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import streamlit as st
from mlx_lm import load
from mlx_lm.utils import generate_step
import mlx.core as mx
from tqdm import tqdm
import pennylane as qml
import numpy as np
import re
from textblob import TextBlob
from nltk.tokenize import word_tokenize
from nltk.tag import pos_tag
from collections import Counter

tqdm(disable=True, total=0)
title = "MLX Chat"
ver = "0.7.3"
debug = False

st.set_page_config(page_title=title, page_icon="🤖", layout="wide", initial_sidebar_state="expanded")
st.title(title)

assistant_greeting = "How may I help you?"
temp = 0.8
model_ref = st.sidebar.text_input("model", "mlx-community/Nous-Hermes-2-Mixtral-8x7B-DPO-4bit")
n_ctx = st.sidebar.number_input('context length', value=300, min_value=100, step=100, max_value=32000)
actions = st.sidebar.columns(2)

@st.cache_resource(show_spinner=True)
def load_model(ref):
    return load(ref)

model, tokenizer = load_model(model_ref)

@qml.qnode(qml.device("default.qubit", wires=4))
def quantum_circuit(color_code, amplitude):
    r, g, b = (int(color_code[i:i+2], 16) for i in (0, 2, 4))
    r, g, b = r / 255.0, g / 255.0, b / 255.0
    qml.RY(r * np.pi, wires=0)
    qml.RY(g * np.pi, wires=1)
    qml.RY(b * np.pi, wires=2)
    qml.RY(amplitude * np.pi, wires=3)
    qml.CNOT(wires=[0, 1])
    qml.CNOT(wires=[1, 2])
    qml.CNOT(wires=[2, 3])
    return qml.probs(wires=[0, 1, 2, 3])

async def sentiment_to_amplitude(text):
    analysis = TextBlob(text)
    return (analysis.sentiment.polarity + 1) / 2

def is_code_like(chunk):
    code_patterns = r'\b(def|class|import|if|else|for|while|return|function|var|let|const|print)\b|[\{\}\(\)=><\+\-\*/]'
    return bool(re.search(code_patterns, chunk))

def determine_token(chunk, memory, max_words_to_check=500):
    combined_chunk = f"{memory} {chunk}"
    if not combined_chunk:
        return "[attention]"
    if is_code_like(combined_chunk):
        return "[code]"
    words = word_tokenize(combined_chunk)[:max_words_to_check]
    tagged_words = pos_tag(words)
    pos_counts = Counter(tag[:2] for _, tag in tagged_words)
    most_common_pos, _ = pos_counts.most_common(1)[0]
    if most_common_pos == 'VB':
        return "[action]"
    elif most_common_pos == 'NN':
        return "[subject]"
    elif most_common_pos in ['JJ', 'RB']:
        return "[description]"
    else:
        return "[general]"

def generate(the_prompt, the_model):
    tokens = []
    skip = 0
    memory = ""
    for token, _ in zip(generate_step(mx.array(tokenizer.encode(the_prompt)), the_model, temp), range(n_ctx)):
        if token == tokenizer.eos_token_id:
            break
        tokens.append(token.item())
        s = tokenizer.decode(tokens)
        chunk = s[skip:]
        token_type = determine_token(chunk, memory)
        memory += f" {token_type} {chunk}"
        yield memory
        skip = len(s)

def show_chat(the_prompt, previous=""):
    with st.chat_message("assistant"):
        message_placeholder = st.empty()
        response = previous
        for chunk in generate(the_prompt, model):
            response = chunk.replace('�', '')
            message_placeholder.markdown(response + "▌")
        message_placeholder.markdown(response)
    st.session_state.messages.append({"role": "assistant", "content": response})

def remove_last_occurrence_in_array(array_of_dicts, criteria):
    for i in reversed(range(len(array_of_dicts))):
        if criteria(array_of_dicts[i]):
            del array_of_dicts[i]
            break

if "messages" not in st.session_state:
    st.session_state["messages"] = [{"role": "assistant", "content": assistant_greeting}]

for msg in st.session_state.messages:
    st.chat_message(msg["role"]).write(msg["content"])

if prompt := st.chat_input():
    st.session_state.messages.append({"role": "user", "content": prompt})
    st.chat_message("user").write(prompt)
    full_prompt = f"user\n{prompt}\n\nassistant\n"
    show_chat(full_prompt)

if st.session_state.messages and sum(msg["role"] == "assistant" for msg in st.session_state.messages) > 1:
    if actions[0].button("Reset", key='reset'):
        st.session_state.messages = [{"role": "assistant", "content": assistant_greeting}]
        st.rerun()

if st.session_state.messages and sum(msg["role"] == "assistant" for msg in st.session_state.messages) > 1:
    if actions[1].button("Continue", key='continue'):
        user_prompts = [msg["content"] for msg in st.session_state.messages if msg["role"] == "user"]
        last_prompt = user_prompts[-1] or "Please continue your response."
        assistant_responses = [msg["content"] for msg in st.session_state.messages if msg["role"] == "assistant"]
        remove_last_occurrence_in_array(st.session_state.messages, lambda item: item.get("role") == "assistant")
        last_assistant_response = assistant_responses[-1] if assistant_responses else ""
        last_assistant_response_lines = last_assistant_response.split('\n')
        if len(last_assistant_response_lines) > 1:
            last_assistant_response_lines.pop()
            last_assistant_response = "\n".join(last_assistant_response_lines)
        full_prompt = f"user\n{last_prompt}\nassistant\n{last_assistant_response}"
        show_chat(full_prompt, last_assistant_response)