Chat Bot by NS

This project implements a simple chat bot using Streamlit and the Groq API. The bot can take user inputs and respond with messages generated by the Groq API's chat completion feature.

Prerequisites

• Python 3.7 or higher
• Streamlit
• Groq API key

Before install i am select the llama3 70b model if you want change the model change it in here in the code

 chat_completion = client.chat.completions.create(
        messages=conversation,
        model="llama3-70b-8192", 
    )

Installation

1. Clone the repository:

   git clone https://github.com/NSTHEHACKER/chatbot_with-groq.git
   cd chatbot_with-groq 

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install dependencies:

   pip install streamlit groq

Configuration

1. Set up your Groq API key: Replace "your_groq_api_key_here" in the code with your actual Groq API key.

Usage

1. Run the Streamlit app:

   streamlit run app.py

2. Interact with the chat bot:

   • Open the URL provided by Streamlit in your web browser.
   • Type a message in the input box and press Enter.
   • The chat bot will respond with a message generated by the Groq API.

Code Overview

The main components of the code are:

• Imports: Import necessary libraries.

  import streamlit as st
  from groq import Groq

• Initialize Groq Client: Set up the Groq client with the provided API key.

  client = Groq(api_key="your_api_key")

• Initialize Conversation: Create an initial conversation list.

  conversation = [
      {
          "role": "user",
          "content": "",
      }
  ]

• Streamlit App Title: Set the title of the Streamlit app.

  st.title("Chat Bot by NS")

• Session State Initialization: Initialize the session state to store messages.

  if "messages" not in st.session_state:
      st.session_state.messages = []

• Display Messages: Loop through the messages in the session state and display them.

  for message in st.session_state.messages:
      with st.chat_message(message["role"]):
          st.markdown(message["content"])

• User Input: Create an input box for user messages.

  userinput = st.chat_input("Type something")

• Handle User Input: If there is user input, display it and get the assistant's response.

  if userinput:
      with st.chat_message("user"):
          st.markdown(userinput)
  
      st.session_state.messages.append({"role": "user", "content": userinput})
      conversation.append({"role": "user", "content": userinput})
  
      chat_completion = client.chat.completions.create(
          messages=conversation,
          model="llama3-70b-8192",
      )
  
      if chat_completion.choices:
          assistant_message = chat_completion.choices[0].message.content
  
          with st.chat_message("assistant"):
              st.markdown(assistant_message)
  
          st.session_state.messages.append({"role": "assistant", "content": assistant_message})
      else:
          st.warning("Assistant did not provide a response.")

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments

• Thanks to Streamlit for providing an easy-to-use framework for building web apps.
• Thanks to Groq for their powerful API for generating chat responses.