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import streamlit as st
import tempfile
import shutil
import psutil
import torch
import torchaudio
from utils.audio_processing import trim_audio, download_audio_as_wav
from utils.video_processing import trim_video
from models.model_loader import load_accent_model, load_whisper
from utils.accent_analysis import analyze_accent
from utils.session_utils import initialize_session_state, display_memory_once, reset_session_state_except_model
from models.custom_interface import CustomEncoderWav2vec2Classifier
st.title("🎙️ English Accent Audio Detector")
# Initialize session state
initialize_session_state()
# Load models once
if 'classifier' not in st.session_state:
st.session_state.classifier = load_accent_model()
if 'whisper' not in st.session_state:
st.session_state.whisper = load_whisper()
# Memory info
display_memory_once()
# Reset state for a new analysis
if st.button("🔄 Analyze new video"):
reset_session_state_except_model()
st.rerun()
# Check for ffmpeg
if not shutil.which("ffmpeg"):
raise EnvironmentError("FFmpeg not found. Please install or add it to PATH.")
# Input options
option = st.radio("Choose input method:", ["Upload video file", "Enter Video Url"])
if option == "Upload video file":
uploaded_video = st.file_uploader("Upload your video", type=["mp4", "mov", "avi", "mkv"])
if uploaded_video is not None:
temp_video_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
with open(temp_video_path.name, "wb") as f:
f.write(uploaded_video.read())
audio_path = trim_video(temp_video_path.name)
st.success("✅ Video uploaded successfully.")
st.session_state.audio_path = audio_path
elif option == "Enter Video Url":
yt_url = st.text_input("Paste YouTube URL")
if st.button("Download Video"):
with st.spinner("Downloading video..."):
audio_path = download_audio_as_wav(yt_url)
audio_path = trim_audio(audio_path)
if audio_path:
st.success("✅ Video downloaded successfully.")
st.session_state.audio_path = audio_path
# Transcription and Accent Analysis
if st.session_state.audio_path and not st.session_state.transcription:
if st.button("🎧 Extract Audio"):
st.session_state.audio_ready = True
st.audio(st.session_state.audio_path, format='audio/wav')
mem = psutil.virtual_memory()
st.write(f"🔍 Memory used: {mem.percent}%")
#Detect Language AND FILTER OUT NON-ENGLISH AUDIOS FOR ANALYSIS
segments, info = st.session_state.whisper.transcribe(st.session_state.audio_path, beam_size=1)
# Convert segments (generator) to full transcription string
st.session_state.transcription = " ".join([segment.text for segment in segments])
if info.language != "en":
st.error("❌ This video does not appear to be in English. Please provide a clear English video.")
else:
# Show transcription for audio
with st.spinner("Transcribing audio..."):
st.markdown(" Transcript Preview")
st.markdown(st.session_state.transcription)
st.success("🎵 Audio extracted and ready for analysis!")
mem = psutil.virtual_memory()
st.write(f"🔍 Memory used: {mem.percent}%")
if st.session_state.transcription:
if st.button("🗣️ Analyze Accent"):
with st.spinner("🔍 Analyzing accent..."):
try:
mem = psutil.virtual_memory()
st.write(f"🔍 Memory used: {mem.percent}%")
waveform, sample_rate = torchaudio.load(st.session_state.audio_path)
readable_accent, confidence = analyze_accent(waveform, sample_rate, st.session_state.classifier)
if readable_accent:
st.success(f"✅ Accent Detected: **{readable_accent}**")
st.info(f"📊 Confidence: {confidence}%")
else:
st.warning("Could not determine accent.")
except Exception as e:
st.error("❌ Failed to analyze accent.")
st.code(str(e))
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