initial commit

.env

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+GROQ_API_KEY = "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"

.gitignore

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+.env

app.py

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+# *** Installing Necessary Libraries ***
+# !pip install streamlit
+# !pip install groq
+# !pip install keras
+# !pip install langchain
+# !pip install langchain_groq
+# !pip install dotenv
+
+# *** Importing Necessary Packages ***
+import streamlit as st
+import os
+from groq import Groq
+import random
+from langchain.chains import ConversationChain
+from langchain.chains.conversation.memory import ConversationBufferWindowMemory
+from langchain_groq import ChatGroq
+from langchain.prompts import PromptTemplate
+from dotenv import load_dotenv
+load_dotenv()
+
+api_key = os.environ['GROQ_API_KEY']                                                       # Retriving API Key from environment file
+
+
+def main():
+
+    st.title("Chai pe Charcha with Arghya")                                                # Define a title for the chatbot Front End
+
+    # Add customization options to the sidebar
+    st.sidebar.title('Select an LLM')                                                      # Define a title for the chatbot Side Bar
+    model = st.sidebar.selectbox(
+        'Choose a model',
+        ['mixtral-8x7b-32768', 'llama2-70b-4096', 'Gemma-7b-lt']                           # Define a choices for LLM Model
+    )
+    conversational_memory_length = st.sidebar.slider('Conversational memory length:', 
+                                                     1, 10, value = 5)                     # Define a slider to choose the lengh of converstaion in Side Bar 
+
+    memory=ConversationBufferWindowMemory(k=conversational_memory_length)                  # Store the user chosen length as memory for future use  
+
+    user_question = st.text_area("What's in your mind..")                                  # Define a prompt for question area
+
+    # session state variable
+    if 'chat_history' not in st.session_state:
+        st.session_state.chat_history=[]
+    else:
+        for message in st.session_state.chat_history:
+            memory.save_context({'input':message['human']},{'output':message['AI']})       # Storing the context of the conversation 
+
+
+    # Initialize Groq Langchain chat object and conversation
+    groq_chat = ChatGroq(
+            groq_api_key = api_key, 
+            model_name=model                                                               # Initializing the Groq ChatBot
+    )
+
+    conversation = ConversationChain(
+            llm=groq_chat,
+            memory=memory                                                                  # Initializing the conversation chain  
+    )
+    if st.button("Submit & Process"):
+        if user_question:
+            with st.spinner("Processing..."):
+                response = conversation(user_question)                                             # Generating response for User's Question
+                message = {'human':user_question,'AI':response['response']}                         
+                st.session_state.chat_history.append(message)                                      # Appending the QnA to chat history
+                st.write("Chatbot:", response['response'])                                         # Writing back the response in Front End
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+streamlit
+groq
+keras
+langchain
+langchain_groq
+dotenv