Delete app.py

app.py

@@ -1,151 +0,0 @@
-import re
-import nltk
-nltk.download('all')
-import keras
-import spacy
-import string 
-import pickle
-import tempfile
-import numpy as np
-import gradio as gr
-import contractions
-import tensorflow as tf
-from nltk.stem import WordNetLemmatizer
-from nltk.tokenize import word_tokenize
-from nltk.corpus import wordnet
-from tensorflow.keras.layers import Layer
-from tensorflow.keras import backend as K
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-
-
-class Attention(Layer):
- 
- def __init__(self, return_sequences=True, **kwargs):
- self.return_sequences = return_sequences
- super(Attention, self).__init__(**kwargs)
-
- def build(self, input_shape):
- 
- self.W=self.add_weight(name="att_weight", shape=(input_shape[-1],1),
- initializer="normal")
- self.b=self.add_weight(name="att_bias", shape=(input_shape[1],1),
- initializer="zeros")
- 
- super(Attention,self).build(input_shape)
- 
- def call(self, x):
- 
- e = K.tanh(K.dot(x,self.W)+self.b)
- a = K.softmax(e, axis=1)
- output = x*a
- 
- if self.return_sequences:
- return output
- 
- return K.sum(output, axis=1)
- 
-
-
-def load_tokenizer(path):
- with open(path, 'rb') as f:
- tokenizer = pickle.load(f)
- return tokenizer
-
-
-def cleaning(text):
- # Punctuation symbols to remove
- exclude = string.punctuation
- 
- def expand_contractions(text): return contractions.fix(text)
- text = expand_contractions(text)
-
- text = text.lower()
- 
- def remove_tags(text): return re.sub(r'@\w*', ' ' , text)
- text = remove_tags(text)
- 
- def remove_hashtags(text): return re.sub(r'#\w*', ' ' , text)
- text = remove_hashtags(text)
- 
- def remove_apostrophe(text): return re.sub(r"'s\b", "", text)
- text = remove_apostrophe(text)
-
- def remove_special_chars(text): return re.sub(r"[^a-zA-Z0-9\s]", ' ', text)
- text = remove_special_chars(text)
- 
- def remove_number(text): return re.sub(r'[\d]', ' ', text)
- text = remove_number(text)
- 
- def remove_punc(text): return ''.join([c for c in text if c not in exclude])
- text = remove_punc(text)
- 
- def remove_extra_spaces(text): return re.sub('^\S', ' ', text)
- text = remove_extra_spaces(text)
- 
- def map_pos_tags(pos_tags):
- # Map NLTK POS tags to WordNet tags
- tag_map = {
- 'N': wordnet.NOUN,
- 'V': wordnet.VERB,
- 'R': wordnet.ADV,
- 'J': wordnet.ADJ
- }
- 
- mapped_tags = []
- for token, tag in pos_tags:
- mapped_tag = tag[0].upper()
- if mapped_tag in tag_map:
- mapped_tag = tag_map[mapped_tag]
- else:
- mapped_tag = wordnet.NOUN # Default to noun if no mapping found
- mapped_tags.append(mapped_tag)
-
- return mapped_tags
- 
- def pos_tag_and_lemmatize(text):
- tokens = word_tokenize(text)
- pos_tags = nltk.pos_tag(tokens)
- 
- # Map POS tags to WordNet tags
- wordnet_tags = map_pos_tags(pos_tags)
-
- # Lemmatize based on POS tags
- lemmatizer = WordNetLemmatizer()
- lemmas = " ".join([lemmatizer.lemmatize(token, tag) for token, tag in zip(tokens, wordnet_tags)])
-
- return lemmas
- text = pos_tag_and_lemmatize(text)
-
- return text
-
-
-def label_tweet(test_review):
- token_list = tokenizer.texts_to_sequences([test_review])[0]
- token_list = pad_sequences([token_list], maxlen=44, padding='post')
- predicted = model.predict(token_list, verbose=0)
- if predicted >= 0.5:
- return 1
- else: 
- return 0
-
-
-def analyze_text(comment):
- comment = cleaning(comment)
- result = label_tweet(comment)
- if result == 0: 
- text = "Negative"
- else:
- text = "Positive"
- return text
-
-
-# It can be used to reconstruct the model identically.
-model = keras.models.load_model("twitter_sentiment.keras",
- custom_objects={'Attention': Attention})
-
-# Load tokenizer
-tokenizer = load_tokenizer('tokenizer.pkl')
-
-interface = gr.Interface(fn=analyze_text, inputs=gr.inputs.Textbox(lines=2, placeholder='Enter a positive or negative tweet here...'),
- outputs='text',title='Twitter Sentimental Analysis', theme='darkhuggingface')
-interface.launch(inline=False)