Spaces:
Sleeping
Sleeping
File size: 3,584 Bytes
854fcbb 53da45d 854fcbb 53da45d 854fcbb c7f829d 854fcbb 7ca041b 854fcbb 2fe9087 854fcbb 7ca041b 854fcbb 53da45d 854fcbb 167402c |
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 |
# https://huggingface.co/spaces/yilmazmusa_ml/abstract_summarizer
# Here are the imports
import warnings
import pdfplumber
import torch
from transformers import pipeline, AutoProcessor, AutoModel
import numpy as np
import gradio as gr
from io import BytesIO
from scipy.io.wavfile import write as write_wav
warnings.filterwarnings("ignore")
# Here is the code
def extract_abstract(uploaded_file):
pdf_bytes = BytesIO(uploaded_file)
with pdfplumber.open(pdf_bytes) as pdf:
abstract = ""
# Iterate through each page
for page in pdf.pages:
text = page.extract_text(x_tolerance = 1, y_tolerance = 1) # these parameters are set 1 in order to get spaces between words and lines
# Search for the "Abstract" keyword
if "abstract" in text.lower():
# Found the "Abstract" keyword
start_index = text.lower().find("abstract") # find the "abstract" title as starter index
end_index = text.lower().find("introduction") # find the "introduction" title as end index
abstract = text[start_index:end_index]
break
print(abstract)
return abstract
def process_summary(summary):
# Split the summary by the first period
summary = summary[0]["summary_text"]
sentences = summary.split('.', 1)
if len(sentences) > 0:
# Retrieve the first part before the period
processed_summary = sentences[0].strip() + "."
# Replace "-" with an empty string
processed_summary = processed_summary.replace("-", "")
return processed_summary
# Function for summarization and audio conversion
def summarize_and_convert_to_audio(pdf_file):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
# Move models and related tensors to CUDA device if available
processor = AutoProcessor.from_pretrained("suno/bark-small")
model = AutoModel.from_pretrained("suno/bark-small").to(device)
# Extract abstract
abstract_text = extract_abstract(pdf_file)
if not abstract_text:
return "No 'abstract' section found in the uploaded PDF. Please upload a different PDF."
# Summarize the abstract
summarization_pipeline = pipeline(task='summarization', model='knkarthick/MEETING_SUMMARY', min_length=15, max_length=30)
summarized_text = summarization_pipeline(abstract_text)
one_sentence_summary = process_summary(summarized_text)
print(one_sentence_summary)
# Text-to-audio conversion
inputs = processor(
text=[one_sentence_summary],
return_tensors="pt",
)
inputs = inputs.to(device)
speech_values = model.generate(**inputs, do_sample=True)
sampling_rate = model.generation_config.sample_rate
# Convert speech values to audio data
audio_data = speech_values.cpu().numpy().squeeze()
# Convert audio data to bytes
buffer = BytesIO()
write_wav(buffer, sampling_rate, (audio_data * (2**15)).astype(np.int16)) # Scaling audio_data to int16 format
audio_bytes = buffer.getvalue()
return audio_bytes, sampling_rate # Return audio as bytes with sampling rate
# Create a Gradio interface
iface = gr.Interface(
fn=summarize_and_convert_to_audio,
inputs=gr.UploadButton(label="Upload PDF", type="binary", file_types=["pdf"]), # Set to accept only PDF files
outputs=gr.Audio(label="Audio"),
title="PDF Abstract Summarizer",
description="Upload a PDF with an abstract to generate a summarized audio."
)
iface.launch(share=True) |