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# 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
with BytesIO() as buffer:
write_wav(buffer, sampling_rate, audio_data.astype(np.float32))
audio_bytes = buffer.getvalue()
return audio_bytes
# 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() |