src/streamlit_app.py

import os
os.environ["STREAMLIT_HOME"] = "/tmp/.streamlit"
from langchain_huggingface import HuggingFaceEndpoint
import streamlit as st
from langchain_core.messages import HumanMessage

# constants
QUESTION = "Compute the integral of f(x) = x^2."
MODEL = "mistralai/Mistral-7B-Instruct-v0.3"
hf_token = os.getenv("HF_TOKEN")
SUBJECT = "Calculus BC"

# Check if HF token is set
if not hf_token:
    st.error("HF_TOKEN is not set. Please add it to your HuggingFace secrets.")
    st.stop()

# remembers session
if "response" not in st.session_state:
    st.session_state.response = ""

def get_llm(model_id=MODEL, max_new_tokens=300, temperature=0.7):
    os.environ["HF_TOKEN"] = os.getenv("HF_TOKEN")  # Optional but ensures it's set

    return HuggingFaceEndpoint(
        repo_id=model_id,
        max_new_tokens=max_new_tokens,
        temperature=temperature,
    )
    
# create llm
llm = get_llm()

#  prompts
prompt = f"""
    You are an AI assistant designed to support high school students in the subject of {SUBJECT}. 
    Your role is to offer friendly, helpful, concise, in-depth guidance, just like a supportive teacher would.
    
    Please follow these guidelines:
    
    1. Maintain a polite, respectful, and professional tone at all times.
    2. Adhere to ethical principles — do not promote cheating, harmful behavior, or misinformation.
    3. Interact in a warm, encouraging, and student-centered style — use clear explanations, positive reinforcement, and examples when needed.
    4. The word limit is 180 words.
"""

p_explanation = """
    5. Focus on thoroughly explaining the question by breaking down its components. Clarify the key concepts and definitions involved, ensuring that the explanation helps the reader fully understand what the question is asking. Avoid jumping to answers or examples; instead, concentrate on making the meaning and scope of the question clear.
    6. Do not include specific examples or real-world applications in your response.
"""
p_example = """
    5. Focus on providing three distinct examples that illustrate different aspects or variations of the question. Each example should highlight a unique approach or scenario related to the topic, helping to clarify the concept from multiple perspectives.
    6. Do not include any explanation or real-world applications in your response.
"""
p_application = """
    5. Provide two clear and relevant real-world applications related to the question or topic. Explain how each application connects to the concepts being discussed, demonstrating practical uses or implications.
    6. Do not include any explanation or examples in your response.
"""

# count the number of times "I don't know is clicked"
if "retry_count" not in st.session_state:
    st.session_state.retry_count = 0

    
# Initialize session state
if "help_clicks" not in st.session_state:
    st.session_state.help_clicks = 0
if "button_clicked" not in st.session_state:
    st.session_state.button_clicked = None

st.set_page_config(page_title="Interactive Help Interface", layout="centered")

st.markdown("## Sample Question Interface")
st.markdown("")
st.markdown(
    f"<p style='font-size:20px;'>{QUESTION}</p>",
    unsafe_allow_html=True
)
# Outer container for neat padding
with st.container():
    # Question area
    st.text_area(
        label="Type your response here.",
        value="",
        height=100,
        key="question_input",
    )

    st.markdown("")

    # Help Button Logic
    def toggle_help():
        st.session_state.help_clicks += 1
        st.session_state.button_clicked = None  # Reset help text on new toggle

    # Help Button (main toggle)
    col1, col2, col3 = st.columns([1, 3, 1])
    with col2:
        st.button("Help", on_click=toggle_help)

    # Show 3 sub-buttons if Help clicked an odd number of times
    if st.session_state.help_clicks % 2 == 1:
        st.markdown("### Need Help?")
        st.markdown("Choose an option below to better understand the question.")
        with st.container():
            st.markdown("---")  # Divider for clarity
            col1, col2, col3 = st.columns(3)

            with col1:
                if st.button("📝 Explain the question"):
                    if st.session_state.button_clicked != "Explain the question":
                        # First time clicked
                        full_prompt = (
                            "[INST]<<SYS>>\n"
                            f"{prompt + p_explanation}\n"
                            "<</SYS>>\n\n"
                            f"{QUESTION}\n"
                            "[/INST]"
                        )
                        st.session_state.response = llm.invoke([HumanMessage(content=full_prompt)])
                        st.session_state.retry_count = 0
                        st.session_state.full_prompt = full_prompt  # Save prompt for retry
                        st.session_state.button_clicked = "Explain the question"
            with col2:
                    if st.button("💡 Give an example"):
                        if st.session_state.button_clicked != "Give an example":
                            # First time clicked
                            full_prompt = (
                                "[INST]<<SYS>>\n"
                                f"{prompt + p_example}\n"
                                "<</SYS>>\n\n"
                                f"{QUESTION}\n"
                                "[/INST]"
                            )
                            st.session_state.response = llm.invoke([HumanMessage(content=full_prompt)])
                            st.session_state.retry_count = 0
                            st.session_state.full_prompt = full_prompt  # Save prompt for retry
                            st.session_state.button_clicked = "Give an example"
            with col3:
                if st.button("🤔 Who cares?"):
                        if st.session_state.button_clicked != "Who cares?":
                            # First time clicked
                            full_prompt = (
                                "[INST]<<SYS>>\n"
                                f"{prompt + p_application}\n"
                                "<</SYS>>\n\n"
                                f"{QUESTION}\n"
                                "[/INST]"
                            )
                            st.session_state.response = llm.invoke([HumanMessage(content=full_prompt)])
                            st.session_state.retry_count = 0
                            st.session_state.full_prompt = full_prompt  # Save prompt for retry
                            st.session_state.button_clicked = "Who cares?"
            st.markdown("---")

        # Display response text if a sub-button is clicked
        if st.session_state.button_clicked:
            with st.container():
                st.info(st.session_state.response)
                
                if st.session_state.button_clicked == "Explain the question":
                    col1, col2, col3 = st.columns([1, 1, 1])
                    with col2:
                        if st.button("I don't understand. Try again.", key="retry_button"):
                            st.session_state.retry_count += 1
                            alt_llm = get_llm(temperature=0.9)
                            st.session_state.response = alt_llm.invoke([HumanMessage(content=st.session_state.full_prompt)])
    if st.session_state.response:
        st.markdown("response")
        st.info(st.session_state.response)
# Optional: Add footer or spacing
st.markdown("<br><br>", unsafe_allow_html=True)

# css
st.markdown(
    """
    <style>
    div.stButton > button {
        width: 200px !important;
        height: 3em;
        font-size: 1.1rem;
        display: block;
        margin-left: auto;
        margin-right: auto;
    }
    </style>
    """,
    unsafe_allow_html=True,
)