Create app.py

app.py

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+import pickle as pkl
+
+import re
+import numpy as np
+
+from nltk.stem import PorterStemmer
+from textblob import Word
+from nltk.corpus import stopwords
+
+def process_row(row):
+    from string import punctuation
+    row = re.sub('(\S+@\S+)(com|\s+com)', ' ', row)
+    row = re.sub('(\S+@\S+)', ' ', row)
+    punctuation = punctuation + '\n' + '—“,”‘-’' + '0123456789'
+    row = ''.join(word for word in row if word not in punctuation)
+    row = row.lower()
+    stop = stopwords.words('english')
+    row = ' '.join(word for word in row.split() if word not in stop ) 
+    row = " ".join([Word(word).lemmatize() for word in row.split()])
+    ps = PorterStemmer()
+    row = " ".join([ps.stem(word) for word in row.split()])
+    row = re.sub('\s{1,}', ' ', row)
+    row = " ".join([word for word in row.split() if len(word) > 2])
+    return row
+
+def predict_class(doc):
+  model = pkl.load(open("logistic_model.pk","rb"))
+  vectorizer = pkl.load(open("tfidf_vectorizer.pk","rb"))
+  clean_doc=process_row(doc)
+  vector =np.array(vectorizer.transform([clean_doc]).todense())
+  class_pred = model.predict(vector)
+  # print(class_pred[0])
+  return class_pred[0]
+
+# predict_class(" Barack Obama is seeking for a conference to be conducted in USA")
+
+import gradio as gr
+iface = gr.Interface(fn = predict_class,
+                     inputs = gr.Textbox(type="text", label="Enter Your Document"), 
+                    #  outputs = ["text", "text"], 
+                     outputs = [
+                         gr.Textbox(type="text", value=". . . ", label="Predicted Class")     
+                     ],
+                     title = "News Class Predictor", 
+                     description ="Predicts whether the News belongs to 'Politics'class or 'Sports' class")
+iface.launch()