src/streamlit_app.py

import subprocess

def install_dependencies():
    subprocess.check_call(["python", "-m", "pip", "install", "--upgrade", "pip"])
    subprocess.check_call(["python", "-m", "pip", "install", "--upgrade", "huggingface_hub"])
    subprocess.check_call(["python", "-m", "pip", "install", "--upgrade", "streamlit"])
    subprocess.check_call(["python", "-m", "pip", "install", "--upgrade", "requests"])
    # other packages here
install_dependencies()

import streamlit as st
# import requests
import json
import os
from huggingface_hub import InferenceClient

# ── Page config ──────────────────────────────────────────────────────────────
st.set_page_config(
    page_title="Math Bot",
    page_icon="➗",
    layout="wide",
)

# ── Custom CSS ────────────────────────────────────────────────────────────────
def local_css(file_name):
    with open(file_name) as f:
        st.markdown(f'{f.read()}', unsafe_allow_html=True)

# Call the function at the top of your app
# local_css("./style.css")
local_css("style.css")
st.title("Math Bot")

# ── Get answers via HuggingFace LLM ───────────────────────────────────────
def get_llm_response(user_prompt):
    """Use HF Inference API to get help with highschool math from a natural-language prompt."""
    client = InferenceClient(
        # model="mistralai/Mistral-7B-Instruct-v0.3",
        # model="mistralai/Mistral-7B-Instruct-v0.2",
        # model="nvidia/Gemma-4-26B-A4B-NVFP4",
        # model="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
        model="meta-llama/Llama-3.3-70B-Instruct",
        token=os.getenv("mathbot110526read"),
        provider="fireworks-ai"
    )

    system_prompt = (
        "You are a helpful homework assistant that helps high school students with homework, especially math. "
        "Respond ONLY with an explanation of the problem that stduent poses."
        "Never give a direct answer to the problem."
    )

    messages = [
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": f"Provide help with this question:\n\n{user_prompt}, give step-by-step diretives, if possible."},
    ]

    # response = client.chat_completion(messages=messages, max_tokens=256, temperature=0.1)
    response = client.chat_completion(messages=messages, max_tokens=4000, temperature=0.1)
    result = response.choices[0].message.content.strip()
    
    return result

# ── Show response ────────────────────────────────────────────────
def show_response(question_text, response_text):
        st.markdown(f"""
    <div class="llm-response">
        <div class="question">
            {question_text}
        </div>
        <div class="response">
            {response_text}
        </div>
    </div>
    """, unsafe_allow_html=True)

# ── Main UI ───────────────────────────────────────────────────────────────────
st.markdown('<div class="main-title">➗ Math Bot</div>', unsafe_allow_html=True)
st.markdown('<div class="subtitle">powered by Llama 70B · Hugging Face</div>', unsafe_allow_html=True)

user_input = st.text_input(
    label="Your question",
    placeholder='e.g. "What are the roots of x^2 - 5x - 6?"',
    label_visibility="collapsed",
)

run = st.button("Get Help → → →", use_container_width=False)

if run and user_input.strip():
    with st.spinner("Asking the LLM to help with math…"):
        try:
            ### get and handle response here
            response = get_llm_response(user_prompt=user_input)
            show_response(user_input, response)
            
        except Exception as e:
            st.markdown(f'<div class="error-box">⚠️ LLM error: {e}</div>', unsafe_allow_html=True)

elif run:
    st.markdown('<div class="error-box">Please enter a question first.</div>', unsafe_allow_html=True)

st.markdown("---")
st.markdown(
    '<div style="font-family: Space Mono, monospace; font-size: 0.65rem; color: #37474f; text-align:center;">'
    'LLM: Llama-3.3-70B-Instruct via Hugging Face Inference API'
    '</div>',
    unsafe_allow_html=True,
)