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YT to blog, bug fixes - WIP

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This commit is contained in:
ajaysi
2024-10-12 07:59:13 +05:30
parent 16bcd86bb7
commit e6f60feba5
6 changed files with 170 additions and 133 deletions
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66
lib/ai_writers/speech_to_blog/main_audio_to_blog.py
View File

@@ -2,10 +2,13 @@ import os
import datetime #I wish
import sys
from textwrap import dedent
import openai
from tqdm import tqdm, trange
import time
from pytubefix import YouTube
import tempfile
from html2image import Html2Image
from loguru import logger
logger.remove()
logger.add(sys.stdout,
@@ -13,11 +16,70 @@ logger.add(sys.stdout,
format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
)
from .write_blogs_from_youtube_videos import youtube_to_blog
from ...ai_web_researcher.gpt_online_researcher import do_google_serp_search
from ..blog_from_google_serp import blog_with_research
from ...blog_metadata.get_blog_metadata import blog_metadata
from ...blog_postprocessing.save_blog_to_file import save_blog_to_file
from ...gpt_providers.audio_to_text_generation.stt_audio_blog import speech_to_text
from ...gpt_providers.text_generation.main_text_generation import llm_text_gen
def youtube_to_blog(video_url):
"""Function to transcribe a given youtube url """
try:
# Starting the speech-to-text process
logger.info("Starting with Speech to Text.")
audio_text, audio_title = speech_to_text(video_url)
except Exception as e:
logger.error(f"Error in speech_to_text: {e}")
sys.exit(1) # Exit the program due to error in speech_to_text
try:
# Summarizing the content of the YouTube video
audio_blog_content = summarize_youtube_video(audio_text)
logger.info("Successfully converted given URL to blog article.")
return audio_blog_content, audio_title
except Exception as e:
logger.error(f"Error in summarize_youtube_video: {e}")
return False
def summarize_youtube_video(user_content):
"""Generates a summary of a YouTube video using OpenAI GPT-3 and displays a progress bar.
Args:
video_link: The URL of the YouTube video to summarize.
Returns:
A string containing the summary of the video.
"""
logger.info("Start summarize_youtube_video..")
prompt = f"""
You are an expert copywriter specializing in digital content writing. I will provide you with a transcript.
Your task is to transform a given transcript into a well-structured and informative blog article.
Please follow the below objectives:
1. Master the Transcript: Understand main ideas, key points, and the core message.
2. Sentence Structure: Rephrase while preserving logical flow and coherence. Dont quote anyone from video.
3. Note: Check if the transcript is about programming, then include code examples and snippets in your article.
4. Write Unique Content: Avoid direct copying; rewrite in your own words.
5. REMEMBER to avoid direct quoting and maintain uniqueness.
6. Proofread: Check for grammar, spelling, and punctuation errors.
7. Use Creative and Human-like Style: Incorporate contractions, idioms, transitional phrases, interjections, and colloquialisms. 8. Avoid repetitive phrases and unnatural sentence structures.
9. Ensure Uniqueness: Guarantee the article is plagiarism-free.
10. Punctuation: Use appropriate question marks at the end of questions.
11. Pass AI Detection Tools: Create content that easily passes AI plagiarism detection tools.
12. Rephrase words like 'video, youtube, channel' with 'article, blog' and such suitable words.
Follow the above guidelines to create a well-optimized, unique, and informative article,
that will rank well in search engine results and engage readers effectively.
Follow above guidelines to craft a blog content from the following transcript:\n{user_content}
"""
try:
response = llm_text_gen(prompt)
return response
except Exception as err:
logger.error(f"Failed to summarize_youtube_video: {err}")
exit(1)
def generate_audio_blog(audio_input):

