Update app.py

app.py

@@ -247,62 +247,3 @@ for key in text_per_page.keys(): # go through keys in dictionary
       break
 print(abstract_from_pdf)
 
-from transformers import pipeline
-summarizer = pipeline("summarization", model="ainize/bart-base-cnn")
-#summarizer = pipeline("summarization", model="linydub/bart-large-samsum") # various models were tried and the best one was selected
-#summarizer = pipeline("summarization", model="slauw87/bart_summarisation")
-#summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
-#summarizer = pipeline("summarization", model="google/pegasus-cnn_dailymail")
-#print(summarizer(abstract_from_pdf, max_length=50, min_length=5, do_sample=False))
-summarized_text=(summarizer(abstract_from_pdf))
-print(summarized_text)
-#summary_of_abstract=str(summarizer)
-#type(summary_of_abstract)
-#print(summary_of_abstract)
-
-# the aim of this section of code is to get a summary of just one sentence by summarizing the summary all while the summary is longer than one sentence.
-# unfortunately, I tried many many models and none of them actually summarize the text to as short as one sentence.
-#I had searched for ways to fine tune the summarization model to specify that the summarization should be done in just one sentence but did not find a way to implement it
-from transformers import pipeline
-summarized_text_list_list=summarized_text_list['summary_text']
-summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
-#print(summarizer)
-number_of_sentences=summarized_text_list_list.count('.')
-print(number_of_sentences)
-while(number_of_sentences)>1:
-    print(number_of_sentences)
-    summarized_text_list_list=summarizer(summarized_text_list_list)[0]['summary_text']
-    number_of_sentences-=1
-    print(summarized_text_list_list)
-    print(number_of_sentences)
-
-
-#text to speech
-#!pip install git+https://github.com/huggingface/transformers.git
-#!pip install datasets sentencepiece
-import torch
-import soundfile as sf
-from IPython.display import Audio
-from datasets import load_dataset
-from transformers import pipeline
-from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech
-processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
-model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
-#text = "The future belongs to those who believe in the beauty of their dreams."
-#text = (summarized_text_list_list)
-
-inputs = processor(text=summarized_text_list_list, return_tensors="pt")
-from datasets import load_dataset
-embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
-
-import torch
-speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
-spectrogram = model.generate_speech(inputs["input_ids"], speaker_embeddings)
-from transformers import SpeechT5HifiGan
-vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
-with torch.no_grad():
-    speech = vocoder(spectrogram)
-speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
-Audio(speech, rate=16000)
-
-