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NLP_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:94581e03b8a93788ab8df45d21834621b95437b20725aab66b0938b88ed02623
+size 616345

app.py

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+import streamlit as st
+import pickle
+import re
+import nltk
+from nltk.corpus import stopwords
+from nltk.stem import PorterStemmer
+#from sklearn.feature_extraction.text import CountVectorizer
+#from sklearn.naive_bayes import BernoulliNB
+
+# Load the saved model and vectorizer
+with open("NLP_model.pkl", "rb") as file:
+    model, vect = pickle.load(file)
+
+# Define a function to preprocess the user input
+def preprocess_text(text):
+    text = re.sub(r'<.*?>', '', text)
+    text = re.sub(r'http\S+', '', text)
+    text = re.sub(r'[^a-zA-Z\s]', '', text)
+    text = text.lower()
+    tokens = nltk.word_tokenize(text)
+    stop_words = set(stopwords.words('english'))
+    tokens = [word for word in tokens if word not in stop_words]
+    stemmer = PorterStemmer()
+    tokens = [stemmer.stem(word) for word in tokens]
+    preprocessed_text = ' '.join(tokens)
+    return preprocessed_text
+
+
+st.title("Emergency Tweet Classifier 🐦")
+st.image("https://img.gta5-mods.com/q95/images/emergency-utility-truck/b30de4-Station%2032.jpg")
+st.write("This NLP model has 79% accuracy. Enter a tweet to determine if there is an emergency or not.")
+
+# User input text box with smaller height
+user_input = st.text_area("Write Tweet Here:", value="13,000 people receive evacuation.")
+
+# Button to classify text
+if st.button("Classify"):
+    # Preprocess the user input
+    processed_input = preprocess_text(user_input)
+    
+    # Vectorize the processed input using the loaded vectorizer
+    processed_input_vectorized = vect.transform([processed_input])
+    
+    # Predict using the loaded model
+    prediction = model.predict(processed_input_vectorized)
+    
+    # Display result
+    if prediction[0] == 1:
+        st.title("There is an emergency 🚨")
+    else:
+        st.title("There is NO emergency 🦄")