app.py

############################################################################################################
# Importing Libraries

import streamlit as st
import hmac
import config
from openai import OpenAI
import matplotlib.pyplot as plt
import numpy as np

############################################################################################################
# Password protection

def check_password():
    """Returns `True` if the user had the correct password."""

    def password_entered():
        """Checks whether a password entered by the user is correct."""
        if hmac.compare_digest(st.session_state["password"], st.secrets["password"]):
            st.session_state["password_correct"] = True
            del st.session_state["password"]  # Don't store the password.
        else:
            st.session_state["password_correct"] = False

    if st.session_state.get("password_correct", False):
        return True

    st.text_input("Password", type="password", on_change=password_entered, key="password")
    if "password_correct" in st.session_state:
        st.error("😕 Password incorrect")
    return False

if not check_password():
    st.stop()

############################################################################################################
# Streamlit app layout

st.set_page_config(
    layout="wide", 
    page_title="Modular Chatbot + Growth Model",
    page_icon=":chart_with_upwards_trend:",
    initial_sidebar_state="expanded"
)

# Initial context for chatbot
initial_context = {
    "role": "system",
    "content": config.prompt
}

client = OpenAI(api_key=st.secrets["OPENAI_API_KEY"])

if "openai_model" not in st.session_state:
    st.session_state["openai_model"] = config.ai_model

if "display_messages" not in st.session_state:
    st.session_state["display_messages"] = [initial_context]

if "chat_initialized" not in st.session_state:
    st.session_state["chat_initialized"] = True
    st.session_state["display_messages"].append({"role": "assistant", "content": config.opening_message})

prompt = st.chat_input("Type your message here...")

if prompt:
    if not st.session_state["display_messages"]:
        st.session_state["display_messages"] = [initial_context]
    st.session_state["display_messages"].append({"role": "user", "content": prompt})

def reset_chat_history():
    st.session_state["display_messages"] = [initial_context]
    st.session_state["display_messages"].append({"role": "assistant", "content": config.opening_message})
    st.rerun()

# Main chat display
with st.container(border=False):
    for message in st.session_state["display_messages"][1:]:
        with st.chat_message(message["role"]):
            st.markdown(message["content"])

    if prompt:
        with st.chat_message("assistant"):
            try:
                stream = client.chat.completions.create(
                    model=st.session_state["openai_model"],
                    messages=[{"role": m["role"], "content": m["content"]} for m in st.session_state["display_messages"]],
                    stream=True,
                    temperature=config.temperature,
                    max_tokens=config.max_tokens,
                    frequency_penalty=config.frequency_penalty,
                    presence_penalty=config.presence_penalty,
                )
                full_response = ""
                message_placeholder = st.empty()
                for chunk in stream:
                    if chunk.choices[0].delta.content is not None:
                        full_response += chunk.choices[0].delta.content
                        message_placeholder.markdown(full_response + "▌")
                message_placeholder.markdown(full_response)
                st.session_state["display_messages"].append({"role": "assistant", "content": full_response})
            except Exception as e:
                st.error(f"An error occurred: {str(e)}")

############################################################################################################
# Sidebar and Growth Model Controls

with st.sidebar:
    st.markdown(config.warning_message, unsafe_allow_html=True)

    if st.button("Clear Chat History"):
        reset_chat_history()

    st.markdown("---")
    st.markdown("<small>Licensed under [GNU GPL v3.0](https://www.gnu.org/licenses/gpl-3.0.en.html)</small>", unsafe_allow_html=True)

    st.markdown("## 📈 Population Growth Model")
    with st.form("growth_model_form"):
        model_type = st.radio("Model type:", ["Exponential", "Logistic"])
        N0 = st.number_input("Initial population size (N₀):", min_value=0.0, value=10.0)
        r = st.number_input("Growth rate (r):", value=0.1)
        K = None
        if model_type == "Logistic":
            K = st.number_input("Carrying capacity (K):", min_value=1.0, value=100.0)
        t_max = st.number_input("Time span (t max):", min_value=1.0, value=50.0)
        submit_model = st.form_submit_button("Generate Growth Plot")

############################################################################################################
# Growth Model Plot

if submit_model:
    try:
        t = np.linspace(0, t_max, 500)
        if model_type == "Exponential":
            N = N0 * np.exp(r * t)
        elif model_type == "Logistic":
            N = K / (1 + ((K - N0) / N0) * np.exp(-r * t))
        else:
            raise ValueError("Invalid model type selected.")

        st.subheader("📊 Population Growth Plot")
        fig, ax = plt.subplots()
        ax.plot(t, N, label=model_type, color="green" if model_type == "Logistic" else "blue")
        ax.set_xlabel("Time")
        ax.set_ylabel("Population Size")
        ax.set_title(f"{model_type} Growth Model")
        ax.grid(True)
        ax.legend()
        st.pyplot(fig)

    except Exception as e:
        st.error(f"Error generating plot: {e}")