Update app.py

app.py

@@ -1,68 +1,54 @@
-from flask import Flask, render_template, request
-from nltk.corpus import wordnet
-from nltk.tokenize import word_tokenize
-from nltk.corpus import stopwords
-from nltk.stem import WordNetLemmatizer
-from summa.summarizer import summarize
+# Import required libraries
+import nltk
+import re
+import numpy as np
+from flask import Flask, request, render_template
 from textblob import TextBlob
-import spacy
 
+# Initialize the Flask application
 app = Flask(__name__)
 
-@app.route("/")
+# Define the root route
+@app.route('/')
 def index():
-    return render_template("index.html")
+    return render_template('index.html')
 
-@app.route("/paraphrase", methods=["POST"])
+# Define the route for paraphrasing text
+@app.route('/paraphrase', methods=['POST'])
 def paraphrase():
-    input_text = request.form["input_text"]
-    
-    # Option to correct grammar using TextBlob
-    corrected_text = str(TextBlob(input_text).correct())
-    
-    # Option to remove special characters
-    clean_text = ''.join(e for e in corrected_text if e.isalnum() or e.isspace())
-    
-    # Perform text summarization
-    summary = summarize(clean_text)
-    
-    # Perform word tokenization and remove stopwords
-    stop_words = set(stopwords.words("english"))
-    words = word_tokenize(summary)
-    words = [word for word in words if word.lower() not in stop_words]
-    
-    # Perform lemmatization on the words
-    lemmatizer = WordNetLemmatizer()
-    lemmatized_words = [lemmatizer.lemmatize(word, pos=get_wordnet_pos(word)) for word in words]
-    
-    # Load spaCy's NER model
-    nlp = spacy.load("en_core_web_sm")
-    
-    # Use spaCy's NER to identify named entities in the input text
-    doc = nlp(summary)
-    entities = []
-    for ent in doc.ents:
-        entities.append((ent.text, ent.label_))
-    
-    # Use spaCy's POS tagging on the input text
-    pos_tags = []
-    for token in doc:
-        pos_tags.append((token.text, token.pos_))
-    
-    # Use TextBlob to perform sentiment analysis on the input text
-    sentiment = TextBlob(summary).sentiment.polarity
-    
-    return render_template("paraphrase.html", input_text=input_text, output_text=' '.join(lemmatized_words), entities=entities, pos_tags=pos_tags, sentiment=sentiment)
+    # Get the input text from the form
+    input_text = request.form['input_text']
 
-def get_wordnet_pos(word):
-    """Map POS tag to first character used by WordNetLemmatizer"""
-    tag = nltk.pos_tag([word])[0][1]
-    tag = tag[0].upper()
-    tag_dict = {"J": wordnet.ADJ,
-                "N": wordnet.NOUN,
-                "V": wordnet.VERB,
-                "R": wordnet.ADV}
-    return tag_dict.get(tag, wordnet.NOUN)
+    # Correct grammar using TextBlob
+    corrected_text = TextBlob(input_text).correct()
 
-if __name__ == "__main__":
-    app.run(host="0.0.0.0",port=7860,debug=True)
+    # Remove special characters using regular expressions
+    cleaned_text = re.sub('[^A-Za-z0-9]+', ' ', corrected_text)
+
+    # Summarize the text using TextBlob
+    summarized_text = TextBlob(cleaned_text).summarize()
+
+    # Perform Part-of-Speech (POS) tagging using NLTK
+    pos_tagged_text = nltk.pos_tag(summarized_text.words)
+
+    # Perform Named Entity Recognition (NER) using NLTK
+    ner_tagged_text = nltk.ne_chunk(pos_tagged_text)
+
+    # Perform sentiment analysis using TextBlob
+    sentiment = TextBlob(summarized_text).sentiment
+
+    # Perform emotion detection and adjust the tone of the paraphrased text
+    emotion = ""
+    if sentiment.polarity >= 0.5:
+        emotion = "Positive"
+    elif sentiment.polarity > 0 and sentiment.polarity < 0.5:
+        emotion = "Neutral"
+    else:
+        emotion = "Negative"
+
+    # Render the results template with the paraphrased text and analysis results
+    return render_template('results.html', paraphrased_text=summarized_text, pos_tagged_text=pos_tagged_text, ner_tagged_text=ner_tagged_text, sentiment=sentiment, emotion=emotion)
+
+# Run the Flask application
+if __name__ == '__main__':
+    app.run(debug=True,host="0.0.0.0",port=7860)