Create app.py

app.py

@@ -0,0 +1,37 @@
+import joblib
+import string
+import re
+import nltk
+nltk.download('stopwords')
+from nltk.corpus import stopwords
+from nltk.stem.porter import PorterStemmer
+import gradio as gr
+
+model = joblib.load('/content/gdrive/MyDrive/Colab Notebooks/Email Spam Classifier/naive_bayes_model.sav')
+tfidf = joblib.load('/content/gdrive/MyDrive/Colab Notebooks/Email Spam Classifier/TfIdfVectorizer.sav')
+pipe = joblib.load('/content/gdrive/MyDrive/Colab Notebooks/Email Spam Classifier/pipeline.sav')
+
+def predict_NB(text):
+  text = text.lower()
+  text = re.sub('[^A-Za-z]',' ',text)
+  text = text.translate(str.maketrans('','',string.punctuation))
+  text = ' '.join(word for word in text.split() if word not in stopwords.words('english'))
+  ps = PorterStemmer()
+  text = ' '.join([ps.stem(word) for word in text.split()])
+  X = tfidf.transform([text]).toarray()
+  return 'spam' if model.predict(X)[0] == 1 else 0
+
+def predict_PIPE(text):
+  result = pipe(text)[0]
+  return f'''{'spam' if result['label']=='LABEL_1' else 0}
+confidence : {result['score']}'''
+
+def fn(model_choice, input):
+  if model_choice=="naive-bayes":
+     return predict_NB(input)
+  elif model_choice=="tiny-bert":
+     return predict_PIPE(input)
+
+gr.Interface(fn, inputs = [gr.inputs.Dropdown(["naive-bayes", "tiny-bert"],default = 'naive-bayes'),'text'], 
+             outputs = "text",
+             title = 'Spam Classifier').launch()