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app.py
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app.py
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from dotenv import load_dotenv
import whisper
import gradio as gr
import ollama
import logging
from logger import logger
import openai
# Load environment variables
load_dotenv()
# Import configurations and functions from modules
from config import openai_api_key, model_id, model_path
from load_model import load_model
#from extract_entities import extract_entities
openai.api_key = openai_api_key
#Load whisher model
model = load_model(model_id, model_path)
#transcripe the audio to its original language
def process_all_steps(audio):
#transcription =transcribe(audio)
translation = translate_with_whisper(audio)
#translation = translate_with_ollama(transcription)
#summary = summarize_using_llama(translation)
summary = summarize_using_openai(translation)
#return [transcription, translation, summary]
return [translation, summary]
def transcribe(audio):
logger.info("Started transciption")
result = model.transcribe(audio,fp16=False)
transcription = result["text"]
return transcription
#translate the audio file to English language using whisper model
def translate_with_whisper(audio):
logger.info("Started transciption through whishper")
options = dict(beam_size=5, best_of=5)
translate_options = dict(task="translate", **options)
result = model.transcribe(audio,**translate_options)
return result["text"]
#translate the text from transciption to English language
def translate_with_ollama(text):
logger.info("Started transciption through llama")
response = ollama.generate(model= "llama3.1", prompt = "Translate the following text to English:"+text+"\n SUMMARY:\n")
translation = response["response"]
return translation
#Using Ollama and llama3.1 modle, summarize the English translation
def summarize_using_llama(text):
response = ollama.generate(model= "llama3.1", prompt = "Provide the summary wiht bullet points:"+text+"\n SUMMARY:\n")
summary = response["response"]
return summary
#Using openaie, summarize the English translation
def summarize_using_openai(text):
logger.info("Started summarization")
prompt = "Summarize the following text: " +text
try:
response = openai.chat.completions.create(
model="gpt-4o",
messages=[
{"role": "system", "content": "You are a helpful assistant that extracts information from Indian multilingual text."},
{"role": "user", "content": prompt}
],
max_tokens=500
)
summary = response.choices[0].message.content
except Exception as e:
logger.error(e)
summary = "Unable to exract summary"
return summary
#UI with tabs,
theme = gr.themes.Glass(spacing_size="lg", radius_size="lg",primary_hue="blue", font=["Optima","Candara"])
with gr.Blocks(theme, title="Voice Summarization") as block:
#Tab for recording the audio and upload it for transription, translation and summarization
with gr.Tab("Record"):
with gr.Row():
inp_audio = gr.Audio(
label="Input Video",
type="filepath",
sources = ["microphone"],
elem_classes=["primary"]
)
with gr.Row():
#out_transcribe = gr.TextArea(label="Transcipt")
out_translate = gr.TextArea(label="Translate")
with gr.Row():
out_summary = gr.TextArea(label="Call Summary")
with gr.Row():
submit_btn = gr.Button("Submit")
#submit_btn.click(transcribe, inputs=[inp_audio], outputs=[out_transcribe,out_translate, out_summary])
submit_btn.click(process_all_steps, inputs=[inp_audio], outputs=[out_translate, out_summary])
#Tab for uploading the audio file for transription, translation and summarization
with gr.Tab("Upload"):
with gr.Row():
inp_audio_file = gr.File(
label="Upload Audio File",
type="filepath",
file_types=["m4a","mp3","webm","mp4","mpga","wav","mpeg"],
)
with gr.Row():
out_transcribe = gr.TextArea(label="Transcipt")
out_translate = gr.TextArea(label="Translate")
with gr.Row():
out_summary = gr.TextArea(label="Call Summary")
with gr.Row():
submit_btn = gr.Button("Submit")
submit_btn.click(transcribe, inputs=[inp_audio_file], outputs=[out_transcribe,out_translate, out_summary])
block.launch(debug = True)