Delete app.py

app.py

@@ -1,77 +0,0 @@
-!pip install transformers pyPDF2 torchaudio
-
-!pip install pdfminer.six
-
-!pip install datasets sentencepiece
-
-from google.colab import drive
-from transformers import pipeline
-import PyPDF2
-
-from pdfminer.high_level import extract_pages, extract_text
-
-from pdfminer.layout import LTTextContainer, LTChar
-
-drive.mount('/content/drive')
-
-pdf_path = '/content/drive/MyDrive/Applied AI/Assessment_3/Article 11 Hidden Technical Debt in Machine Learning Systems.pdf'
-
-summarization = pipeline ('summarization', model = "pszemraj/long-t5-tglobal-base-16384-book-summary")
-
-# Open the PDF file
-pdf_file = open(pdf_path, 'rb')
-pdf_reader = PyPDF2.PdfReader(pdf_file)
-
-# Extract text from the Abstract section
-abstract_text = pdf_reader.pages[0].extract_text()
-
-# Close the PDF file
-pdf_file.close()
-
-summary = summarization(abstract_text, max_length=13, min_length=10)[0]['summary_text']
-
-print(summary)
-
-!pip install --upgrade transformers sentencepiece datasets[audio]
-
-import torch
-
-import soundfile as sf
-
-from IPython.display import Audio
-
-from datasets import load_dataset
-
-synthesiser = pipeline("text-to-speech", "facebook/mms-tts-eng")
-
-TTS_Output = synthesiser(summary)
-
-print(TTS_Output.keys())
-
-
-audio_key = TTS_Output["audio"]
-
-
-Audio(data=audio_key[0], rate=16000)
-
-!pip install gradio==2.3.6
-!pip install --upgrade typing-extensions
-
-import gradio as gr
-
-def summarize_pdf(pdf_path):
-    pdf_file = open(pdf_path, 'rb')
-    pdf_reader = PyPDF2.PdfReader(pdf_file)
-    abstract_text = pdf_reader.pages[0].extract_text()
-    summary = summarization(abstract_text, max_length=13, min_length=10)[0]['summary_text']
-    pdf_file.close()
-    return summary
-    
-iface = gr.Interface(
-    fn=summarize_pdf,
-    inputs= "file",
-    outputs="text",
-    live=True,
-    title="PDF Summarizer",
-    description="Upload a PDF with an abstract, and the model will generate a summary."
-)