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import logging
import gradio as gr
import os
import zipfile
import pydub
import datetime
import openai
import jwt
from summarizer import count_tokens,main_summarizer_action_items,main_summarizer_meet
from decouple import config
DEBUG = True
API_KEY = config('API_KEY')
model_id = 'whisper-1'
SECRET_KEY = "$§%§$secret"
# Set the summarization parameters
# Set the maximum chunk size and tokens per chunk
max_chunk_size = 2000
max_tokens_per_chunk = 500
temperature = 0.7
top_p = 0.5
frequency_penalty = 0.5
temp_dir = os.path.join(os.path.dirname(__file__), 'temp')
title = description = article = "Meeting Summariser ⚡️ "
logger = logging.getLogger("Summariser")
logger.setLevel(logging.INFO)
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
formatter = logging.Formatter("%(asctime)s;%(levelname)s;%(message)s", "%Y-%m-%d %H:%M:%S")
ch.setFormatter(formatter)
logger.addHandler(ch)
def authentication(username, password):
if username == "admin" and password == "admin":
return True
def transcribe_audio(audio_file_path, temp_folder_path):
if DEBUG:
return "This is a test transcription"
max_size_bytes = 20 * 1024 * 1024 # 24 MB
if os.path.getsize(audio_file_path) <= max_size_bytes:
media_file = open(audio_file_path, 'rb')
response = openai.Audio.transcribe(
api_key=API_KEY,
model=model_id,
file=media_file
)
return response['text']
else:
sound = pydub.AudioSegment.from_file(audio_file_path, format="mp3")
chunks = pydub.utils.make_chunks(sound, max_size_bytes)
transcriptions = []
for i, chunk in enumerate(chunks):
print("chunk ", i)
chunk_path = os.path.join(temp_folder_path, f"audio_chunk_{i}.mp3")
chunk.export(chunk_path, format="mp3")
response = openai.Audio.transcribe(api_key=API_KEY,model=model_id,file=open(chunk_path, 'rb'))
transcriptions.append(response['text'])
return ' '.join(transcriptions)
def download_files(transcription: str, summary: str):
time_now = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
# Create transcription file
transcript_file_path = os.path.join(temp_dir, f'transcription_{time_now}.txt')
with open(transcript_file_path, 'w') as f:
f.write(transcription)
# Create summary file
summary_file_path = os.path.join(temp_dir, f'summary_{time_now}.txt')
with open(summary_file_path, 'w') as f:
f.write(summary)
# Create zip file
zip_file_path = os.path.join(temp_dir, 'download.zip')
with zipfile.ZipFile(zip_file_path, 'w', zipfile.ZIP_DEFLATED) as zip_file:
# Add transcription file to zip
zip_file.write(transcript_file_path, 'transcription.txt')
# Add summary file to zip
zip_file.write(summary_file_path, 'summary.txt')
return zip_file_path
def clean_trancript(text):
return text
def main_meet_summarizer(audio_file):
summary = ""
transcript = ""
action_items = ""
print("Starting Transcription")
transcript = transcribe_audio(audio_file,temp_dir)
print(f"Starting Summarization | {count_tokens(transcript)}")
cleaned_transcript = clean_trancript(transcript)
summary = main_summarizer_meet(cleaned_transcript, debug=DEBUG)
action_items = main_summarizer_action_items(cleaned_transcript, debug=DEBUG)
print("Finished Summarization")
return summary,transcript,download_files(transcription = transcript, summary = (summary + action_items))
summarizer_interface = gr.Interface(
fn=main_meet_summarizer,
inputs=[gr.inputs.Audio(source='upload', type='filepath', label='Audio File')],
outputs=[gr.outputs.Textbox(label='Summary'), gr.outputs.Textbox(label='Transcription'),gr.outputs.File(label="Download files here"),],
title='Summarizer',
description='Transcribe speech in an audio file & summarize it.',
)
summarizer_interface.launch(debug=True) |