| # importing Libraries | |
| import streamlit as st | |
| import PIL | |
| from PIL import Image | |
| import tensorflow as tf | |
| from nltk.stem import WordNetLemmatizer | |
| from nltk.tokenize import RegexpTokenizer | |
| import re | |
| import string | |
| import numpy as np | |
| import pandas as pd | |
| import nltk | |
| try: # Check if wordnet is installed | |
| nltk.find("corpora/wordnet.zip") | |
| except LookupError: | |
| nltk.download('wordnet') | |
| # ---------------------------------------------------------------------------------- | |
| # read files | |
| try: | |
| acronyms_dict, contractions_dict, stops | |
| except NameError: | |
| acronyms_dict = pd.read_json("helper/acronym.json", typ = "series") | |
| contractions_dict = pd.read_json("helper/contractions.json", typ = "series") | |
| stops = list(pd.read_csv('helper/stopwords.csv').values.flatten()) | |
| # ---------------------------------------------------------------------------------- | |
| # Defining tokenizer | |
| regexp = RegexpTokenizer("[\w']+") | |
| # preprocess Function | |
| def preprocess(text): | |
| text = text.lower() # lowercase | |
| text = text.strip() # whitespaces | |
| # Removing html tags | |
| html = re.compile(r'<.*?>') | |
| text = html.sub(r'', text) # html tags | |
| # Removing emoji patterns | |
| emoji_pattern = re.compile("[" | |
| u"\U0001F600-\U0001F64F" # emoticons | |
| u"\U0001F300-\U0001F5FF" # symbols & pictographs | |
| u"\U0001F680-\U0001F6FF" # transport & map symbols | |
| u"\U0001F1E0-\U0001F1FF" # flags (iOS) | |
| u"\U00002702-\U000027B0" | |
| u"\U000024C2-\U0001F251" | |
| "]+", flags = re.UNICODE) | |
| text = emoji_pattern.sub(r'', text) # unicode char | |
| # Removing urls | |
| http = "https?://\S+|www\.\S+" # matching strings beginning with http (but not just "http") | |
| pattern = r"({})".format(http) # creating pattern | |
| text = re.sub(pattern, "", text) # remove urls | |
| # Removing twitter usernames | |
| pattern = r'@[\w_]+' | |
| text = re.sub(pattern, "", text) # remove @twitter usernames | |
| # Removing punctuations and numbers | |
| punct_str = string.punctuation + string.digits | |
| punct_str = punct_str.replace("'", "") | |
| punct_str = punct_str.replace("-", "") | |
| text = text.translate(str.maketrans('', '', punct_str)) # punctuation and numbers | |
| # Replacing "-" in text with empty space | |
| text = text.replace("-", " ") # "-" | |
| # Substituting acronyms | |
| words = [] | |
| for word in regexp.tokenize(text): | |
| if word in acronyms_dict.index: | |
| words = words + acronyms_dict[word].split() | |
| else: | |
| words = words + word.split() | |
| text = ' '.join(words) # acronyms | |
| # Substituting Contractions | |
| words = [] | |
| for word in regexp.tokenize(text): | |
| if word in contractions_dict.index: | |
| words = words + contractions_dict[word].split() | |
| else: | |
| words = words + word.split() | |
| text = " ".join(words) # contractions | |
| punct_str = string.punctuation | |
| text = text.translate(str.maketrans('', '', punct_str)) # punctuation again to remove "'" | |
| # lemmatization | |
| lemmatizer = WordNetLemmatizer() | |
| text = " ".join([lemmatizer.lemmatize(word) for word in regexp.tokenize(text)]) # lemmatize | |
| # Stopwords Removal | |
| text = ' '.join([word for word in regexp.tokenize(text) if word not in stops]) # stopwords | |
| # Removing all characters except alphabets and " " (space) | |
| filter = string.ascii_letters + " " | |
| text = "".join([chr for chr in text if chr in filter]) # remove all characters except alphabets and " " (space) | |
| # Removing words with one alphabet occuring more than 3 times continuously | |
| pattern = r'\b\w*?(.)\1{2,}\w*\b' | |
| text = re.sub(pattern, "", text).strip() # remove words with one alphabet occuring more than 3 times continuously | |
| # Removing words with less than 3 characters | |
| short_words = r'\b\w{1,2}\b' | |
| text = re.sub(short_words, "", text) # remove words with less than 3 characters | |
| # return final output | |
| return text | |
| # ================================================================================================================================================================ | |
| # STREAMLIT | |
| # ================================================================================================================================================================ | |
| # App Devolopment Starts | |
| st.set_page_config(layout="wide") | |
| st.write("# A Predictive Analysis of Disaster Tweets") | |
| img = Image.open("images/t2.png") | |
| st.image(img) | |
| tweet = st.text_input(label = "Type or paste your tweet here", value = "") | |
| # Defining a function to store the model in streamlit cache memory | |
| def cache_model(model_name): | |
| model = tf.keras.models.load_model(model_name) | |
| return model | |
| model = cache_model("model/tweet_model") #--------------------------- model | |
| # if user gives any input | |
| if len(tweet) > 0: | |
| clean_tweet = preprocess(tweet) # cleans tweet | |
| y_pred = model.predict([clean_tweet]) # gives probability of class = 1 | |
| y_pred_num = int(np.round(y_pred)[0][0]) # get final prediction of output class | |
| if y_pred_num == 0: | |
| # st.write(f"#### Non-Disaster tweet with disaster probability {round(y_pred[0][0]*100, 4)}%") | |
| st.write(f"#### 🌞🌞This tweet is not flagged as a disaster, but with a probability of {round(y_pred[0][0]*100, 4)}% that it might be. ") | |
| else: | |
| st.write(f"#### 🚩🚩High probability ( {round(y_pred[0][0]*100, 4)}%) indicates that this tweet is related to a disaster🚨🚨.") | |
| # ================================================================================================================================================================ | |
| # -------------------------------------------------------------------- Example of Tweets ----------------------------------------------------------------------- | |
| # ================================================================================================================================================================ | |
| # ---------------------------- Disaster Tweets ------------------------------- | |
| # "🚨 Just felt a strong earthquake! Stay safe everyone! #earthquake #safetyfirst" [93.62] | |
| # "⚠️ Urgent: Massive wildfire approaching our community. Evacuation orders in effect. Please heed warnings and evacuate immediately. #wildfire #safety" [99.30] | |
| # "🌪️ Tornado warning in effect for our area. Take shelter now! #tornadowarning #safetyfirst" [92.84] | |
| # "🌊 Coastal areas under tsunami alert. Seek higher ground immediately! #tsunami #emergencyalert" [99.54] | |
| # ---------------------------- Non disaster Tweets ------------------------------- | |
| # "Enjoying a peaceful evening with a good book and a cup of tea. #Relaxation" [4.52] | |
| # "Excited for the weekend! Planning a movie night with friends. 🍿🎬 #FridayFeeling" [3.27] | |
| # "Just finished a great workout session at the gym. Feeling energized! 💪 #FitnessGoals" [6.17] | |
| # "Spent the day exploring a new hiking trail. Nature is so beautiful! 🌳 #OutdoorAdventure" [19.44] | |
| # "Cooked a delicious homemade dinner tonight. #Foodie #HomeChef" [7.1] |