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import gradio as gr
from gtts import gTTS
import pdfplumber
from sumy.parsers.plaintext import PlaintextParser
from sumy.nlp.tokenizers import Tokenizer
from sumy.summarizers.lsa import LsaSummarizer
import nltk
import os
# Download NLTK data for sumy
nltk.download('punkt')
def extract_text_from_pdf(pdf_file):
"""
Extract text from a PDF file using pdfplumber.
Args:
pdf_file: Uploaded PDF file.
Returns:
str: Extracted text from the PDF.
"""
try:
with pdfplumber.open(pdf_file) as pdf:
text = ""
for page in pdf.pages:
text += page.extract_text() or ""
return text if text else "No text could be extracted from the PDF."
except Exception as e:
return f"Error extracting text: {str(e)}"
def summarize_text(text, sentences_count=12):
"""
Summarize text to approximately four paragraphs using sumy LSA summarizer.
Args:
text (str): Text to summarize.
sentences_count (int): Number of sentences in summary (approx. 3 sentences per paragraph).
Returns:
str: Summarized text.
"""
try:
# Initialize parser and tokenizer
parser = PlaintextParser.from_string(text, Tokenizer("english"))
summarizer = LsaSummarizer()
# Summarize to specified number of sentences
summary = summarizer(parser.document, sentences_count)
# Combine sentences and format into paragraphs (approx. 3 sentences per paragraph)
summary_text = ""
for i, sentence in enumerate(summary):
summary_text += str(sentence) + " "
if (i + 1) % 3 == 0: # Add paragraph break every 3 sentences
summary_text += "\n\n"
return summary_text.strip() if summary_text else "No summary generated."
except Exception as e:
return f"Error summarizing text: {str(e)}"
def pdf_to_speech(pdf_file, lang="en"):
"""
Convert text from a PDF to summarized speech using gTTS.
Args:
pdf_file: Uploaded PDF file.
lang (str): Language code (default is 'en' for English).
Returns:
tuple: (Path to audio file, summarized text) or (error message, error message).
"""
try:
# Extract text from PDF
text = extract_text_from_pdf(pdf_file)
if "Error" in text:
return text, text
# Summarize text (approx. 12 sentences for 4 paragraphs)
summarized_text = summarize_text(text, sentences_count=12)
if "Error" in summarized_text:
return summarized_text, summarized_text
# Create gTTS object
tts = gTTS(text=summarized_text, lang=lang, slow=False)
# Save the audio file
output_file = "output.mp3"
tts.save(output_file)
return output_file, summarized_text
except Exception as e:
error_msg = f"An error occurred: {str(e)}"
return error_msg, error_msg
# Define Gradio interface
demo = gr.Interface(
fn=pdf_to_speech,
inputs=[
gr.File(label="Upload a PDF file", file_types=[".pdf"]),
gr.Dropdown(choices=["en", "es", "fr"], label="Select Language", value="en")
],
outputs=[
gr.Audio(label="Generated Speech"),
gr.Textbox(label="Summarized Text")
],
title="PDF Summary to Speech",
description="Upload an English PDF file, select a language, and generate speech from a summarized version (approx. 4 paragraphs). The summarized text is also displayed."
)
# Launch the app
if __name__ == "__main__":
demo.launch() |