app.py

import streamlit as st
import subprocess

def run_makefile():
    result = subprocess.run(["make"], capture_output=True, text=True)
    if result.returncode == 0:
        st.success(f"Makefile executed successfully:\n{result.stdout}")
    else:
        st.error(f"Makefile execution failed:\n{result.stderr}")

st.title("Run Makefile")

if st.button("Run Makefile"):
    run_makefile()

'''import streamlit as st
import time
import soundfile as sf
from whisper_processor import process_audio

def process_audio_streamlit(audio_file, model_name, lang):
  start_time = time.time()

  # Read audio data
  audio, sample_rate = sf.read(audio_file)

  # Check if conversion is necessary
  if sample_rate != 16000:
    # Resample to 16kHz
    audio = sf.resample(audio, sample_rate, 16000)
  
  # Save the resampled audio (optional)
  # sf.write("temp_resampled.wav", audio, 16000)

  # Process the audio with Whisper
  result = process_audio(audio, model_name=model_name, lang=lang)

  end_time = time.time()
  elapsed_time = end_time - start_time
  st.write("Time taken:", elapsed_time, "seconds")
  return result

st.title("Audio Transcription")

# Upload audio file
uploaded_file = st.file_uploader("Choose an audio file")

# Select model and language
model_name = st.selectbox("Select model", ["tiny", "base", "small", "medium", "large"])
lang = st.selectbox("Select language", ["en", "hi", "fr", "de", "es", "it", "pt", "ru", "zh", "ja", "ko", "ar", "tr"])

if uploaded_file is not None:
  # Save the uploaded file to a temporary location
  with open("temp.wav", "wb") as f:
    f.write(uploaded_file.read())

  # Process the audio file
  result = process_audio_streamlit("temp.wav", model_name, lang)

  # Display the transcription result
  st.write("Transcription:")
  st.text_area("", value=result, height=300)'''