import os
import base64

import numpy as np
import streamlit as st
import streamlit.components.v1 as components
from streamlit_mic_recorder import mic_recorder

from utils import load_model, generate_response, bytes_to_array, start_server

def home_page():
    ## Set up home page Title
    col1, col2 = st.columns([1, 4])
    custom_html = """
    <div class="banner">
        <img src="https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRhB2e_AhOe11wKxnnwOmOVg9E7J1MBgiTeYzzFAESwcCP5IbBAc2X8BwGChMfJzwqtVg&usqp=CAU" alt="Banner Image">
    </div>
    <style>
        .banner {
            width: 100%;
            height: 200px;
            overflow: visible;
        }
        .banner img {
            width: 100%;
            object-fit: cover;
        }
    </style>
    """
    with col1:
        components.html(custom_html)
    with col2: 
        st.write("# Welcome to MERaLiON - AudioLLMs 🤖")

    ## Set up home page other information
    st.markdown('')


def audio_llm():    
    with st.sidebar:
        st.divider()
        st.markdown("""<div class="sidebar-intro">
                    <p><strong>Purpose</strong>: Complex Audio Understanding</p>
                    <p><strong>Name</strong>: MERaLiON-AudioLLM-v1</p>
                    <p><strong>Version</strong>: Dec. 20, 2024</p>
                    </div>""", unsafe_allow_html=True)
    
    
    if st.sidebar.button('Clear History'):
        st.session_state.update(messages=[], 
                                on_upload=False, 
                                on_record=False, 
                                on_select=False, 
                                audio_array=np.array([]))   

    if "server" not in st.session_state:
        st.session_state.server = start_server()
    
    if "client" not in st.session_state or 'model_name' not in st.session_state:
        st.session_state.client, st.session_state.model_name = load_model()

    if "audio_array" not in st.session_state:
        st.session_state.audio_base64 = ''
        st.session_state.audio_array = np.array([])
    
    if "default_instruction" not in st.session_state: 
        st.session_state.default_instruction = ""

    st.markdown("**Step1: Specify Model Configuration**")
    col4, col5 = st.columns(2)

    with col4:
        st.slider(label='Temperature', min_value=0.0, max_value=2.0, value=0.7, key='temperature')

    with col5:
        st.slider(label='Top P', min_value=0.0, max_value=1.0, value=1.0, key='top_p')

    st.markdown("**Step2: Specify Audio**")
    col1, col2, col3 = st.columns(3)
    
    with col2:
        st.markdown("Record Audio:")
        
        recording = mic_recorder(
            format="wav", 
            use_container_width=True, 
            callback=lambda: st.session_state.update(on_record=True, messages=[]),
            key='record')
        
        if recording and st.session_state.on_record:
            audio_bytes = recording["bytes"]
            st.session_state.audio_base64 = base64.b64encode(audio_bytes).decode('utf-8')
            st.session_state.audio_array = bytes_to_array(audio_bytes)
    
    with col3:
        st.markdown("Upload Audio:")

        uploaded_file = st.file_uploader(
            label="**Upload Audio:**", 
            label_visibility="collapsed",
            type=['wav', 'mp3'],
            on_change=lambda: st.session_state.update(on_upload=True, messages=[]),
            key='upload'
        )
        
        if uploaded_file and st.session_state.on_upload:
            audio_bytes = uploaded_file.read()
            st.session_state.audio_base64 = base64.b64encode(audio_bytes).decode('utf-8')
            st.session_state.audio_array = bytes_to_array(audio_bytes)

    with col1:
        audio_samples_w_instruct = {
            '1_ASR_IMDA_PART1_ASR_v2_141' : "- Turn the spoken language into a text format.\n\n- Please translate the content into Chinese.",
            '2_ASR_IMDA_PART1_ASR_v2_2258': "- Turn the spoken language into a text format.\n\n- Please translate the content into Chinese.",
            '3_ASR_IMDA_PART1_ASR_v2_2265': "- Turn the spoken language into a text format.",
                    
