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import streamlit as st
from deep_translator import GoogleTranslator
from gtts import gTTS
from pydub import AudioSegment
import tempfile
import os
import speech_recognition as sr
import css
from voice import transcribe
from transformers import pipeline as pl
# from speechbrain.pretrained import EncoderClassifier
# @st.cache_resource
# def load_emotion_model():
# return EncoderClassifier.from_hparams(
# source="emotion_model_local",
# savedir="tmp_emotion_model"
# )
# emotion_model = load_emotion_model()
# def detect_emotion(uploaded_file):
# # Save the uploaded file temporarily
# # Use a more robust way to handle the temporary file lifecycle
# with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
# tmp_file.write(uploaded_file.getvalue())
# raw_path = tmp_file.name
# try:
# audio = AudioSegment.from_file(raw_path)
# audio = audio.set_frame_rate(16000).set_channels(1)
# audio.export(raw_path, format="wav")
# # Predict emotion using the cleaned file
# # Ensure the path is passed as a standard string
# result = emotion_model.classify_file(str(raw_path))
# predicted_emotion = result[3][0]
# return predicted_emotion
# finally:
# # Clean up the temporary file
# if os.path.exists(raw_path):
# os.remove(raw_path)
def tone():
st.session_state.analyse=False
st.markdown('<div class="middle">', unsafe_allow_html=True)
with st.session_state.mid_col:
css.cicle_button()
if st.button("Translate"):
st.session_state.analyse=True
st.markdown('</div>', unsafe_allow_html=True)
with st.session_state.right_col:
if st.session_state.analyse:
if st.session_state.inp != "Text":
st.session_state.text = transcribe(st.session_state.uploaded_file)
st.write(" ")
st.write(" ")
st.write(" ")
with st.form("Tone_form"):
if st.session_state.text !="" and st.session_state.text != " ":
pipe = pl("text-classification", model="tabularisai/multilingual-sentiment-analysis")
sentence = st.session_state.text
result = pipe(sentence)[0]
sentiment = result['label']
if sentiment == "Very Negative":
st.error('This is Very Negative', icon="🚨")
elif sentiment == "Negative":
st.error('This is Negative', icon="😭")
elif sentiment == "Neutral":
st.warning('This is Neutral', icon="😐")
elif sentiment == "Positive":
st.success('This is Positive', icon="😊")
else:
st.success('This is Very Positive', icon="😃")
else:
st.warning("write something first")
reset = st.form_submit_button("Reset ↻ ")
if reset:
st.session_state.analyse= False
# if st.session_state.inp != "Text":
# text = transcribe(st.session_state.uploaded_file)
# if text !="" and text != " ":
# emotion = detect_emotion(st.session_state.uploaded_file)
# st.write(f"🎭 Detected Emotion: `{emotion}`") |