updated API key reference

app.py

@@ -7,7 +7,6 @@
 #   - Updated to UI to show predicted mental health condition in behind the scence regardless of the ositive/negative sentiment
 ###
 
-from dotenv import load_dotenv, find_dotenv
 import pandas as pd
 import streamlit as st
 from q_learning_chatbot import QLearningChatbot
@@ -251,7 +250,7 @@ if user_message:
             print(st.session_state.messages)
 
             # LLM Response Generator
-            load_dotenv(find_dotenv())
+            HUGGINGFACEHUB_API_TOKEN = os.getenv('HUGGINGFACEHUB_API_TOKEN')
 
             llm_model = LLLResponseGenerator()
             temperature = 0.5
@@ -265,10 +264,10 @@ if user_message:
             # Question asked to the user: {question}
 
             template = """INSTRUCTIONS: {context}
-            
-                Respond to the user with a tone of {ai_tone}. 
-                
-                Response by the user: {user_text}  
+
+                Respond to the user with a tone of {ai_tone}.
+
+                Response by the user: {user_text}
                 Response;
                 """
             context = f"You are a mental health supporting non-medical assistant. Provide some advice and ask a relevant question back to the user. {all_messages}"

llm_response_generator.py

@@ -2,14 +2,13 @@
 #- Author: Jaelin Lee
 #- Date: Mar 16, 2024
 #- Description: Calls HuggingFace API to generate natural response.
-#- Credit: The initial code is from Abhishek Dutta. 
-#           Most of the code is kept as he created. 
+#- Credit: The initial code is from Abhishek Dutta.
+#           Most of the code is kept as he created.
 #           I only added a modification to convert it to class.
 #           And, I tweaked the prompt to feed into the `streamlit_app.py` file.
 #---
 
 import os
-from dotenv import load_dotenv, find_dotenv
 from langchain_community.llms import HuggingFaceHub
 from langchain_community.llms import OpenAI
 # from langchain.llms import HuggingFaceHub, OpenAI
@@ -23,7 +22,7 @@ class LLLResponseGenerator():
 
     def __init__(self):
         print("initialized")
-        
+
 
     def llm_inference(
         self,
@@ -117,7 +116,7 @@ class LLLResponseGenerator():
 
 if __name__ == "__main__":
     # Please ensure you have a .env file available with 'HUGGINGFACEHUB_API_TOKEN' and 'OPENAI_API_KEY' values.
-    load_dotenv(find_dotenv())
+    HUGGINGFACEHUB_API_TOKEN = os.getenv('HUGGINGFACEHUB_API_TOKEN')
 
     context = "You are a mental health supporting non-medical assistant. DO NOT PROVIDE any medical advice with conviction."
 
@@ -130,15 +129,15 @@ if __name__ == "__main__":
 
     # The user may have signs of {questionnaire}.
     template = """INSTRUCTIONS: {context}
-    
-    Respond to the user with a tone of {ai_tone}. 
-    
+
+    Respond to the user with a tone of {ai_tone}.
+
     Question asked to the user: {question}
-    
-    Response by the user: {user_text}    
-    
+
+    Response by the user: {user_text}
+
     Provide some advice and ask a relevant question back to the user.
-    
+
     Response;
     """
 
@@ -146,7 +145,7 @@ if __name__ == "__main__":
     max_length = 128
 
     model = LLLResponseGenerator()
-    
+
 
     llm_response = model.llm_inference(
         model_type="huggingface",
@@ -159,5 +158,5 @@ if __name__ == "__main__":
         temperature=temperature,
         max_length=max_length,
         )
-    
-    print(llm_response)
+
+    print(llm_response)