Delete app.py

app.py

@@ -1,160 +0,0 @@
-import PyPDF2
-import pdfplumber
-from pdfminer.high_level import extract_pages, extract_text
-from pdfminer.layout import LTTextContainer, LTChar, LTRect, LTFigure
-import re
-import torch
-import transformers
-from transformers import pipeline
-from datasets import load_dataset
-import soundfile as sf
-from IPython.display import Audio
-from datasets import load_dataset
-import sentencepiece as spm
-import os
-import tempfile
-import gradio as gr
-
-description = """**SpeechAbstractor**\n
-This app enables users to upload academic articles in PDF format, specifically focusing on abstracts. 
-It efficiently summarizes the abstract and provides an audio playback of the summarized content. 
-Below are some example PDFs for you to experiment with. Feel free to explore the functionality of SpeechAbstractor!"""
-
-examples = [
-    ["Article_7.pdf"],["Article_9.pdf"],["Article_11.pdf"]
-    ]
-
-#reporting the functions created for the part 1 
-def text_extraction(element):
-    line_text = element.get_text()
-
-    line_formats = []
-    for text_line in element:
-        if isinstance(text_line, LTTextContainer):
-            for character in text_line:
-                if isinstance(character, LTChar):
-                    line_formats.append(character.fontname)
-                    line_formats.append(character.size)
-    format_per_line = list(set(line_formats))
-
-    return (line_text, format_per_line)
-
-def read_pdf(pdf_pathy):
-  pdfFileObj = open(pdf_pathy, 'rb')
-  pdfReaded = PyPDF2.PdfReader(pdfFileObj)
-
-  text_per_pagy = {}
-  for pagenum, page in enumerate(extract_pages(pdf_pathy)):
-      print("Elaborating Page_" +str(pagenum))
-      pageObj = pdfReaded.pages[pagenum]
-      page_text = []
-      line_format = []
-      page_content = []
-
-      pdf = pdfplumber.open(pdf_pathy)
-
-      page_elements = [(element.y1, element) for element in page._objs]
-      page_elements.sort(key=lambda a: a[0], reverse=True)
-
-      for i,component in enumerate(page_elements):
-          pos= component[0]
-          element = component[1]
-
-          if isinstance(element, LTTextContainer):
-                  (line_text, format_per_line) = text_extraction(element)
-                  page_text.append(line_text)
-                  line_format.append(format_per_line)
-                  page_content.append(line_text)
-          
-
-      dctkey = 'Page_'+str(pagenum)
-      text_per_pagy[dctkey]= [page_text, line_format, page_content]
-
-  pdfFileObj.close()
-
-
-  return text_per_pagy
-
-
-def clean_text(text):
-    # remove extra spaces
-    text = re.sub(r'\s+', ' ', text)
-
-    return text.strip()
-
-
-def extract_abstract(text_per_pagy):
-    abstract_text = ""
-    
-    for page_num, page_text in text_per_pagy.items():
-        if page_text:
-            page_text = page_text.replace("- ", "")
-           
-            start_index = page_text.find("Abstract")
-            if start_index != -1:
-                start_index += len("Abstract") + 1
-
-                end_markers = ["Introduction", "Summary", "Overview", "Background", "Contents"]
-                end_index = -1
-
-                for marker in end_markers:
-                    temp_index = page_text.find(marker, start_index)
-                    if temp_index != -1:
-                        end_index = temp_index
-                        break
-
-                if end_index == -1:
-                    end_index = len(page_text)
-
-                abstract = page_text[start_index:end_index].strip()
-
-                abstract_text += " " + abstract
-
-                break
-
-    return abstract_text
-
-#let's define a main function that gets the uploaded file (pdf) to do the job
-def main_function(uploaded_filepath):
-    #put a control to see if there is a file uploaded
-    if uploaded_filepath is None:
-        return "No file loaded", None
-
-    #read and process the file according to read_pdf
-    text_per_pagy = read_pdf(uploaded_filepath)
-
-    #cleaning the text and getting the abstract using the 2 other functions
-    for key, value in text_per_pagy.items():
-        cleaned_text = clean_text(' '.join(value[0]))
-        text_per_pagy[key] = cleaned_text
-    abstract_text = extract_abstract(text_per_pagy)
-
-    #abstract the summary with my pipeline and model, deciding the length
-    summarizer = pipeline("summarization", model="pszemraj/long-t5-tglobal-base-sci-simplify")
-    summary = summarizer(abstract_text, max_length=65, do_sample=False)[0]['summary_text']
-
-    #generating the audio from the text, with my pipeline and model
-    synthesiser = pipeline("text-to-speech", model="microsoft/speecht5_tts")
-    embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
-    speaker_embedding = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
-    speech = synthesiser(summary, forward_params={"speaker_embeddings": speaker_embedding})
-
-    #saving the audio in a temporary file
-    audio_file_path = "summary.wav"
-    sf.write(audio_file_path, speech["audio"], samplerate=speech["sampling_rate"])
-
-    #the function returns the 2 pieces we need
-    return summary, audio_file_path
-
-#let's communicate with gradio what it has to put in
-iface = gr.Interface(
-    fn=main_function,
-    inputs=gr.File(type="filepath"),  
-    outputs=[gr.Textbox(label="Summary Text"), gr.Audio(label="Summary Audio", type="filepath")],
-    description=description, 
-    examples=examples
-)
-
-#launching the app
-if __name__ == "__main__":
-    iface.launch()