Update app.py

app.py

@@ -1,6 +1,6 @@
 import os
 import gradio as gr
-from transformers import pipeline, BertTokenizer, BertModel
+from transformers import pipeline
 import spacy
 import lib.read_pdf
 import pandas as pd
@@ -12,18 +12,6 @@ import io
 nlp = spacy.load('en_core_web_sm')
 nlp.add_pipe('sentencizer')
 
-#bert_model_name = "bert-base-uncased"
-#tokenizer = BertTokenizer.from_pretrained(bert_model_name)
-#model = BertModel.from_pretrained(bert_model_name)
-#model.eval()
-
-#import torch
-#import numpy as np
-#from sklearn.metrics.pairwise import cosine_similarity
-
-
-
-
 def split_in_sentences(text):
     doc = nlp(text)
     return [str(sent).strip() for sent in doc.sents]
@@ -242,8 +230,6 @@ with gr.Blocks() as demo:
             with gr.Column():
                 gr.Markdown("### PDF 1 Analysis")
                 selected_paragraph_1 = gr.Textbox(label="Selected Paragraph 1 Content", lines=4)
-                #selected_paragraph_btn1 = gr.Button("Nearest paragraph content from pdf 2")
-                #selected_paragraph_btn1.click(fn=lambda p: compare_paragraph_to_list(p, stored_paragraphs_2), inputs=paragraph_1_dropdown, outputs=selected_paragraph_1)
                 summarize_btn1 = gr.Button("Summarize Text from PDF 1")
                 summary_textbox_1 = gr.Textbox(label="Summary for PDF 1", lines=2)
                 summarize_btn1.click(fn=lambda p: process_paragraph_1_sum(p), inputs=paragraph_1_dropdown, outputs=summary_textbox_1)