Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+from PyPDF2 import PdfReader
+import numpy as np
+from bark import generate_audio, preload_models
+from scipy.io.wavfile import write as write_wav
+import torch
+import tempfile
+import os
+
+# Preload models if needed
+preload_models()
+
+def summarize_abstract_from_pdf(pdf_file):
+    # Function to extract and summarize the abstract from a PDF
+    abstract_string = 'abstract'
+    found_abstract = False
+    intro_string = 'introduction'
+    extracted_text_string = ""
+
+    # Read the PDF and extract text from the first page
+    reader = PdfReader(pdf_file)
+    text = reader.pages[0].extract_text()
+
+    for line in text.splitlines():
+        lower_line = line.lower()
+        if lower_line.strip() == abstract_string:
+            found_abstract = True
+        elif "1" in lower_line.strip() and intro_string in lower_line.strip():
+            found_abstract = False
+
+        if found_abstract:
+            extracted_text_string += line + " "
+
+    extracted_text_string = extracted_text_string.replace("Abstract", "")
+
+    # Use Hugging Face summarization pipeline
+    summarizer = pipeline("summarization", "pszemraj/led-base-book-summary", device=0 if torch.cuda.is_available() else -1)
+    summarized_abstract = summarizer(extracted_text_string, min_length=16, max_length=150, no_repeat_ngram_size=3, encoder_no_repeat_ngram_size=3, repetition_penalty=3.5, num_beams=4, early_stopping=True)
+    return summarized_abstract[0]['summary_text']
+
+def generate_audio_func(pdf_file):
+    text_prompt = summarize_abstract_from_pdf(pdf_file)
+    audio_array = generate_audio(text_prompt)
+
+    # Create a temporary WAV file to save the audio
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_wav_file:
+        write_wav(temp_wav_file.name, 22050, (audio_array * 32767).astype(np.int16))
+        return temp_wav_file.name
+
+# Define the Gradio interface
+demo = gr.Interface(
+    fn=generate_audio_func,
+    inputs=gr.inputs.File(file_types=["pdf"]),
+    outputs=gr.outputs.Audio(type="file"),
+    title="PDF to Audio Converter",
+    description="Convert text from a PDF file to audio. Upload a PDF file with an abstract to get started."
+)
+
+if __name__ == "__main__":
+    demo.launch()