Update app.py

app.py

@@ -1,94 +1,93 @@
-import gradio as gr
-import pandas as pd
-import numpy as np
-import re
-import pickle
-from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
-from sklearn.preprocessing import LabelEncoder
-from sklearn.metrics import accuracy_score
-from nltk.corpus import stopwords
-from nltk.stem import WordNetLemmatizer
-import inflect
-
-# Load the tokenizer, label encoder, and model
-def load_resources():
-    tokenizer = AutoTokenizer.from_pretrained('./transformer_tokenizer')
-    with open('./label_encoder_tf.pickle', 'rb') as handle:
-        encoder = pickle.load(handle)
-    model = TFAutoModelForSequenceClassification.from_pretrained('./transformer_model')
-    return tokenizer, encoder, model
-
-tokenizer, encoder, model = load_resources()
-
-# Preprocessing functions
-def expand_contractions(text, contractions_dict):
-    contractions_pattern = re.compile('({})'.format('|'.join(contractions_dict.keys())), flags=re.IGNORECASE | re.DOTALL)
-    def expand_match(contraction):
-        match = contraction.group(0)
-        first_char = match[0]
-        expanded_contraction = contractions_dict.get(match.lower(), match)
-        return first_char + expanded_contraction[1:]
-    expanded_text = contractions_pattern.sub(expand_match, text)
-    return re.sub("'", "", expanded_text)
-
-def convert_numbers_to_words(text):
-    p = inflect.engine()
-    words = text.split()
-    return ' '.join([p.number_to_words(word) if word.isdigit() else word for word in words])
-
-def preprocess_text(text):
-    contractions_dict = {
-        "ain't": "am not", "aren't": "are not", "can't": "cannot", "can't've": "cannot have", "'cause": "because",
-        "could've": "could have", "couldn't": "could not", "couldn't've": "could not have", "didn't": "did not",
-        "doesn't": "does not", "don't": "do not", "hadn't": "had not", "hadn't've": "had not have", "hasn't": "has not",
-        "haven't": "have not", "he'd": "he had", "he'd've": "he would have", "he'll": "he will", "he'll've": "he will have",
-        "he's": "he is", "how'd": "how did", "how'd'y": "how do you", "how'll": "how will", "how's": "how is",
-        "I'd": "I had", "I'd've": "I would have", "I'll": "I will", "I'll've": "I will have", "I'm": "I am", "I've": "I have",
-        "isn't": "is not", "it'd": "it had", "it'd've": "it would have", "it'll": "it will", "it'll've": "it will have",
-        "it's": "it is", "let's": "let us", "ma'am": "madam", "mayn't": "may not", "might've": "might have", "mightn't": "might not",
-        "mightn't've": "might not have", "must've": "must have", "mustn't": "must not", "mustn't've": "must not have",
-        "needn't": "need not", "needn't've": "need not have", "o'clock": "of the clock", "oughtn't": "ought not",
-        "oughtn't've": "ought not have", "shan't": "shall not", "sha'n't": "shall not", "shan't've": "shall not have",
-        "she'd": "she had", "she'd've": "she would have", "she'll": "she will", "she'll've": "she will have", "she's": "she is",
-        "should've": "should have", "shouldn't": "should not", "shouldn't've": "should not have", "so've": "so have",
-        "so's": "so is", "that'd": "that had", "that'd've": "that would have", "that's": "that is", "there'd": "there had",
-        "there'd've": "there would have", "there's": "there is", "they'd": "they had", "they'd've": "they would have",
-        "they'll": "they will", "they'll've": "they will have", "they're": "they are", "they've": "they have", "to've": "to have",
-        "wasn't": "was not", "we'd": "we had", "we'd've": "we would have", "we'll": "we will", "we'll've": "we will have",
-        "we're": "we are", "we've": "we have", "weren't": "were not", "what'll": "what will", "what'll've": "what will have",
-        "what're": "what are", "what's": "what is", "what've": "what have", "when's": "when is", "when've": "when have",
