#import dependacies 

from groq import Groq
import streamlit as st
import os


groq_api_key=os.getenv("GROQ_API_KEY")

#initialize the a groq model

client=Groq(api_key=groq_api_key)

def get_response(query):

    response=client.chat.completions.create(
    
        messages=[
            {"role":"system","content":"You are MATH LEARN, a math assistant chatbot. Your role is to solve math problems with a detailed, step-by-step solution. Be clear and concise in each step. If there are multiple approaches, select the most efficient method. Include any formulas or key concepts used, and provide the final answer at the end."},
            {"role":"user","content": query}
        ],
        model='gemma2-9b-it',
        temperature=0.4,
        stream=False,  
        max_tokens=1024,
        stop=None
    )

    return response.choices[0].message.content


st.title('📚🔗 Welcome to MathLearn ♾ ')

# Streamlit session state to manage chat messages
if "messages" not in st.session_state:
    st.session_state.messages = []

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

# Accept user input and process response
if user_input := st.chat_input():
  st.session_state.messages.append({"role": "user", "content": user_input})
  with st.chat_message("user"):
      st.markdown(user_input)

  with st.chat_message("assistant"):
        with st.spinner("Thinking..."):
          response_text = get_response(user_input)
          st.write(response_text)

  # Save assistant's response to chat history
  st.session_state.messages.append({"role": "assistant", "content": response_text})