app.py

import os
from dotenv import find_dotenv, load_dotenv
import streamlit as st
from typing import Generator
from groq import Groq
import datetime
import json

_ = load_dotenv(find_dotenv())
st.set_page_config(page_icon="💬", layout="wide", page_title="...")

def icon(emoji: str):
    """Shows an emoji as a Notion-style page icon."""
    st.write(
        f'<span style="font-size: 78px; line-height: 1">{emoji}</span>',
        unsafe_allow_html=True,
    )

icon("⚡")

st.subheader("Chatbot", divider="rainbow", anchor=False)

# LOGIN
password = os.environ['PASSWORD']

# Create a login form container
login_container = st.container()

def login(password_input):
    if password_input == password:
        st.session_state.authenticated = True
        login_container.empty() 
    else:
        st.error("Invalid password")

with login_container:
    password_input = st.text_input("Password", type="password")
    st.button("authenticate", on_click=login(password_input))

# Only allow access to the app if the user is authenticated
if "authenticated" not in st.session_state or not st.session_state.authenticated:
    st.stop()  # Stop the app from running if the user is not authenticated
else:
    login_container.empty()

client = Groq(
    api_key=os.environ['GROQ_API_KEY'],
)

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

if "selected_model" not in st.session_state:
    st.session_state.selected_model = None

# Define model details
models = {
    "mixtral-8x7b-32768": {
        "name": "Mixtral-8x7b-Instruct-v0.1",
        "tokens": 32768,
        "developer": "Mistral",
    },
    "gemma-7b-it": {"name": "Gemma-7b-it", "tokens": 8192, "developer": "Google"},
    "llama2-70b-4096": {"name": "LLaMA2-70b-chat", "tokens": 4096, "developer": "Meta"},
    "llama3-70b-8192": {"name": "LLaMA3-70b-8192", "tokens": 8192, "developer": "Meta"},
    "llama3-8b-8192": {"name": "LLaMA3-8b-8192", "tokens": 8192, "developer": "Meta"},
}

# Layout for model selection and max_tokens slider
col1, col2 = st.columns(2)

with col1:
    model_option = st.selectbox(
        "Choose a model:",
        options=list(models.keys()),
        format_func=lambda x: x,
        index=0,  # Default to the first model in the list
    )

# Detect model change and clear chat history if model has changed
if st.session_state.selected_model != model_option:
    st.session_state.messages = []
    st.session_state.selected_model = model_option

max_tokens_range = models[model_option]["tokens"]

with col2:
    # Adjust max_tokens slider dynamically based on the selected model
    max_tokens = st.slider(
        "Max Tokens:",
        min_value=512,  # Minimum value to allow some flexibility
        max_value=max_tokens_range,
        # Default value or max allowed if less
        value=min(32768, max_tokens_range),
        step=512,
        help=f"Adjust the maximum number of tokens (words) for the model's response. Max for selected model: {max_tokens_range}",
    )
    

# Display chat messages from history on app rerun
for message in st.session_state.messages:
    avatar = "🧠" if message["role"] == "assistant" else "❓"
    with st.chat_message(message["role"], avatar=avatar):
        st.markdown(message["content"])

def generate_chat_responses(chat_completion) -> Generator[str, None, None]:
    """Yield chat response content from the Groq API response."""
    for chunk in chat_completion:
        if chunk.choices[0].delta.content:
            yield chunk.choices[0].delta.content

if prompt := st.chat_input("Enter your prompt here..."):
    st.session_state.messages.append({"role": "user", "content": prompt})

    with st.chat_message("user", avatar="❓"):  
        st.markdown(prompt)

    # Fetch response from Groq API
    try:
        chat_completion = client.chat.completions.create(
            model=model_option,
            messages=[
                {"role": m["role"], "content": m["content"]}
                for m in st.session_state.messages
            ],
            max_tokens=max_tokens,
            stream=True,
        )

        # Use the generator function with st.write_stream
        with st.chat_message("assistant", avatar="🧠"):
            chat_responses_generator = generate_chat_responses(chat_completion)
            full_response = st.write_stream(chat_responses_generator)
    except Exception as e:
        st.error(e, icon="🚨")

    # Append the full response to session_state.messages
    if isinstance(full_response, str):
        st.session_state.messages.append(
            {"role": "assistant", "content": full_response}
        )
    else:
        # Handle the case where full_response is not a string
        combined_response = "\n".join(str(item) for item in full_response)
        st.session_state.messages.append(
            {"role": "assistant", "content": combined_response}
        )