            '4_ASR_IMDA_PART2_ASR_v2_999' : "- Translate the spoken words into text format.",
            '5_ASR_IMDA_PART2_ASR_v2_2241': "- Translate the spoken words into text format.",
            '6_ASR_IMDA_PART2_ASR_v2_3409': "- Translate the spoken words into text format.",
    
            '7_ASR_IMDA_PART3_30_ASR_v2_2269': "- Need this talk written down, please.",
            '8_ASR_IMDA_PART3_30_ASR_v2_1698': "- Need this talk written down, please.",
            '9_ASR_IMDA_PART3_30_ASR_v2_2474': "- Need this talk written down, please.",

            '10_ASR_IMDA_PART4_30_ASR_v2_1527': "- Write out the dialogue as text.",
            '11_ASR_IMDA_PART4_30_ASR_v2_3771': "- Write out the dialogue as text.",
            '12_ASR_IMDA_PART4_30_ASR_v2_103' : "- Write out the dialogue as text.",
    
            '13_ASR_IMDA_PART5_30_ASR_v2_1446': "- Translate this vocal recording into a textual format.",
            '14_ASR_IMDA_PART5_30_ASR_v2_2281': "- Translate this vocal recording into a textual format.",
            '15_ASR_IMDA_PART5_30_ASR_v2_4388': "- Translate this vocal recording into a textual format.",

            '16_ASR_IMDA_PART6_30_ASR_v2_576': "- Record the spoken word in text form.",
            '17_ASR_IMDA_PART6_30_ASR_v2_1413': "- Record the spoken word in text form.",
            '18_ASR_IMDA_PART6_30_ASR_v2_2834': "- Record the spoken word in text form.",

            '19_ASR_AIShell_zh_ASR_v2_5044': "- Transform the oral presentation into a text document.",

            '20_ASR_LIBRISPEECH_CLEAN_ASR_V2_833': "- Please provide a written transcription of the speech."
        }
        
        audio_sample_names = [audio_sample_name for audio_sample_name in audio_samples_w_instruct.keys()]
       
        st.markdown("Select From Examples:")
       
        sample_name = st.selectbox(
            label="**Select Audio:**",
            label_visibility="collapsed",
            options=audio_sample_names,
            index=None,
            placeholder="Select an audio sample:",
            on_change=lambda: st.session_state.update(on_select=True, messages=[]),
            key='select')
       
        if sample_name and st.session_state.on_select:
            audio_bytes = open(f"audio_samples/{sample_name}.wav", "rb").read()
            st.session_state.default_instruction = audio_samples_w_instruct[sample_name]
            st.session_state.audio_base64 = base64.b64encode(audio_bytes).decode('utf-8')
            st.session_state.audio_array = bytes_to_array(audio_bytes)
    
    st.audio(st.session_state.audio_array, format="audio/wav", sample_rate=16000)
    st.session_state.update(on_upload=False, on_record=False, on_select=False)

    st.markdown("**Step 3: Input your Instruction**")
    if st.session_state.default_instruction:
        st.write("Example Instructions:")
        st.write(st.session_state.default_instruction)

    st.markdown(
        """
        <style>
            .st-emotion-cache-1c7y2kd {
                flex-direction: row-reverse;
                text-align: right;
            }
        </style>
    
        """,
        unsafe_allow_html=True,
    )

    if "messages" not in st.session_state:
        st.session_state.messages = []
    
    if prompt := st.chat_input(placeholder="Your Instruction"):
        with st.chat_message("user"):
            st.write(prompt)
        st.session_state.messages.append({"role": "user", "content": prompt})
    
        with st.chat_message("assistant"):
            with st.spinner("Thinking..."):
                try:
                    stream = generate_response(prompt)
                    response = st.write_stream(stream)
                except Exception as e:
                    response = f"Caught Exception: {repr(e)}. Please contact the administrator to restart this space."
                    st.write(response)
                    raise(e)
                st.session_state.messages.append({"role": "assistant", "content": response})