Spaces:
Running
Running
File size: 3,584 Bytes
57954bb 8260580 ed6000d 8260580 ed6000d 8260580 ed6000d 8260580 a05ebb5 ed6000d a05ebb5 ed6000d 8260580 9e3d236 8260580 9e3d236 139900a 9e3d236 8260580 139900a 5a078c0 8260580 39a42b2 8260580 ed6000d 8260580 ed6000d 8260580 ed6000d 8260580 ed6000d 5e43307 6f6ee6e ed6000d 8260580 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 |
# https://huggingface.co/spaces/varl42/audio_abstract42
# Import libraries
import gradio as gr
import torch
import PyPDF2
from transformers import pipeline
import numpy
import scipy
from gtts import gTTS
from io import BytesIO
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# Function to extract text from PDF
# Defines a function to extract raw text from a PDF file
def extract_text(pdf_file):
pdfReader = PyPDF2.PdfReader(pdf_file)
pageObj = pdfReader.pages[0]
return pageObj.extract_text()
# Function to summarize text
# Defines a function to summarize the extracted text using pszemraj/led-base-book-summary
def summarize_text(text):
sentences = text.split(". ")
# Find the start index of the Abstract section
for i, sentence in enumerate(sentences):
if "Abstract" in sentence:
start = i + 1
# Find the end index (6 sentences after start)
end = start + 6
if start is not None and end is not None:
# Join the sentences from start to end into the abstract
abstract = ". ".join(sentences[start:end+1])
else:
return("Abstract section not found")
# Load BART model & tokenizer
tokenizer = AutoTokenizer.from_pretrained("pszemraj/led-base-book-summary")
model = AutoModelForSeq2SeqLM.from_pretrained("pszemraj/led-base-book-summary")
# Tokenize abstract
inputs = tokenizer(abstract,
max_length=1024,
return_tensors="pt",
truncation=True)
# Generate summary
summary_ids = model.generate(inputs['input_ids'],
max_length=50,
min_length=30,
no_repeat_ngram_size=3,
encoder_no_repeat_ngram_size=3,
repetition_penalty=3.5,
num_beams=4,
do_sample=True,
early_stopping=False)
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
if '.' in summary:
index = summary.rindex('.')
if index != -1:
summary = summary[:index+1]
return summary
# Function to convert text to audio
# Defines a function to convert text to an audio file using Google Text-to-Speech
def text_to_audio(text):
tts = gTTS(text, lang='en')
buffer = BytesIO()
tts.write_to_fp(buffer)
buffer.seek(0)
return buffer.read()
### Main function
### The main function that ties everything together:
### extracts text, summarizes, and converts to audio.
def audio_pdf(pdf_file):
text = extract_text(pdf_file)
summary = summarize_text(text)
audio = text_to_audio(summary)
return summary, audio
# Define Gradio interface
# Gradio web interface with a file input, text output to display the summary
# and audio output to play the audio file. # Launches the interface
inputs = gr.File()
summary_text = gr.Text()
audio_summary = gr.Audio()
iface = gr.Interface(
fn=audio_pdf,
inputs=inputs,
outputs=[summary_text,audio_summary],
title="PDF Audio Summarizer 📻",
description="App that converts an abstract into audio, please upload only PDFs with an abstract",
examples=["Hidden_Technical_Debt.pdf",
"Attention_is_all_you_need.pdf",
"ImageNet_Classification.pdf"
]
)
iface.launch() # Launch the interface |