# -*- coding: utf-8 -*-
"""Accent.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1yprWdRUXGqD4QIFAZuMwdyTuwA2Hhdvj
"""

# Install needed libraries (run this cell first!)
!pip install --quiet yt-dlp ffmpeg-python torch torchaudio transformers streamlit speechbrain

import os
import subprocess
import torchaudio
import torch
from speechbrain.pretrained import EncoderClassifier
import yt_dlp

# Paste your video URL here (YouTube or direct MP4 link)
VIDEO_URL = "https://youtu.be/DDjWTWHHkpk?si=oIj6Fuy8Hg2E8U_l"  # Example: Replace with your actual link!

def download_video(url, out_path="input_video.mp4"):
    """
    Downloads a video from YouTube or direct MP4 link.
    Returns the filename of the downloaded video.
    """
    # If it's a YouTube link, use yt-dlp
    if "youtube.com" in url or "youtu.be" in url:
        ydl_opts = {'outtmpl': out_path}
        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
            ydl.download([url])
    else:
        # For direct links, use wget/curl fallback
        os.system(f"wget -O {out_path} {url}")
    return out_path

video_file = download_video(VIDEO_URL)
print(f"Downloaded video: {video_file}")

def extract_audio(video_path, audio_path="audio.wav"):
    """
    Extracts audio from a video file using ffmpeg.
    Returns the filename of the audio file.
    """
    # Remove if already exists
    if os.path.exists(audio_path):
        os.remove(audio_path)
    # Extract audio with ffmpeg
    cmd = f"ffmpeg -y -i {video_path} -ar 16000 -ac 1 -vn {audio_path}"
    subprocess.call(cmd, shell=True)
    return audio_path

audio_file = extract_audio(video_file)
print(f"Extracted audio file: {audio_file}")

def extract_audio(video_path, audio_path="/content/audio.wav"):
    """
    Extracts audio from a video file using ffmpeg.
    Returns the filename of the audio file.
    """
    # Remove if already exists
    if os.path.exists(audio_path):
        os.remove(audio_path)
    # Extract audio with ffmpeg
    cmd = f"ffmpeg -y -i {video_path} -ar 16000 -ac 1 -vn {audio_path}"
    # Use subprocess.run to capture output and check the return code
    result = subprocess.run(cmd, shell=True, capture_output=True, text=True)

    if result.returncode != 0:
        print(f"FFmpeg command failed with error code {result.returncode}")
        print("FFmpeg stderr:")
        print(result.stderr)
        # Optionally, raise an error or exit if audio extraction fails
        raise RuntimeError(f"Failed to extract audio using FFmpeg. See stderr above.")
    else:
        print("FFmpeg stdout:")
        print(result.stdout)
        print("FFmpeg stderr:")
        print(result.stderr) # ffmpeg often outputs info/warnings to stderr

    # Check if the audio file was actually created
    if not os.path.exists(audio_path):
         raise FileNotFoundError(f"Audio file '{audio_path}' was not created after FFmpeg execution.")

    return audio_path

# Download the pre-trained English accent classifier (SpeechBrain)
accent_model = EncoderClassifier.from_hparams(
    source="speechbrain/lang-id-commonlanguage_ecapa",
    savedir="tmp_accent_model"
)

"""Used to Debuging the code"""

# List the files to see if input_video.mp4 is present
import os
print(os.listdir('.'))

"""TO check the debug file path"""

# Try extracting audio again, but print output to check for errors
video_path = "/content/input_video.mp4.webm"  # or whatever your filename is!
audio_path = "audio.wav"

os.system(f"ffmpeg -y -i {video_path} -ar 16000 -ac 1 -vn {audio_path}")

# See if audio.wav was created
print(os.listdir('.'))

"""Check the Size of the file"""

# Check if the file now exists and get its size
import os
print("audio.wav exists:", os.path.exists(audio_path))
if os.path.exists(audio_path):
    print("audio.wav size (bytes):", os.path.getsize(audio_path))

# Load the audio file (must be 16kHz mono)
signal, fs = torchaudio.load(audio_file)

# If stereo, take only the first channel
if signal.shape[0] > 1:
    signal = signal[0].unsqueeze(0)

# Run classification
prediction = accent_model.classify_batch(signal)
pred_label = prediction[3][0]
pred_scores = prediction[1][0]

# Convert score to percentage
confidence = float(pred_scores.max()) * 100

# Display top label and score
print(f"Predicted Accent: {pred_label}")
print(f"Confidence: {confidence:.1f}%")
print("Possible accent labels:", accent_model.hparams.label_encoder.lab2ind.keys())

explanation = f"The speaker's English accent was classified as '{pred_label}' with a confidence score of {confidence:.1f}%. This means the model is {confidence:.0f}% sure the person sounds most similar to this accent group."

print(explanation)

# Save as app.py in Colab for launching a simple web UI
with open("app.py", "w") as f:
    f.write('''
import streamlit as st
import os
import subprocess
import torchaudio
from speechbrain.pretrained import EncoderClassifier

st.title("🗣️ English Accent Classifier (Proof of Concept)")

url = st.text_input("Enter public video URL (YouTube or direct MP4):")
if st.button("Analyze"):
    with st.spinner("Downloading video..."):
        if "youtube.com" in url or "youtu.be" in url:
            os.system(f'yt-dlp -o input_video.mp4 "{url}"')
        else:
            os.system(f'wget -O input_video.mp4 "{url}"')
    with st.spinner("Extracting audio..."):
        os.system("ffmpeg -y -i input_video.mp4 -ar 16000 -ac 1 -vn audio.wav")
    with st.spinner("Classifying accent..."):
        accent_model = EncoderClassifier.from_hparams(
            source="speechbrain/lang-id-commonlanguage_ecapa",
            savedir="tmp_accent_model"
        )
        signal, fs = torchaudio.load("audio.wav")
        if signal.shape[0] > 1:
            signal = signal[0].unsqueeze(0)
        prediction = accent_model.classify_batch(signal)
        pred_label = prediction[3][0]
        pred_scores = prediction[1][0]
        confidence = float(pred_scores.max()) * 100
        st.success(f"Predicted Accent: {pred_label} ({confidence:.1f}%)")
        st.info(f"The model is {confidence:.0f}% confident this is a {pred_label} English accent.")
''')

print("Streamlit app code saved as app.py!")
print("To launch the UI, run: !streamlit run app.py --server.headless true --server.port 8501")