Delete app.py

app.py

@@ -1,77 +0,0 @@
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-import pickle
-import torch
-
-
-import io
-
-
-#contents = pickle.load(f) becomes...
-#contents = CPU_Unpickler(f).load()
-
-
-model_path = "t5_10k_small_cpu.sav"
-
-#load model from drive
-with open(model_path, "rb") as f:
-    model=  pickle.load(f)
-    
-
-#tokenizer = AutoTokenizer.from_pretrained(checkpoint)
-#model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
-
-
-import nltk
-from finbert_embedding.embedding import FinbertEmbedding
-import pandas as pd
-from nltk.cluster import KMeansClusterer
-import numpy as np
-import os
-from scipy.spatial import distance_matrix
-from tensorflow.python.lib.io import file_io
-import pickle
-
-nltk.download('punkt')
-
-
-import itertools
-
-def make_abstractive(word):
-    import os
-    data_path = "/tmp/"
-    if not os.path.exists(data_path):
-        os.makedirs(data_path)
-    input_ = "/tmp/input.txt"
-
-    with open(input_, "w") as file:
-        file.write(word)
-    # read the written txt into a variable
-    with open(input_ , 'r') as f:
-        text_ = f.read()
-
-    def clean_data(texts):
-        import re
-        words = list()
-        for text in texts.split():
-            text = re.sub(r'\n','',text)
-            text = re.sub(r'\s$','',text)
-            words.append(text)
-
-        return "summarize " + " ".join(words)
-    text = clean_data(text_)
-
-    final_summary = []
-    for x in range(0,len(text)-1256,1256):
-        text_to_summarize= text[x:x+1256]
-        final_summary.append(model.predict(text_to_summarize))
-
-    final_list = list(itertools.chain.from_iterable(final_summary))
-    final_list = ''.join(final_list)
-    return final_list
-    
-import gradio as gr
-
-iface = gr.Interface(fn= make_abstractive, 
-                     inputs =gr.inputs.Textbox(lines=15,placeholder="Enter your text !!"),
-                     outputs="text",title="Document Summarizer",description ="An AI that makes your life easier by helping you summarise long texts.")
-iface.launch()