def main():
-    # Initialize the KadiBot
-    kadi_bot = KadiBot(HF_TOKEN, GROQ_API_KEY, config, vectorstore_path)
-
     with gr.Blocks() as demo:
         gr.Markdown("## KadiAPY - AI Coding-Assistant")
         gr.Markdown("AI assistant for KadiAPY based on RAG architecture powered by LLM")
@@ -87,12 +93,12 @@ def main():
                         cache_examples=False,
                         examples_per_page=3,
                     )
-
+        
         # Use the state to persist chat history between interactions
-        user_txt.submit(add_text_to_chat_history, [chat_history, user_txt], [chat_history, user_txt]).then(show_history, [chat_history], [chatbot])\
-                  .then(kadi_bot.process_query, [chat_history], [chatbot])          
-        submit_btn.click(add_text_to_chat_history, [chat_history, user_txt], [chat_history, user_txt]).then(show_history, [chat_history], [chatbot])\
-                  .then(kadi_bot.process_query, [chat_history], [chatbot])        
+        user_txt.submit(add_text_to_chat_history, [chat_history, user_txt], [chat_history, user_txt]).then(show_history,[chat_history], [chatbot])\
+                  .then(bot_kadi, [chat_history], [chatbot])          
+        submit_btn.click(add_text_to_chat_history, [chat_history, user_txt], [chat_history, user_txt]).then(show_history,[chat_history], [chatbot])\
+                  .then(bot_kadi, [chat_history], [chatbot])        
         clear_btn.click(
             reset_all, 
             None,  
@@ -101,6 +107,5 @@ def main():
         )
     demo.launch()
 
-
 if __name__ == "__main__":
     main()