app.py

import streamlit as st
from groq import Groq
import os
from dotenv import load_dotenv

# Load environment variables from .env
load_dotenv()

# Set up Groq client
client = Groq()

# Define a function to get the response from the Llama model
def get_response(user_input):
    completion = client.chat.completions.create(
        model="deepseek-r1-distill-llama-70b",
        messages=[{"role": "user", "content": user_input}],
        temperature=0.6,
        max_completion_tokens=4096,
        top_p=0.95,
        stream=True,
        stop=None,
    )
    
    response = ""
    for chunk in completion:
        response += chunk.choices[0].delta.content or ""
    
    return response

# Streamlit UI for user interaction
st.title("Educational Assistant Chatbot")
st.write("Ask me anything related to Math, English (Nouns), and other educational topics!")

# Provide different subjects to choose from
subject_choice = st.selectbox("Choose Subject:", ["Math", "English", "Science", "History", "Geography"])

# Based on the subject, prompt the user
if subject_choice == "Math":
    st.write("Ask me any math question!")
elif subject_choice == "English":
    st.write("Ask me about Nouns or other English topics!")
elif subject_choice == "Science":
    st.write("Ask me about Science topics!")
elif subject_choice == "History":
    st.write("Ask me about History topics!")
elif subject_choice == "Geography":
    st.write("Ask me about Geography!")

user_input = st.text_input("Your Question:")

if user_input:
    question = f"Subject: {subject_choice}. Question: {user_input}"
    response = get_response(question)
    st.write(f"**Bot Response:** {response}")