Branch1

.env

@@ -0,0 +1,3 @@
+#groq_api_key = os.environ['GROQ_API_KEY']
+
+GROQ_API_KEY=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

.gitignore

@@ -0,0 +1 @@
+.env

README.md

@@ -1,3 +1,5 @@
+**This is a fork of Groq's demo chatbot app.** You can find it here: https://github.com/definitive-io/conversational-chatbot-groq
+
 # Groq LangChain Conversational Chatbot
 
 This repository contains a Streamlit application that allows users to interact with a conversational chatbot powered by the LangChain API. The application uses the Groq API to generate responses and maintains a history of the conversation to provide context for the chatbot's responses.

app.py

@@ -13,15 +13,18 @@
 from langchain.chains.conversation.memory import ConversationBufferWindowMemory
 from langchain_groq import ChatGroq
 from langchain.prompts import PromptTemplate
+from dotenv import load_dotenv
 
+load_dotenv()
 
 def main():
     """
     This function is the main entry point of the application. It sets up the Groq client, the Streamlit interface, and handles the chat interaction.
     """
-
-    # Get Groq API key
+
     groq_api_key = os.environ['GROQ_API_KEY']
+    # Get Groq API key
+    #groq_api_key = 
 
     # Display the Groq logo
     spacer, col = st.columns([5, 1])  
@@ -37,7 +40,7 @@ def main():
     system_prompt = st.sidebar.text_input("System prompt:")
     model = st.sidebar.selectbox(
         'Choose a model',
-        ['llama3-8b-8192', 'mixtral-8x7b-32768', 'gemma-7b-it']
+        ['llama3-70b-8192', 'llama3-8b-8192', 'mixtral-8x7b-32768', 'gemma-7b-it']
     )
     conversational_memory_length = st.sidebar.slider('Conversational memory length:', 1, 10, value = 5)
 

requirements.txt

@@ -1,3 +1,4 @@
+python-dotenv
 aiohttp==3.9.3
 aiosignal==1.3.1
 altair==5.2.0