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import streamlit as st
import pandas as pd
from CallCenter import get_included_files, convertMp3ToWav, diarize_wav_file
import os
from segment_wave_files import segment_wave_files
from transcript_analysis import transcript_analysis
from transcribe_files import transcribe_segments
location, wave_files = get_included_files()
def main():
# --- Streamlit App ---
st.title("Call Center Analysis")
# --- Selectable Elements ---
def get_file_sel():
if selected_file is None:
return
st.session_state.selected_file = location+selected_file # Store the selected option in session state
st.write(f"{selected_file} is selected") # Display feedback
st.session_state.wave_file = st.session_state.selected_file
selected_file = st.selectbox("Select an element:", wave_files)
get_file_sel()
# --- File Upload ---
uploaded_file = st.file_uploader("Upload a file", type=['mp3', 'wav'])
if uploaded_file:
st.session_state.uploaded_file = uploaded_file.name
if st.session_state.uploaded_file is not None:
if os.path.splitext(uploaded_file.name)[1].lower()==".mp3":
bytes_data = uploaded_file.read() # read the content of the file in binary
if not os.path.exists("/tmp"):
os.makedirs("/tmp")
with open(os.path.join("/tmp", uploaded_file.name), "wb") as f:
f.write(bytes_data) # write this content elsewhere
wav_file = convertMp3ToWav(f"/tmp/{uploaded_file.name}")
st.session_state.wave_file = wav_file # Store the selected option in session state
else:
bytes_data = uploaded_file.read() # read the content of the file in binary
with open(os.path.join("/tmp", uploaded_file.name), "wb") as f:
f.write(bytes_data) # write this content elsewhere
st.session_state.wave_file = os.path.join("/tmp", uploaded_file.name) # Store the selected option in session state
st.write(f"You uploaded: {uploaded_file.name}") # Display feedback
uploaded_file = None
analysis = ""
# --- Start Button ---
if st.button("Start"):
if st.session_state.wave_file is not None:
with st.spinner("Please wait..."):
try:
speakers = diarize_wav_file(st.session_state.wave_file )
speakers = segment_wave_files(speakers, st.session_state.wave_file )
transcripts = transcribe_segments(speakers)
analysis = transcript_analysis(transcripts)
except Exception as e:
st.error(f"Error processing file: {st.session_state.wave_file}")
else:
st.warning("Please upload a file.")
# --- Reset Button ---
if st.button("Reset"):
st.experimental_rerun()
# --- Large Text Box (Display Only) ---
if analysis != "":
analysis = analysis[9:]
index=analysis.lower().find("sentiment")
summary=analysis[0:index].lstrip("\n")
sentiment = analysis[index:]
index=sentiment.lower().find(":")
sentiment = sentiment[index+1:].lstrip("\n")
height = 34*15
st.text_area("SUMMARY:", value=summary, disabled=True, height=height)
st.text_area("SENTIMENT:", value=sentiment, disabled=True, height=height)
main() |