app.py

from groq import Groq
import os
from dotenv import load_dotenv

# Load API key from .env file
load_dotenv()
GROQ_API_KEY = os.getenv("GROQ_API_KEY")

# Initialize Groq client
client = Groq(api_key=GROQ_API_KEY)

def generate_fitness_response(user_input):
    """
    Generate a response from the LLaMA model based on user input.
    """
    system_prompt = """
    You are a Health Fitness Assistant. Your role is to provide personalized fitness advice, including:
    - Diet plans (e.g., vegetarian, vegan, keto, etc.)
    - Exercise routines (e.g., gym, home workouts, yoga, etc.)
    - Weight loss strategies
    - Portion control guidance
    - Sleep optimization tips
    - Expert fitness tips and tricks

    Always respond in a friendly, professional, and informative manner. Tailor your advice to the user's specific needs and goals.
    """

    completion = client.chat.completions.create(
        model="deepseek-r1-distill-llama-70b",
        messages=[
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": user_input},
        ],
        temperature=0.6,
        max_tokens=4096,
        top_p=0.95,
        stream=True,
        stop=None,
    )

    response = ""
    for chunk in completion:
        response += chunk.choices[0].delta.content or ""
    return response
import streamlit as st

# Title of the chatbot
st.title("Health Fitness Assistant")

# Initialize chat history
if "messages" not in st.session_state:
    st.session_state.messages = []

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

# User input
if prompt := st.chat_input("Ask me anything about fitness..."):
    # Add user message to chat history
    st.session_state.messages.append({"role": "user", "content": prompt})
    with st.chat_message("user"):
        st.markdown(prompt)

    # Generate assistant response
    with st.chat_message("assistant"):
        response = generate_fitness_response(prompt)
        st.markdown(response)

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