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Update app.py
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app.py
CHANGED
@@ -1,19 +1,52 @@
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import streamlit as st
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from youtube_transcript_api import YouTubeTranscriptApi
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import re
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from dotenv import load_dotenv
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import
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from groq import Groq
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#
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api_key = os.getenv('GROQ_API_KEY')
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client = Groq(api_key=api_key)
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def get_transcript(url):
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try:
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@@ -31,32 +64,39 @@ def get_transcript(url):
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def summarize_text(text):
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try:
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response = client.chat.completions.create(
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model="
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messages=[
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": f"Please provide a concise summary of the following text:\n\n{text}"}
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],
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max_tokens=150
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)
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if response.choices and response.choices[0].message:
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summary = response.choices[0].message.content.strip()
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return summary
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else:
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return "No summary available."
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except Exception as e:
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return f"Error in summarizing text: {str(e)}"
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def handle_summary(
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summary = summarize_text(transcript)
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return summary
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st.title("YouTube Summary Generator")
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youtube_url = st.text_input("YouTube URL", placeholder="Enter YouTube URL here...")
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import os
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import io
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import streamlit as st
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from youtube_transcript_api import YouTubeTranscriptApi
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import re
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import logging
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import warnings
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from pydub import AudioSegment
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import tempfile
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from dotenv import load_dotenv
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from openai import OpenAI
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# Load environment variables
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load_dotenv()
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# Initialize the OpenAI client
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client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
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# Set up logging
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logging.basicConfig(level=logging.INFO)
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def convert_to_supported_format(file):
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audio = AudioSegment.from_file(file)
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buffer = io.BytesIO()
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audio.export(buffer, format="wav")
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buffer.seek(0)
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return buffer
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def transcribe_audio(file):
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logging.info("Transcribing audio file")
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file = convert_to_supported_format(file)
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logging.info("Converted file to WAV format")
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with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_file:
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temp_file.write(file.getvalue())
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temp_file_path = temp_file.name
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try:
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with open(temp_file_path, "rb") as audio_file:
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transcript = client.audio.transcriptions.create(
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model="whisper-1",
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file=audio_file
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)
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return transcript.text
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except Exception as e:
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logging.error(f"Error in transcription: {str(e)}")
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return f"Error in transcription: {str(e)}"
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finally:
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os.remove(temp_file_path)
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def get_transcript(url):
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try:
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def summarize_text(text):
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try:
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response = client.chat.completions.create(
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model="gpt-3.5-turbo",
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messages=[
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": f"Please provide a concise summary of the following text:\n\n{text}"}
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],
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max_tokens=150
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)
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return response.choices[0].message.content.strip()
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except Exception as e:
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return f"Error in summarizing text: {str(e)}"
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def handle_summary(transcript_text):
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if "Error" in transcript_text:
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return transcript_text
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summary = summarize_text(transcript_text)
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return summary
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st.title("YouTube Summary Generator")
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option = st.selectbox("Choose input type", ("YouTube URL", "Upload audio/video file"))
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if option == "YouTube URL":
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youtube_url = st.text_input("YouTube URL", placeholder="Enter YouTube URL here...")
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if st.button("Generate Summary"):
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transcript = get_transcript(youtube_url)
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summary = handle_summary(transcript)
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st.text_area("Summary", summary, height=200)
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elif option == "Upload audio/video file":
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uploaded_file = st.file_uploader("Choose an audio or video file", type=["mp3", "wav", "mp4", "mov"])
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if uploaded_file is not None:
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if st.button("Generate Summary"):
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with st.spinner('Transcribing audio...'):
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transcript_text = transcribe_audio(uploaded_file)
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summary = handle_summary(transcript_text)
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st.text_area("Summary", summary, height=200)
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