-        "where'd": "where did", "where's": "where is", "where've": "where have", "who'll": "who will", "who'll've": "who will have",
-        "who's": "who is", "who've": "who have", "why's": "why is", "why've": "why have", "will've": "will have", "won't": "will not",
-        "won't've": "will not have", "would've": "would have", "wouldn't": "would not", "wouldn't've": "would not have",
-        "y'all": "you all", "y'all'd": "you all would", "y'all'd've": "you all would have", "y'all're": "you all are",
-        "y'all've": "you all have", "you'd": "you had", "you'd've": "you would have", "you'll": "you will", "you'll've": "you will have",
-        "you're": "you are", "you've": "you have"
-    }
-    text = text.lower()
-    text = expand_contractions(text, contractions_dict)
-    text = convert_numbers_to_words(text)
-    text = re.sub(r'[^\w\s]', '', text)
-    stop_words = set(stopwords.words('english'))
-    text = ' '.join([word for word in text.split() if word not in stop_words])
-    lemmatizer = WordNetLemmatizer()
-    text = ' '.join([lemmatizer.lemmatize(word) for word in text.split()])
-    return text
-
-# Define the prediction function
-def predict_spam(text):
-    preprocessed_text = preprocess_text(text)
-    encoding = tokenizer(preprocessed_text, return_tensors='tf', truncation=True, padding=True)
-    prediction = model(encoding).logits
-    predicted_label = np.argmax(prediction, axis=1)
-    decoded_label = encoder.inverse_transform(predicted_label)
-    return decoded_label[0]
-
-# Create the Gradio interface
-iface = gr.Interface(fn=predict_spam, 
-                     inputs=gr.inputs.Textbox(lines=2, placeholder="Enter SMS message here..."), 
-                     outputs="text", 
-                     title="SMS Spam Classification with Transformer Model", 
-                     description="Enter an SMS message to classify it as spam or ham.")
-
-# Launch the interface
+import gradio as gr
+import pandas as pd
+import numpy as np
+import re
+import pickle
+from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
+from sklearn.preprocessing import LabelEncoder
+from nltk.corpus import stopwords
+from nltk.stem import WordNetLemmatizer
+import inflect
+
+# Load the tokenizer, label encoder, and model
+def load_resources():
+    tokenizer = AutoTokenizer.from_pretrained('./transformer_tokenizer')
+    with open('./label_encoder_tf.pickle', 'rb') as handle:
+        encoder = pickle.load(handle)
+    model = TFAutoModelForSequenceClassification.from_pretrained('./transformer_model')
+    return tokenizer, encoder, model
+
+tokenizer, encoder, model = load_resources()
+
+# Preprocessing functions
+def expand_contractions(text, contractions_dict):
+    contractions_pattern = re.compile('({})'.format('|'.join(contractions_dict.keys())), flags=re.IGNORECASE | re.DOTALL)
+    def expand_match(contraction):
+        match = contraction.group(0)
+        first_char = match[0]
+        expanded_contraction = contractions_dict.get(match.lower(), match)
+        return first_char + expanded_contraction[1:]
+    expanded_text = contractions_pattern.sub(expand_match, text)
+    return re.sub("'", "", expanded_text)
+
+def convert_numbers_to_words(text):
+    p = inflect.engine()
+    words = text.split()
+    return ' '.join([p.number_to_words(word) if word.isdigit() else word for word in words])
+
+def preprocess_text(text):
+    contractions_dict = {
+        "ain't": "am not", "aren't": "are not", "can't": "cannot", "can't've": "cannot have", "'cause": "because",
+        "could've": "could have", "couldn't": "could not", "couldn't've": "could not have", "didn't": "did not",
+        "doesn't": "does not", "don't": "do not", "hadn't": "had not", "hadn't've": "had not have", "hasn't": "has not",
