app.py

import streamlit as st
import os

from groq import Groq
from dotenv import load_dotenv

load_dotenv()

st.title("AI-powered Mindfulness App")
st.write("""
The application is designed to assist you in making informed decisions by providing a structured platform for consultation. 
Embedded with Panca Sradha values, this application will ensure that all guidance aligns with the core principles upheld by Kalbe Group, promoting consistency and integrity in decision-making.
You can input the background context of the decision you are considering and articulate the specific questions for which you need guidance. 
The application will generate two outputs: a detailed recommendation and a corresponding risk assessment with suggested mitigation strategies. 
""")

# Input text boxes
input1 = st.text_area("Background", height=200)
input2 = st.text_area("Question", height=50)

client = Groq(
    api_key=os.environ['GROQ_API_KEY'],
)

prompt = """
You are a company consultant in Kalbe Group with a deep understanding of the company's core values, known as Panca Sradha.
Your task is to provide strategic recommendations and mitigated risks based on the following inputs:

Background: {}
Question: {}

IMPORTANT: Your recommendations must align with the Panca Sradha values:

1. Trust is the glue of life.
2. Mindfulness is the foundation of our action.
3. Innovation is the key to our success.
4. Strive to be the best.
5. Interconnectedness is a universal way of life.


Based on these inputs, your task is to provide two outputs:

1. Recommendation: A detailed suggestion that aligns with Kalbe Group's strategic objectives.
2. Risk and Mitigation: An assessment of potential risks associated with the decision and corresponding strategies to mitigate these risks.

Ensure your response is connected to the Panca Sradha values by emphasizing them in italics whenever mentioned.
"""

def generate_answer(background, question):

    chat_completion = client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": prompt.format(background, question),
            }
        ],
        model="llama-3.1-70b-versatile",
    )
    tasks = chat_completion.choices[0].message.content.strip()

    return tasks

if st.button("Submit"):
    result = generate_answer(input1, input2)
    st.write(result)