Upload app.py

app.py

@@ -0,0 +1,50 @@
+import pandas as pd
+import numpy as np
+import json
+import gradio as gr
+from keras.models import load_model
+from keras.preprocessing.text import tokenizer_from_json, Tokenizer
+from keras.preprocessing.sequence import pad_sequences
+import spacy
+from string import punctuation
+import re
+
+import en_core_web_sm
+nlp = en_core_web_sm.load()
+
+stopwords = nlp.Defaults.stop_words
+
+def clean_text(text):
+
+    text = text.translate(punctuation)
+
+    text = re.sub(r"[^\w\s]", " ",text)
+    text = re.sub(r"[^A-Za-z0-9^,!.\/'+-=]", " ",text)
+
+    doc = nlp(text)
+    
+    text = [token for token in doc if str(token) not in stopwords]
+    lemmatized = [token.lemma_ for token in text]
+    
+    text = ' '.join(lemmatized)
+
+    return text
+
+with open("tokenizer.json", "r") as read_file:
+   tokenizer = json.load(read_file)
+
+tokenizer = tokenizer_from_json(tokenizer)   
+
+model = load_model('tweets_disaster_model.h5')
+
+def tweets_predictions(text):
+    text = clean_text(text)
+    text = tokenizer.texts_to_sequences([text])
+    text = pad_sequences(text, padding='post', maxlen=50)
+    pred = model.predict(text.reshape(1,-1)).tolist()[0]
+    dic = {}
+    dic['No disaster'] = 1 - pred[0]
+    dic['Disaster'] = pred[0]
+    return dic
+
+interface = gr.Interface(fn=tweets_predictions, inputs='textbox', outputs='label', theme='darkdefault').launch(share=True)