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#import dependacies | |
from groq import Groq | |
import streamlit as st | |
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.compeletions.create( | |
messages=[ | |
{"role":"system","content":"You are a math assistant. 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, | |
stope=None | |
) | |
return response.choices[0].delta.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}) |