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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 scipy.io.wavfile import write as write_wav
warnings.filterwarnings("ignore")
# Here is the code
def extract_abstract(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")
model = AutoModel.from_pretrained("suno/bark").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, 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="filepath", 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() |