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Update app.py
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app.py
CHANGED
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@@ -5,8 +5,6 @@ from pydub import AudioSegment
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import tempfile
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import os
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import io
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from pytube import YouTube
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import requests
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# Function to convert video to audio
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def video_to_audio(video_file):
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@@ -53,32 +51,12 @@ def transcribe_audio(audio_file):
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except sr.RequestError:
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return "Could not request results from Google Speech Recognition service."
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# Function to download audio from YouTube and convert it to WAV
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def download_youtube_audio(url):
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# Get the YouTube video
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yt = YouTube(url)
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# Get the highest quality stream available (audio only)
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audio_stream = yt.streams.filter(only_audio=True).first()
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# Download the audio as an MP4 file (audio-only)
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temp_audio_path = tempfile.mktemp(suffix=".mp4")
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audio_stream.download(output_path=temp_audio_path)
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# Convert the downloaded MP4 to WAV format
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wav_audio_file = convert_mp3_to_wav(temp_audio_path)
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# Cleanup the temporary MP4 file
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os.remove(temp_audio_path)
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return wav_audio_file
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# Streamlit app layout
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st.title("Video
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st.write("Upload a video
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# Create tabs to separate video
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tab = st.selectbox("Select the type of file to upload", ["Video", "Audio"
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if tab == "Video":
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# File uploader for video
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@@ -195,58 +173,4 @@ elif tab == "Audio":
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data=st.session_state.wav_audio_file_audio,
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file_name="converted_audio_audio.wav",
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mime="audio/wav"
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)
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elif tab == "YouTube URL":
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# Input for YouTube URL
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youtube_url = st.text_input("Enter YouTube URL")
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if youtube_url:
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# Add an "Analyze YouTube URL" button
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if st.button("Analyze YouTube URL"):
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with st.spinner("Processing YouTube video... Please wait."):
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try:
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# Download audio from the YouTube video
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wav_audio_file = download_youtube_audio(youtube_url)
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# Transcribe audio to text
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transcription = transcribe_audio(wav_audio_file)
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# Show the transcription
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st.text_area("Transcription", transcription, height=300)
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# Store transcription and audio file in session state
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st.session_state.transcription_youtube = transcription
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# Store the audio file as a BytesIO object in memory
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with open(wav_audio_file, "rb") as f:
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audio_data = f.read()
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st.session_state.wav_audio_file_youtube = io.BytesIO(audio_data)
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# Cleanup the temporary audio file
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os.remove(wav_audio_file)
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except Exception as e:
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st.error(f"Error processing the YouTube URL: {e}")
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# Check if transcription and audio file are stored in session state
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if 'transcription_youtube' in st.session_state and 'wav_audio_file_youtube' in st.session_state:
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# Provide the audio file to the user for download
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st.audio(st.session_state.wav_audio_file_youtube, format='audio/wav')
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# Add download buttons for the transcription and audio
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# Downloadable transcription file
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st.download_button(
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label="Download Transcription",
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data=st.session_state.transcription_youtube,
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file_name="transcription_youtube.txt",
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mime="text/plain"
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)
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# Downloadable audio file
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st.download_button(
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label="Download Audio",
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data=st.session_state.wav_audio_file_youtube,
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file_name="converted_audio_youtube.wav",
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mime="audio/wav"
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)
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import tempfile
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import os
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import io
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# Function to convert video to audio
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def video_to_audio(video_file):
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except sr.RequestError:
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return "Could not request results from Google Speech Recognition service."
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# Streamlit app layout
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st.title("Video and Audio to Text Transcription")
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st.write("Upload a video or audio file to convert it to transcription.")
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# Create tabs to separate video and audio uploads
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tab = st.selectbox("Select the type of file to upload", ["Video", "Audio"])
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if tab == "Video":
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# File uploader for video
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data=st.session_state.wav_audio_file_audio,
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file_name="converted_audio_audio.wav",
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mime="audio/wav"
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)
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