Delete app_assessment3.py

app_assessment3.py

@@ -1,97 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-from tempfile import NamedTemporaryFile
-from PyPDF2 import PdfReader
-from IPython.display import Audio
-import numpy as np
-from bark import SAMPLE_RATE, generate_audio, preload_models
-from scipy.io.wavfile import write as write_wav
-import torch
-
-def summarize_abstract_from_pdf(pdf_file_path):
-    abstract_string = 'abstract'
-    found_abstract = False
-    intro_string ='introduction'
-    extracted_text_string =""
-
-    
-    # Read the PDF and extract text from the first page
-    with open(pdf_file_path, 'rb') as pdf_file:
-        reader = PdfReader(pdf_file)
-        text = ""
-        text += reader.pages[0].extract_text()
-
-
-    file = text.splitlines()
-    for lines in file:
-      lower_lines = lines.lower()
-      if lower_lines.strip()== abstract_string:
-        found_abstract = True
-      elif "1" in lower_lines.strip() and intro_string in lower_lines.strip():
-        found_abstract = False
-
-      if found_abstract == True:
-        extracted_text_string += lines
-
-
-    extracted_text_string = extracted_text_string.replace("Abstract", "")
-    summarizer = pipeline("summarization", "pszemraj/led-base-book-summary",device=0 if torch.cuda.is_available() else -1,)
-    # Generate a summarized abstract using the specified model
-    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,
-    )
-    #I run this twice to get summazired text
-    summarized_abstract2 = summarizer(summarized_abstract[0]['summary_text'],
-    min_length=16,
-    max_length=25,
-    no_repeat_ngram_size=3,
-    encoder_no_repeat_ngram_size=3,
-    repetition_penalty=3.5,
-    num_beams=4,
-    early_stopping=True,
-    )
-
-
-
-    # Return the summarized abstract as a string
-    return summarized_abstract2[0]['summary_text']
-
-def generate_audio_func(pdf_file):
-    
-    pdf_file_path = pdf_file.name
-  # Generate audio from text
-  #call the summarize abstract function
-    text_prompt =  summarize_abstract_from_pdf(pdf_file_path)
-    audio_array = generate_audio(text_prompt)
-    
-  # Create a temporary WAV file to save the audio
-    with NamedTemporaryFile(suffix=".wav", delete=False) as temp_wav_file:
-        wav_file_path = temp_wav_file.name
-        write_wav(wav_file_path, 22050, (audio_array * 32767).astype(np.int16))
-    return wav_file_path
-
-    
-
-# Define app name, app description, and examples
-app_name = "PDF to Audio Converter"
-app_description = "Convert text from a PDF file to audio. Upload a PDF file. We accept only PDF files with abstracts."
-
-# Create the Gradio app
-input_component = gr.File(file_types=["pdf"])
-output_component = gr.Audio()
-
-demo = gr.Interface(
-    fn=generate_audio_func,
-    inputs=input_component,
-    outputs=output_component,
-    title=app_name,
-    description=app_description
-)
-
-demo.launch()