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import streamlit as st
import os
import shutil
import time
from test_video_url_download import download_video
from test_audio_extraction import extract_audio
from noise_reduce import denoise_audio
from accent_analyzer import predict_accent
# Constants
download_dir = "downloads"
audio_dir = "audio"
reduced_dir = os.path.join(audio_dir, "reduced")
# Ensure output folders exist
os.makedirs(download_dir, exist_ok=True)
os.makedirs(audio_dir, exist_ok=True)
os.makedirs(reduced_dir, exist_ok=True)
st.set_page_config(page_title="Accent Analyzer", page_icon="🎤", layout="centered")
st.title("🎧 English Accent Analyzer")
st.markdown("""
Paste a **YouTube video URL** or a direct video link below. This tool will:
1. Download the video
2. Extract the speaker's audio
3. Optionally apply noise reduction
4. Analyze and classify the English accent
""")
# Sidebar options
with st.sidebar:
st.header("🛠 Audio Processing")
enable_denoise = st.checkbox("🎛 Enable Noise Canceling", value=True)
st.markdown("---")
st.info("This feature enhances clarity if the audio has background noise.")
# Main form
video_url = st.text_input("🔗 Paste Video URL")
if st.button("🚀 Analyze") and video_url:
# Generate a unique ID for this session
unique_id = str(int(time.time()))
# Define expected paths
video_output_path = os.path.join(download_dir, f"video_{unique_id}.mp4")
audio_output_path = os.path.join(audio_dir, f"audio_{unique_id}.mp3")
cleaned_output_path = os.path.join(reduced_dir, f"audio_{unique_id}_cleaned.wav")
with st.spinner("📥 Downloading video..."):
download_video(video_url, output_path=download_dir, filename=f"video_{unique_id}")
if not os.path.exists(video_output_path):
st.error("❌ Failed to download video.")
st.stop()
st.video(video_output_path)
with st.spinner("🎙 Extracting audio from video..."):
extract_audio(video_output_path, output_audio_path=audio_output_path)
# Remove video after extracting audio
os.remove(video_output_path)
final_audio_path = audio_output_path
# Optional: noise reduction
if enable_denoise:
st.info("🎚 Noise canceling is enabled. Cleaning audio...")
final_audio_path = denoise_audio(audio_output_path, output_path=cleaned_output_path)
# Display audio players
st.markdown("---")
st.subheader("🔊 Audio Previews")
st.markdown("**🎧 Original Audio:**")
st.audio(audio_output_path, format="audio/mp3")
if enable_denoise:
st.markdown("**🔇 Denoised Audio:**")
st.audio(cleaned_output_path, format="audio/wav")
# Analyze accent
with st.spinner("🧠 Analyzing accent..."):
label, confidence = predict_accent(final_audio_path)
from accent_summary import generate_summary
# Get LLM-based explanation
summary = generate_summary(label, confidence)
# Display results
st.markdown("---")
st.subheader("🎯 Accent Analysis Result")
st.markdown(f"""
<div style='padding:1rem;border-radius:10px;background:#f9f9f9;border:1px solid #ccc;'>
<h3 style='color:#333;'>🗣 Accent: <span style='color:#0066cc'>{label}</span></h3>
<h4 style='color:#555;'>Confidence Score: <b>{confidence:.2%}</b></h4>
</div>
""", unsafe_allow_html=True)
st.markdown("### 📝 Accent Summary")
st.success(summary)
# Cleanup audio files
try:
if os.path.exists(audio_output_path):
os.remove(audio_output_path)
if enable_denoise and os.path.exists(cleaned_output_path):
os.remove(cleaned_output_path)
except Exception as e:
st.warning(f"⚠️ Could not delete some temporary files: {e}")
st.markdown("""
<style>
.stSpinner > div > div {
padding-top: 1rem;
}
</style>
""", unsafe_allow_html=True)
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