Upload app.py

app.py

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+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):
+    # 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)