import streamlit as st
import os
from pydub import AudioSegment
from pydub.silence import split_on_silence
from dotenv import load_dotenv
from tempfile import NamedTemporaryFile
import math
from docx import Document
import whisper

# Load environment variables from .env file (if needed for other configurations)
load_dotenv()

@st.cache_resource
def load_whisper_model():
    """
    Load the Whisper model once and cache it for future use.
    You can choose the model size: "tiny", "base", "small", "medium", or "large".
    """
    model = whisper.load_model("base")
    return model

# Load the Whisper model globally so it’s only loaded once.
model = load_whisper_model()

def split_audio_on_silence(audio_file_path, min_silence_len=500, silence_thresh=-40, keep_silence=250):
    """
    Split an audio file into chunks using silence detection.
    
    Args:
        audio_file_path (str): Path to the audio file.
        min_silence_len (int): Minimum length of silence (in ms) required for a split.
        silence_thresh (int): The volume (in dBFS) below which is considered silence.
        keep_silence (int): Amount of silence (in ms) to retain at the beginning and end of each chunk.
    
    Returns:
        list: List of AudioSegment chunks.
    """
    audio = AudioSegment.from_file(audio_file_path)
    chunks = split_on_silence(
        audio, 
        min_silence_len=min_silence_len,
        silence_thresh=silence_thresh,
        keep_silence=keep_silence
    )
    return chunks

def transcribe(audio_file):
    """
    Transcribe an audio file using the locally loaded Whisper model.
    
    Args:
        audio_file (str): Path to the audio file.
    
    Returns:
        str: Transcribed text.
    """
    result = model.transcribe(audio_file, language="en")
    return result["text"]

def process_audio_chunks(audio_chunks):
    """
    Process and transcribe each audio chunk in sequence.
    
    Args:
        audio_chunks (list): List of AudioSegment chunks.
    
    Returns:
        str: Combined transcription from all chunks.
    """
    transcriptions = []
    min_length_ms = 100  # Minimum length required for processing
    for i, chunk in enumerate(audio_chunks):
        if len(chunk) < min_length_ms:
            st.warning(f"Chunk {i} is too short to be processed.")
            continue
        
        # Save the chunk temporarily as a WAV file
        with NamedTemporaryFile(delete=False, suffix=".wav") as temp_audio_file:
            chunk.export(temp_audio_file.name, format="wav")
            temp_audio_file_path = temp_audio_file.name

        transcription = transcribe(temp_audio_file_path)
        if transcription:
            transcriptions.append(transcription)
            st.write(f"Transcription for chunk {i}: {transcription}")

        os.remove(temp_audio_file_path)
    return " ".join(transcriptions)

def save_transcription_to_docx(transcription, audio_file_path):
    """
    Save the transcription as a .docx file.
    
    Args:
        transcription (str): Transcribed text.
        audio_file_path (str): Path to the original audio file for naming purposes.
    
    Returns:
        str: Path to the saved .docx file.
    """
    base_name = os.path.splitext(os.path.basename(audio_file_path))[0]
    output_file_name = f"{base_name}_full_transcription.docx"
    doc = Document()
    doc.add_paragraph(transcription)
    doc.save(output_file_name)
    return output_file_name

st.title("Audio Transcription with Whisper (Local)")

# Allow uploading of audio or video files
uploaded_file = st.file_uploader("Upload an audio or video file", type=["wav", "mp3", "ogg", "m4a", "mp4", "mov"])

if 'transcription' not in st.session_state:
    st.session_state.transcription = None

if uploaded_file is not None and st.session_state.transcription is None:
    st.audio(uploaded_file)
    
    # Save uploaded file temporarily
    file_extension = uploaded_file.name.split(".")[-1]
    temp_audio_file = f"temp_audio_file.{file_extension}"
    with open(temp_audio_file, "wb") as f:
        f.write(uploaded_file.getbuffer())

    # Split and process audio using silence detection
    with st.spinner('Transcribing...'):
        audio_chunks = split_audio_on_silence(
            temp_audio_file, 
            min_silence_len=500,   # adjust based on your audio
            silence_thresh=-40,    # adjust based on ambient noise level
            keep_silence=250       # retains a bit of silence at the edges
        )
        transcription = process_audio_chunks(audio_chunks)
        if transcription:
            st.session_state.transcription = transcription
            st.success('Transcription complete!')
            output_docx_file = save_transcription_to_docx(transcription, uploaded_file.name)
            st.session_state.output_docx_file = output_docx_file
            
    if os.path.exists(temp_audio_file):
        os.remove(temp_audio_file)

if st.session_state.transcription:
    st.text_area("Transcription", st.session_state.transcription, key="transcription_area_final")
    with open(st.session_state.output_docx_file, "rb") as docx_file:
        st.download_button(
            label="Download Transcription (.docx)",
            data=docx_file,
            file_name=st.session_state.output_docx_file,
            mime='application/vnd.openxmlformats-officedocument.wordprocessingml.document'
        )