82
lib/ai_writers/speech_to_blog/write_blogs_from_youtube_videos.py
View File

@@ -1,82 +0,0 @@
import os
import time
import sys
from pytube import YouTube
import tempfile
import openai
from html2image import Html2Image
from tqdm import tqdm, trange
import google.generativeai as genai
from loguru import logger
logger.remove()
logger.add(sys.stdout,
colorize=True,
format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
)
from ...gpt_providers.audio_to_text_generation.stt_audio_blog import speech_to_text
from ...gpt_providers.text_generation.main_text_generation import llm_text_gen
def youtube_to_blog(video_url):
"""Function to transcribe a given youtube url """
# fixme: Doesnt work all types of yt urls.
vid_id = video_url.split("=")[1]
try:
# Starting the speech-to-text process
logger.info("Starting with Speech to Text.")
audio_text, audio_title = speech_to_text(video_url)
except Exception as e:
logger.error(f"Error in speech_to_text: {e}")
sys.exit(1) # Exit the program due to error in speech_to_text
try:
# Summarizing the content of the YouTube video
audio_blog_content = summarize_youtube_video(audio_text)
logger.info("Successfully converted given URL to blog article.")
return audio_blog_content, audio_title
except Exception as e:
logger.error(f"Error in summarize_youtube_video: {e}")
sys.exit(1) # Exit the program due to error in summarize_youtube_video
def summarize_youtube_video(user_content):
"""Generates a summary of a YouTube video using OpenAI GPT-3 and displays a progress bar.
Args:
video_link: The URL of the YouTube video to summarize.
Returns:
A string containing the summary of the video.
"""
logger.info("Start summarize_youtube_video..")
prompt = f"""
You are an expert copywriter specializing in digital content writing. I will provide you with a transcript.
Your task is to transform a given transcript into a well-structured and informative blog article.
Please follow the below objectives:
1. Master the Transcript: Understand main ideas, key points, and the core message.
2. Sentence Structure: Rephrase while preserving logical flow and coherence. Dont quote anyone from video.
3. Note: Check if the transcript is about programming, then include code examples and snippets in your article.
4. Write Unique Content: Avoid direct copying; rewrite in your own words.
5. REMEMBER to avoid direct quoting and maintain uniqueness.
6. Proofread: Check for grammar, spelling, and punctuation errors.
7. Use Creative and Human-like Style: Incorporate contractions, idioms, transitional phrases, interjections, and colloquialisms. 8. Avoid repetitive phrases and unnatural sentence structures.
9. Ensure Uniqueness: Guarantee the article is plagiarism-free.
10. Punctuation: Use appropriate question marks at the end of questions.
11. Pass AI Detection Tools: Create content that easily passes AI plagiarism detection tools.
12. Rephrase words like 'video, youtube, channel' with 'article, blog' and such suitable words.
Follow the above guidelines to create a well-optimized, unique, and informative article,
that will rank well in search engine results and engage readers effectively.
Follow above guidelines to craft a blog content from the following transcript:\n{user_content}
"""
try:
response = llm_text_gen(prompt)
return response
except Exception as err:
logger.error(f"Failed to summarize_youtube_video: {err}")
exit(1)

116
lib/gpt_providers/audio_to_text_generation/gemini_audio_text.py
View File

@@ -1,61 +1,101 @@
import os
import sys
import google.generativeai as genai
from dotenv import load_dotenv
from loguru import logger
logger.remove()
logger.add(sys.stdout,
colorize=True,
format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
)
def load_environment():
"""Load environment variables from a .env file."""
"""Loads environment variables from a .env file."""
load_dotenv()
logger.info("Environment variables loaded successfully.")
def configure_google_api():
"""Configure the Google API for audio summarization."""
"""Configures the Google Gemini API for audio transcription.
Raises:
ValueError: If the GEMINI_API_KEY environment variable is not set.
"""
api_key = os.getenv("GEMINI_API_KEY")
if not api_key:
raise ValueError("Google API key not found. Please set the GEMINI_API_KEY environment variable.")
error_message = "Google API key not found. Please set the GEMINI_API_KEY environment variable."
logger.error(error_message)
raise ValueError(error_message)
genai.configure(api_key=api_key)
logger.info("Google Gemini API configured successfully.")
def transcribe_audio(audio_file_path):
"""Summarize the audio using Google's Generative API.
"""
Transcribes audio using Google's Gemini Pro model.
Args:
audio_file_path (str): The path to the audio file to be summarized.
audio_file_path (str): The path to the audio file to be transcribed.
Returns:
str: The summary text of the audio.
str: The transcribed text from the audio.
Returns None if transcription fails.
Raises:
ValueError: If the audio file path is invalid or the API response is not successful.
Exception: For any other errors that occur during the process.
FileNotFoundError: If the audio file is not found.
"""
try:
# Load environment variables and configure API
# Load environment variables and configure the Google API
load_environment()
configure_google_api()
# Create generative model instance
model = genai.GenerativeModel("models/gemini-1.5-pro-latest")
audio_file = None
try:
# Upload the audio file
audio_file = genai.upload_file(path=audio_file_path)
except Exception as err:
print(err)
# Generate the summary
response = model.generate_content(
[
"Listen carefully to the given following audio file. Transcribe the following given audio.",
audio_file
]
)
# Check if the response contains text
if not hasattr(response, 'text'):
raise ValueError("The API response does not contain text.")
return response.text
logger.info(f"Attempting to transcribe audio file: {audio_file_path}")
# Check if file exists
if not os.path.exists(audio_file_path):
error_message = f"FileNotFoundError: The audio file at {audio_file_path} does not exist."
logger.error(error_message)
raise FileNotFoundError(error_message)
# Initialize a Gemini model appropriate for your use case.
model = genai.GenerativeModel(model_name="gemini-1.5-flash")
# Upload the audio file
try:
audio_file = genai.upload_file(audio_file_path)
logger.info(f"Audio file uploaded successfully: {audio_file=}")
except FileNotFoundError:
error_message = f"FileNotFoundError: The audio file at {audio_file_path} does not exist."
logger.error(error_message)
raise FileNotFoundError(error_message)
except Exception as e:
logger.error(f"Error uploading audio file: {e}")
return None
# Generate the transcription
try:
response = model.generate_content([
"Transcribe the following audio:",
audio_file
])
# Check for valid response and extract text
if response and hasattr(response, 'text'):
transcript = response.text
logger.info(f"Transcription successful:\n{transcript}")
return transcript
else:
logger.warning("Transcription failed: Invalid or empty response from API.")
return None
except Exception as e:
logger.error(f"Error during transcription: {e}")
return None
except ValueError as ve:
print(f"ValueError: {ve}")
except FileNotFoundError:
print(f"FileNotFoundError: The audio file at {audio_file_path} does not exist.")
except Exception as e:
print(f"An error occurred: {e}")
logger.error(f"An unexpected error occurred: {e}")
return None

