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import streamlit as st
from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
import speech_recognition as sr
from gtts import gTTS
from pydub import AudioSegment
import os
import tempfile
# Load Translation Model
@st.cache_resource
def load_m2m_model():
model_name = "facebook/m2m100_418M"
model = M2M100ForConditionalGeneration.from_pretrained(model_name)
tokenizer = M2M100Tokenizer.from_pretrained(model_name)
return model, tokenizer
model, tokenizer = load_m2m_model()
# Language Mapping
language_codes = {
"English": "en",
"Hindi": "hi",
"Marathi": "mr",
"Chinese": "zh",
"Japanese": "ja",
"Spanish": "es",
"Korean": "ko"
}
# Streamlit App
st.markdown("<h1 style='text-align: center;'>🔄 PolyglotPal 🔄</h1>", unsafe_allow_html=True)
st.markdown("<h5 style='text-align: center;'>Your friendly multilingual assistant</h5>", unsafe_allow_html=True)
st.sidebar.header("Language Translation Options")
# Input Language
input_language = st.sidebar.selectbox("Select Input Language", list(language_codes.keys()))
# Target Language
target_language = st.sidebar.selectbox(
"Select Target Language",
[lang for lang in language_codes.keys() if lang != input_language]
)
# Input Method
input_method = st.radio("Select Input Method", ["Text Input", "Speech Input"])
if input_method == "Speech Input":
st.subheader("Upload Audio File for Translation")
audio_file = st.file_uploader("Upload an MP3 file", type=["mp3"])
# Text Input or Speech-to-Text
input_text = ""
if input_method == "Text Input":
st.subheader(f"Input Text ({input_language})")
input_text = st.text_area("Enter text to translate:", height=150)
elif input_method == "Speech Input" and audio_file:
# Process MP3 to WAV
try:
# Convert MP3 to WAV using pydub
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_wav_file:
audio = AudioSegment.from_file(audio_file, format="mp3")
audio.export(tmp_wav_file.name, format="wav")
# Perform Speech-to-Text
recognizer = sr.Recognizer()
with sr.AudioFile(tmp_wav_file.name) as source:
audio_data = recognizer.record(source)
input_text = recognizer.recognize_google(audio_data, language=language_codes[input_language])
st.success(f"Recognized Speech: {input_text}")
except Exception as e:
st.error(f"Error processing audio: {e}")
if st.button("Translate"):
if input_text.strip():
try:
# Set source and target languages
tokenizer.src_lang = language_codes[input_language]
encoded_input = tokenizer(input_text, return_tensors="pt")
# Generate translation
generated_tokens = model.generate(
**encoded_input,
forced_bos_token_id=tokenizer.lang_code_to_id[language_codes[target_language]]
)
translated_text = tokenizer.decode(generated_tokens[0], skip_special_tokens=True)
# Display Translation
st.subheader(f"Translated Text ({target_language})")
st.text_area("Translation Result:", value=translated_text, height=150, disabled=True)
# Text-to-Speech
# st.subheader("Text-to-Speech Output")
# tts = gTTS(translated_text, lang=language_codes[target_language])
# tts.save("translated_audio.mp3")
# st.audio("translated_audio.mp3", format="audio/mp3")
except:
#st.error(f"Translation error: {e}")
pass
else:
st.error("Please provide text or speech input for translation.")
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