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app/app.py

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+import gradio as gr
+import pandas as pd
+import nltk
+import pickle
+import tensorflow as tf
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.models import load_model
+
+def cleaning_text(stop_words, tokenizer, title, text):
+    text = title + text
+    text.lower().replace('[^A-Za-z0-9\s]', '')
+
+    text = " ".join([word for word in text.split() if word not in stop_words])
+    df_text = pd.DataFrame({'new': text}, index=[0])
+
+    sequence = tokenizer.texts_to_sequences(df_text['new'])
+    print(len(sequence))
+    padded_sequence = pad_sequences(sequence, maxlen=600, padding='post', truncating='post')
+
+    return padded_sequence
+
+nltk.download('stopwords')
+from nltk.corpus import stopwords
+stop_words = stopwords.words('english')
+
+# load Tokenizer
+with open('tokenizer.pkl', 'rb') as f:
+    tokenizer = pickle.load(f)
+
+# load model
+model_lstm = load_model('model_lstm_1.0.h5')
+
+def detecter(title, text, stop_words, tokenizer, model):
+    seq = cleaning_text(stop_words, tokenizer, title=title, text=text)
+    pred = model.predict(seq)[0, 0]
+    if pred > 0.5:
+        return "True!"
+    elif pred <= 0.5:
+        return "False!"
+    else:
+        return "Please try again."
+
+title = gr.Textbox(label="Input the news title")
+text = gr.Textbox(label="Input the news full content")
+
+stop_words = stop_words
+tokenizer = tokenizer
+model = model_lstm
+
+imf = gr.Interface(fn=lambda input1, input2: detecter(input1, input2, stop_words, tokenizer, model), inputs=[title, text], outputs="text")
+
+if __name__ == "__main__":
+    imf.launch()