35
lib/gpt_providers/audio_to_text_generation/stt_audio_blog.py
View File

@@ -2,7 +2,7 @@ import os
import re
import sys
from pytube import YouTube
from pytubefix import YouTube
from loguru import logger
from openai import OpenAI
from tqdm import tqdm
@@ -22,6 +22,7 @@ def progress_function(stream, chunk, bytes_remaining):
current = ((stream.filesize - bytes_remaining) / stream.filesize)
progress_bar.update(current - progress_bar.n) # Update the progress bar
def rename_file_with_underscores(file_path):
"""Rename a file by replacing spaces and special characters with underscores.
@@ -62,22 +63,32 @@ def speech_to_text(video_url):
SystemExit: If a critical error occurs that prevents successful execution.
"""
output_path = os.getenv("CONTENT_SAVE_DIR")
yt = None
audio_file = None
with st.status("Started Writing..", expanded=False) as status:
try:
audio_file = None
if video_url.startswith("https://www.youtube.com/") or video_url.startswith("http://www.youtube.com/"):
logger.info(f"Accessing YouTube URL: {video_url}")
status.update(label=f"Accessing YouTube URL: {video_url}")
yt = YouTube(video_url, on_progress_callback=progress_function)
logger.info("Fetching the highest quality audio stream")
status.update(label="Fetching the highest quality audio stream")
audio_stream = yt.streams.filter(only_audio=True).first()
try:
vid_id = video_url.split("=")[1]
yt = YouTube(video_url, on_progress_callback=progress_function)
except Exception as err:
logger.error(f"Failed to get pytube stream object: {err}")
st.stop()
logger.info(f"Fetching the highest quality audio stream:{yt.title}")
status.update(label=f"Fetching the highest quality audio stream: {yt.title}")
try:
audio_stream = yt.streams.filter(only_audio=True).first()
except Exception as err:
logger.error(f"Failed to Download Youtube Audio: {err}")
st.stop()
if audio_stream is None:
logger.warning("No audio stream found for this video.")
st.warning("No audio stream found for this video.")
return None
st.stop()
logger.info(f"Downloading audio for: {yt.title}")
status.update(label=f"Downloading audio for: {yt.title}")
@@ -113,9 +124,13 @@ def speech_to_text(video_url):
# FIXME: We can chunk hour long videos, the code is not tested.
#long_video(audio_file)
sys.exit("File size limit exceeded.")
st.error("Audio File size limit exceeded.")
st.error("Audio File size limit exceeded. File a fixme/issues at ALwrity github.")
try:
print(f"Audio File: {audio_file}")
transcript = transcribe_audio(audio_file)
print(f"\n\n\n--- Tracribe: {transcript} ----\n\n\n")
exit(1)
status.update(label=f"Initializing OpenAI client for transcription: {audio_file}")
logger.info(f"Initializing OpenAI client for transcription: {audio_file}")
client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
@@ -170,7 +185,7 @@ def long_video(temp_file_name):
video_url (str): URL of the YouTube video to be transcribed.
"""
# Extract audio and split into chunks
app.logger.info(f"Processing the YT video: {temp_file_name}")
logger.info(f"Processing the YT video: {temp_file_name}")
full_audio = mp.AudioFileClip(temp_file_name)
duration = full_audio.duration
chunk_length = 600 # 10 minutes in seconds

3
lib/utils/alwrity_utils.py
View File

@@ -169,7 +169,8 @@ def blog_from_keyword():
st.error('🚫 Blog keywords should be at least two words long. Please try again.')
elif input_type == "youtube_url" or input_type == "audio_file":
generate_audio_blog(user_input)
if not generate_audio_blog(user_input):
st.stop()
elif input_type == "web_url":
blog_from_url(user_input)

1
requirements.txt
View File

@@ -38,4 +38,5 @@ streamlit-mic-recorder
tinify
cloudscraper
xmlschema
moviepy
googlesearch-python