Delete app.py

app.py

@@ -1,158 +0,0 @@
-import re
-import nltk
-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 stopwords, 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):
-    nlp = spacy.load('en_core_web_sm')
-    # 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 remove_stopwords(text):
-        stop_words = set(stopwords.words('english'))
-        tokens = word_tokenize(text)
-        filtered_text = [word for word in tokens if word.lower() not in stop_words]
-        return ' '.join(filtered_text)
-    text = remove_stopwords(text)
-    
-    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)