Create app.py

app.py

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+import streamlit as st
+import joblib
+import numpy as np
+from sklearn.feature_extraction.text import TfidfVectorizer
+# Import necessary libraries
+import re
+from urllib.parse import urlparse
+from nltk.tokenize import word_tokenize
+from nltk.corpus import stopwords
+from nltk.stem import WordNetLemmatizer
+
+# Initialize NLTK resources
+stop_words = set(stopwords.words("english"))  # Create a set of English stopwords
+lemmatizer = WordNetLemmatizer()  # Initialize the WordNet Lemmatizer
+
+# Define a function for text processing
+def textProcess(sent):
+    try:
+        if sent is None:  # Check if the input is None
+            return ""  # Return an empty string if input is None
+
+        # Remove square brackets, parentheses, and other special characters
+        sent = re.sub('[][)(]', ' ', sent)
+
+        # Tokenize the text into words
+        sent = [word for word in sent.split() if not urlparse(word).scheme]
+
+        # Join the words back into a sentence
+        sent = ' '.join(sent)
+
+        # Remove Twitter usernames (words starting with @)
+        sent = re.sub(r'\@\w+', '', sent)
+
+        # Remove HTML tags using regular expression
+        sent = re.sub(re.compile("<.*?>"), '', sent)
+
+        # Remove non-alphanumeric characters (keep only letters and numbers)
+        sent = re.sub("[^A-Za-z0-9]", ' ', sent)
+
+        # Convert text to lowercase
+        sent = sent.lower()
+
+        # Split the text into words, strip whitespace, and join them back into a sentence
+        sent = [word.strip() for word in sent.split()]
+        sent = ' '.join(sent)
+
+        # Tokenize the text again
+        tokens = word_tokenize(sent)
+
+        # Remove stop words
+        for word in tokens.copy():
+            if word in stop_words:
+                tokens.remove(word)
+
+        # Lemmatize the remaining words
+        sent = [lemmatizer.lemmatize(word) for word in tokens]
+
+        # Join the lemmatized words back into a sentence
+        sent = ' '.join(sent)
+
+        # Return the processed text
+        return sent
+
+    except Exception as ex:
+        print(sent, "\n")
+        print("Error ", ex)
+        return ""  # Return an empty string in case of an error
+
+# Rest of your code...
+
+# Load the pre-trained model from joblib
+model = joblib.load('Stress identification NLP')
+
+# Load the TF-IDF vectorizer used during training
+tfidf_vectorizer = joblib.load('tfidf_vectorizer.joblib')
+
+# Define the Streamlit web app
+def main():
+    st.title("Stress Predictor Web App")
+    st.write("Enter some text to predict if the person is in stress or not.")
+
+    # Input text box
+    user_input = st.text_area("Enter text here:")
+
+    if st.button("Predict"):
+        if user_input:
+            # Process the input text
+            processed_text = textProcess(user_input)
+
+            # Use the same TF-IDF vectorizer to transform the input text
+            tfidf_text = tfidf_vectorizer.transform([processed_text])
+
+            # Make predictions using the loaded model
+            prediction = model.predict(tfidf_text)[0]
+
+            if prediction == 1:
+                result = "This person is in stress."
+            else:
+                result = "This person is not in stress."
+
+            st.write(result)
+
+if __name__ == '__main__':
+    main()