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

_ = load_dotenv(find_dotenv())
st.set_page_config(page_icon="📃", layout="wide", page_title="Groq & LLaMA3 Chat Bot...")


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("Groq Chat with LLaMA3 App", divider="rainbow", anchor=False)

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

# Layout for model selection and max_tokens slider
col1, col2 = st.columns([1, 3])  # Adjust the ratio to make the first column smaller


with col1:
    model_option = st.selectbox(
        "Choose a model:",
        options=list(models.keys()),
        format_func=lambda x: models[x]["name"],
        index=0,  # Default to the first model in the list
    )
    max_tokens_range = models[model_option]["tokens"]
    max_tokens = st.slider(
        "Max Tokens:",
        min_value=512,
        max_value=max_tokens_range,
        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}",
    )
    system_message = {}
    if system_prompt := st.text_input("System Prompt"):
        system_message = {"role": "system", "content": system_prompt}

# 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

# Add a "Clear Chat" button
if st.button("Clear Chat"):
    st.session_state.messages = []
    
# 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)

    messages=[
        {"role": m["role"], "content": m["content"]}
        for m in st.session_state.messages]
    if system_message:
        messages.insert(0,system_message)
    # Fetch response from Groq API
    try:
        chat_completion = client.chat.completions.create(
            model=model_option,
            messages=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}
        )