+        "haven't": "have not", "he'd": "he had", "he'd've": "he would have", "he'll": "he will", "he'll've": "he will have",
+        "he's": "he is", "how'd": "how did", "how'd'y": "how do you", "how'll": "how will", "how's": "how is",
+        "I'd": "I had", "I'd've": "I would have", "I'll": "I will", "I'll've": "I will have", "I'm": "I am", "I've": "I have",
+        "isn't": "is not", "it'd": "it had", "it'd've": "it would have", "it'll": "it will", "it'll've": "it will have",
+        "it's": "it is", "let's": "let us", "ma'am": "madam", "mayn't": "may not", "might've": "might have", "mightn't": "might not",
+        "mightn't've": "might not have", "must've": "must have", "mustn't": "must not", "mustn't've": "must not have",
+        "needn't": "need not", "needn't've": "need not have", "o'clock": "of the clock", "oughtn't": "ought not",
+        "oughtn't've": "ought not have", "shan't": "shall not", "sha'n't": "shall not", "shan't've": "shall not have",
+        "she'd": "she had", "she'd've": "she would have", "she'll": "she will", "she'll've": "she will have", "she's": "she is",
+        "should've": "should have", "shouldn't": "should not", "shouldn't've": "should not have", "so've": "so have",
+        "so's": "so is", "that'd": "that had", "that'd've": "that would have", "that's": "that is", "there'd": "there had",
+        "there'd've": "there would have", "there's": "there is", "they'd": "they had", "they'd've": "they would have",
+        "they'll": "they will", "they'll've": "they will have", "they're": "they are", "they've": "they have", "to've": "to have",
+        "wasn't": "was not", "we'd": "we had", "we'd've": "we would have", "we'll": "we will", "we'll've": "we will have",
+        "we're": "we are", "we've": "we have", "weren't": "were not", "what'll": "what will", "what'll've": "what will have",
+        "what're": "what are", "what's": "what is", "what've": "what have", "when's": "when is", "when've": "when have",
+        "where'd": "where did", "where's": "where is", "where've": "where have", "who'll": "who will", "who'll've": "who will have",
+        "who's": "who is", "who've": "who have", "why's": "why is", "why've": "why have", "will've": "will have", "won't": "will not",
+        "won't've": "will not have", "would've": "would have", "wouldn't": "would not", "wouldn't've": "would not have",
+        "y'all": "you all", "y'all'd": "you all would", "y'all'd've": "you all would have", "y'all're": "you all are",
+        "y'all've": "you all have", "you'd": "you had", "you'd've": "you would have", "you'll": "you will", "you'll've": "you will have",
+        "you're": "you are", "you've": "you have"
+    }
+    text = text.lower()
+    text = expand_contractions(text, contractions_dict)
+    text = convert_numbers_to_words(text)
+    text = re.sub(r'[^\w\s]', '', text)
+    stop_words = set(stopwords.words('english'))
+    text = ' '.join([word for word in text.split() if word not in stop_words])
+    lemmatizer = WordNetLemmatizer()
+    text = ' '.join([lemmatizer.lemmatize(word) for word in text.split()])
+    return text
+
+# Define the prediction function
+def predict_spam(text):
+    preprocessed_text = preprocess_text(text)
+    encoding = tokenizer(preprocessed_text, return_tensors='tf', truncation=True, padding=True)
+    prediction = model(encoding).logits
+    predicted_label = np.argmax(prediction, axis=1)
+    decoded_label = encoder.inverse_transform(predicted_label)
+    return decoded_label[0]
+
+# Create the Gradio interface
+iface = gr.Interface(fn=predict_spam, 
+                     inputs=gr.Textbox(lines=2, placeholder="Enter SMS message here..."), 
+                     outputs="text", 
+                     title="SMS Spam Classification with Transformer Model", 
+                     description="Enter an SMS message to classify it as spam or ham.")
+
+# Launch the interface
 iface.launch()