Delete main.py

main.py

@@ -1,91 +0,0 @@
-import streamlit as st
-import os
-import random, string
-
-from langchain.chains import LLMChain
-from langchain_core.prompts import (
-    ChatPromptTemplate,
-    HumanMessagePromptTemplate,
-    MessagesPlaceholder,
-)
-from langchain_core.messages import SystemMessage
-from langchain.chains.conversation.memory import ConversationBufferWindowMemory
-from langchain_groq import ChatGroq
-
-if 'chat_list' not in st.session_state:
-    st.session_state.chat_list = []
-
-
-def arr():
-    for c_list in st.session_state.chat_list:
-        with st.chat_message("user"):
-            st.write("Question : " + c_list["ques"])
-        with st.chat_message("machine"):
-            st.write("Answer : " + c_list["ans"])
-
-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 = "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"
-    model = 'llama3-8b-8192'
-    # Initialize Groq Langchain chat object and conversation
-    groq_chat = ChatGroq(
-        groq_api_key=groq_api_key, 
-        model_name=model
-    )
-
-    st.title('Langchain Chatbot With llama3-8b-8192 model')
-    
-    # print("Hello! I'm your friendly Groq chatbot. I can help answer your questions, provide information, or just chat. I'm also super fast! Let's start our conversation!")
-    st.markdown("Hello! I'm your friendly Groq chatbot, dev by GJ. I can help answer your questions, provide information, or just chat. I'm also super fast! Let's start our conversation!")
-
-    system_prompt = 'You are a friendly conversational chatbot'
-    conversational_memory_length = 5 # number of previous messages the chatbot will remember during the conversation
-
-    if 'memory' not in st.session_state:
-        st.session_state.memory = ConversationBufferWindowMemory(k=conversational_memory_length, memory_key="chat_history", return_messages=True)
-    # st.write(st.session_state.memory)
-
-
-    # user_question = st.text_input("Ask a question: ")
-    user_question = st.chat_input("Ask a question:")
-    if user_question:
-        # Construct a chat prompt template using various components
-        prompt = ChatPromptTemplate.from_messages(
-            [
-                SystemMessage(
-                    content=system_prompt
-                ),  # This is the persistent system prompt that is always included at the start of the chat.
-
-                MessagesPlaceholder(
-                    variable_name="chat_history"
-                ),  # This placeholder will be replaced by the actual chat history during the conversation. It helps in maintaining context.
-
-                HumanMessagePromptTemplate.from_template(
-                    "{human_input}"
-                ),  # This template is where the user's current input will be injected into the prompt.
-            ]
-        )
-
-        # Create a conversation chain using the LangChain LLM (Language Learning Model)
-        conversation = LLMChain(
-            llm=groq_chat,  # The Groq LangChain chat object initialized earlier.
-            prompt=prompt,  # The constructed prompt template.
-            verbose=False,   # TRUE Enables verbose output, which can be useful for debugging.
-            memory=st.session_state.memory,  # The conversational memory object that stores and manages the conversation history.
-        )
-        # The chatbot's answer is generated by sending the full prompt to the Groq API.
-        response = conversation.predict(human_input=user_question)
-        # st.text("Question: " + user_question)
-        # st.text("Chatbot: " + response)
-        result = {"ques":user_question, "ans":response}
-        st.session_state.chat_list.append(result)
-        arr()
-        # st.write(st.session_state.memory)
-
-if __name__ == "__main__":
-    main()