Add Application files

app.py

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+import streamlit as st
+import pytube as pt
+import os
+import subprocess
+import re
+from utils import logtime, load_ffmpeg
+import whisper
+from langchain.document_loaders import YoutubeLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+URL = 'URL'
+TEXT = 'TEXT'
+WHISPER = 'WHISPER'
+PROCESSING = 'PROCESSING'
+STATES = [URL, TEXT, WHISPER, PROCESSING]
+AUDIO_FILE = "audio.mp3"
+AUDIO_EXISTS = "AUDIO_EXISTS"
+model = ''
+
+st.title('Youtube Audio+Text')
+
+def init_state():
+    if URL not in st.session_state:
+        st.session_state[URL] = ''
+    
+    if TEXT not in st.session_state:
+        st.session_state[TEXT] = ''
+
+    if WHISPER not in st.session_state:
+        st.session_state[WHISPER] = ''
+
+    if AUDIO_EXISTS not in st.session_state:
+        st.session_state[AUDIO_EXISTS] = False
+    
+    # if not st.session_state[URL]:
+    #     clear_old_files()
+
+def clear_old_files():
+    for file in os.listdir():
+        if file.endswith(".mp3") or file == 'transcript.txt':
+            os.remove(file)
+            print(f"Removed old files::{file}")
+
+    
+def extract_youtube_video_id(url):
+  regex = r"v=([^&]+)"
+  match = re.search(regex, url)
+  if match:
+    return match.group(1)
+  else:
+    return None
+
+@logtime
+def load_whisper():
+    # if not model:
+    model = whisper.load_model("small")
+    print('Loaded Whisper Medium model')
+    # else:
+    #     print('Already downloaded Whisper model')
+    print('Transcribing with Whisper model')
+    result = model.transcribe("audio.mp3")
+    st.session_state[WHISPER] = result["text"]
+    write_file(result["text"], "transcript.txt")
+AUDIO_FILE = "audio.mp3"
+
+def load_audio():
+    if os.path.exists(AUDIO_FILE):
+        st.session_state[AUDIO_EXISTS] = True
+        audio_file = open(AUDIO_FILE, 'rb')
+        audio_bytes = audio_file.read()
+        print(f"Audio file exists...{len(audio_bytes)}")
+        st.audio(audio_bytes, format="audio/mp3")
+    elif st.session_state[AUDIO_EXISTS]:
+        st.session_state[AUDIO_EXISTS] = False
+
+def display():
+    container = st.container()
+    text_container = st.container()
+    # whisper_container = st.container()
+    load_audio()
+
+    #Download Button section
+    col1, col2 = st.columns(2)
+    with col1:
+        if st.session_state[AUDIO_EXISTS]:
+            st.download_button("Download Audio","file","audio.mp3","application/octet-stream")
+    with col2:
+        if os.path.exists("transcript.txt"):
+            st.download_button("Download Transcript",st.session_state[TEXT],"transcript.txt","text/plain")
+
+    with container:
+        with st.form(key='input_form'):
+            user_input = st.text_input("Youtube URL:", placeholder="http://www.youtube.com", key=URL)
+            input_submit_button = st.form_submit_button(label='Send')
+
+    if input_submit_button and user_input:
+
+        st.write("You entered... " + st.session_state[URL])
+        # transcribe()
+        # download()
+        # download_audio()
+        load_whisper()
+
+    
+    with text_container:
+       st.text_area(label="Youtube Transcript:",
+                    height=200,
+                    value=st.session_state[TEXT])
+    # with whisper_container:
+    #    st.text_area(label="Whisper Transcript:",
+    #                 height=200,
+    #                 value=st.session_state[WHISPER])
+
+@logtime
+def download_audio():
+    if st.session_state[URL]:
+        print("Downloading....")
+        yt = pt.YouTube(st.session_state[URL])
+        stream = yt.streams.filter(only_audio=True)[0]
+        stream.download(filename="audio.mp3")
+        print("Downloaded Audio file....")
+
+def download():
+  id = extract_youtube_video_id(st.session_state[URL])
+  command = [f"yt-dlp --no-config -v --extract-audio --audio-format mp3 {st.session_state[URL]} -o audio.mp3"]
+  print(command)
+  out = subprocess.run(command, shell=True)
+  print('Download with YT-DLP done!!')
+
+@logtime
+def transcribe():
+    loader = YoutubeLoader.from_youtube_url(
+        st.session_state[URL], add_video_info=True)
+    splitter = RecursiveCharacterTextSplitter(chunk_size=2000,chunk_overlap=500)
+    docs = loader.load_and_split(splitter)
+    length = len(docs)
+    index = int(length/3+1)
+    print(f"Loaded {length} documents, Displaying {index}-th document")
+    # st.session_state[TEXT] = docs[index].page_content
+    st.session_state[TEXT] = write_chunks(docs,"transcript.txt")
+
+@logtime
+def write_chunks(docs, filename):
+    full_doc = ''
+    for doc in docs:
+       full_doc = full_doc + doc.page_content + "\n"
+    with open(filename, "w") as f:
+        f.write(full_doc)
+        return full_doc
+
+def write_file(text, filename):
+    with open(filename, "w") as f:
+        f.write(text)
+        # return full_doc
+
+def main():
+    # load_ffmpeg()
+    init_state()
+    display()
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+openai
+langchain
+youtube-transcript-api
+pytube
+openai-whisper