Delete app.py

app.py

@@ -1,77 +0,0 @@
-# https://huggingface.co/spaces/itsmariamaraki/AAI-Assessment3
-
-# Here are the imports
-
-import gradio as gr
-import PyPDF2
-from PyPDF2 import PdfReader
-from pdfminer.high_level import extract_pages, extract_text
-from transformers import pipeline, AutoProcessor, AutoModel, AutoTokenizer
-import torch
-import soundfile as sf
-from IPython.display import Audio
-from datasets import load_dataset
-from io import BytesIO
-import os
-
-# Here is the code
-
-def abstract(pdf_file):
-    pdf_bytes = BytesIO(pdf_file)
-    pdf_reader = PyPDF2.PdfReader(pdf_bytes)
-    
-    abstract = ''
-    
-    for page_number in range(len(pdf_reader.pages)):
-        text = pdf_reader.pages[page_number].extract_text()
-        
-        if 'abstract' in text.lower(): #in order to read only the abstract, i set as a start the abstract point & as an end the introduction point
-            start_index = text.lower().find('abstract')  
-            end_index = text.lower().find('introduction')
-            abstract = text[start_index:end_index]
-            break
-    
-    return abstract
-
-
-
-summarization = pipeline('summarization', model = 'pszemraj/long-t5-tglobal-base-16384-book-summary') #best summarization model i tested regarding this assessment
-audiospeech = pipeline('text-to-speech', model = 'suno/bark-small') #the voice is a bit distorted but gives a good output & takes less time
-
-
-
-def summarization_n_audiospeech(pdf_file):
-    abstract_text = abstract(pdf_file)
-
-    summary = summarization(abstract_text, max_length=50, min_length=10)[0]['summary_text'] #didn't know exactly what would give one sentence, so i checked multiple times the min & max lengths regarding the 11th article. for a dif article, those parameters would probably have to be different as well
-          
-    fin_summary = summary.split('.')[0] + '.' #extract and print only the first sentence of the summary
-
-    #converting the summarization into an audio output
-    tts_output = audiospeech(fin_summary)
-    audio_data = tts_output['audio'][0]
-    
-    with BytesIO() as buffer:
-        sf.write(buffer, audio_data, 16000, format = 'wav')
-        audio_bytes = buffer.getvalue()
-
-    return fin_summary, audio_bytes
-
-
-
-iface = gr.Interface(
-    fn = summarization_n_audiospeech,
-    inputs = gr.File(label='upload PDF', type='binary'), #if i didn't set a type, the gradio output was an error -  searched it online for the solution
-    outputs = [
-        gr.Textbox(label='Summarization of the Abstract:'), 
-        gr.Audio(label="Audio Speech of the Abstract's Summary:")
-        ],
-    title = "PDF's Abstract Summarization & Audio Speech Processor",
-    description = "App that generates a one-line summary of the abstract & a speech audio of this summarization -- requirements: app only accepts PDFs which include an ABSTRACT section",
-        examples = [os.path.join(os.path.dirname(__file__),  'Hidden_Technical_Debt.pdf'), 
-                os.path.join(os.path.dirname(__file__),  'Semiconductors.pdf'), 
-                os.path.join(os.path.dirname(__file__),  'Efficient_Estimation_of_Word_Representations.pdf') 
-               ]
-)
-
-iface.launch()