Delete app.py

app.py

@@ -1,71 +0,0 @@
-import numpy as np
-import pandas as pd
-import requests
-from bs4 import BeautifulSoup
-
-URL = "https://en.wikisource.org/wiki/The_Verdict"
-page = requests.get(URL)
-soup = BeautifulSoup(page.content, "html.parser")
-# soup.prettify()
-text = [i.text for i in soup.find_all("p")]
-text = text[0:83]
-# with open('Data.txt', 'w') as file:
-#     for string in text:
-#         file.write(string + '\n')
-
-from tensorflow.keras.preprocessing.text import Tokenizer
-tokenizer = Tokenizer(oov_token='<nothing>')
-tokenizer.fit_on_texts(text)
-
-input_sequences = []
-
-for sentences in text:
-  tokenized_sen = tokenizer.texts_to_sequences([sentences])[0]
-  for i in range(1,len(tokenized_sen)):
-    input_sequences.append(tokenized_sen[:i+1])
-
-max_len = max(len(x) for x in input_sequences)
-
-from keras.preprocessing.sequence import pad_sequences
-padded_input_sequences = pad_sequences(input_sequences, maxlen = max_len, padding='pre')
-
-X = padded_input_sequences[:,:max_len-1]
-y = padded_input_sequences[:,-1:]
-
-from tensorflow.keras.utils import to_categorical #OHE
-y = to_categorical(y, num_classes = 1100) # vocal size + 1
-
-from keras.layers import Embedding, Dense, LSTM
-from keras.models import Sequential
-
-model = Sequential()
-model.add(Embedding(1100, 50, input_length = 230))
-model.add(LSTM(250))
-model.add(Dense(1100, activation = 'softmax'))
-model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
-model.summary()
-model.fit(X, y, epochs = 45)
-
-def prediction(t='',l=1):
-  text = t
-  sentence_length = l
-  for repeat in range(sentence_length):
-    token_text = tokenizer.texts_to_sequences([text])
-    padded_token_text = pad_sequences(token_text, maxlen = 230, padding = 'pre')
-    pos = np.argmax(model.predict(padded_token_text))
-    for (word,index) in tokenizer.word_index.items():
-      if index == pos:
-        text = text + " " + word
-  return text
-
-import gradio as gr
-
-demo = gr.Interface(title = "The Verdict",
-                    examples = [['It had always been'], ['I found the couple at'],['She glanced out almost']],
-                    fn=prediction,
-                    inputs=[gr.Textbox(lines = 2, label = 'Query', placeholder='Enter Here', value=""),
-                            gr.Slider(1,100,step = 1, label = "How many Words to generate?", value = 1)],
-                    outputs=gr.Text(lines = 7, ), allow_flagging = 'never', theme=gr.themes.Base())
-
-demo.launch(share = True)
-