push root

Pushing 'Sms Spam Classification' portfolio project from local (cloud) to remote repository

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+rf_model.sav filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,80 @@
+import pickle
+import re
+import string
+import numpy as np
+import nltk
+import pandas as pd
+import sklearn
+import gradio as gr
+from nltk.stem.porter import PorterStemmer
+from sklearn.feature_extraction.text import CountVectorizer
+from nltk.corpus import stopwords
+
+
+# File Paths
+model_path = 'rf_model.sav' 
+bow_vectorizer_path = "vectorizer"
+tfidf_path = "tfidf_transformer"
+
+# Loading the files
+model = pickle.load(open(model_path, 'rb'))
+bow_vectorizer = pickle.load(open(bow_vectorizer_path, 'rb'))
+tfidf_transformer = pickle.load(open(tfidf_path, 'rb'))
+nltk.download("stopwords")
+
+labels = ["not spam", "spam"]
+
+# declerating the example case
+Examples = [
+    "Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...",
+    "Dear John, thank you for submitting your application for the open position. We appreciate your interest and will be in touch soon regarding next steps",
+    "Hi Sarah, just wanted to follow up on our meeting last week and see if you had any further questions or concerns. Let me know if there's anything else I can help you with.",
+    "Free entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply 08452810075over18's",
+    "Congratulations! You have been selected to receive a free trip to the Bahamas. To claim your prize, simply click on the link below and fill out the registration form.",
+    "URGENT: Your account has been compromised. Please click on the link below to reset your password and secure your account.",
+]
+
+# Util functions
+def text_preprocessing(text):
+
+    # getting the stopwords
+    STOPWORDS = stopwords.words('english') + ['u', 'ü', 'ur', '4', '2', 'im', 'dont', 'doin', 'ure']
+    
+    # selecting non puction words
+    nopunc = [char for char in text if char not in string.punctuation]
+    nopunc = ''.join(nopunc)
+    
+    # clearning and joining the words to create text
+    clean_text = ' '.join([word for word in nopunc.split() if word.lower() not in STOPWORDS])
+    
+    return clean_text
+
+def vector_transformation(sentence):
+  return bow_vectorizer.transform(sentence)
+def tfidf_transformation(bow):
+  return tfidf_transformer.transform(bow)
+
+def predict(text):
+
+  # preparing the input into convenient form
+  sentence = text_preprocessing(text)
+  
+  # converting the text into numerical representation
+  bow = bow_vectorizer.transform([sentence])
+  features = tfidf_transformation(bow)
+
+  # prediction
+  probabilities = model.predict_proba(features) #.predict(features)
+  probs = probabilities.flatten()
+
+  # output form
+  results = {l : np.round(p, 3) for l, p in zip(labels, probs)}
+
+  return results
+
+# GUI Component
+demo = gr.Interface(predict, "text", "label", examples = Examples)
+
+# Launching the demo
+if __name__ == "__main__":
+    demo.launch()

rf_model.sav

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3539929b7acf3379a5876dbefd64b462d82b1e1abd6c19e0546fc73edbf606b2
+size 4778296