Update app.py

app.py

@@ -1,54 +1,125 @@
-# Import required libraries
-import nltk
-import re
+from flask import Flask, render_template, request
+from nltk.tokenize import word_tokenize
+from nltk.corpus import wordnet
+from nltk import pos_tag, ne_chunk
+import textblob
+from polyglot.detect import Detector
 import numpy as np
-from flask import Flask, request, render_template
-from textblob import TextBlob
+from keras.models import load_model
 
-# Initialize the Flask application
 app = Flask(__name__)
+model = load_model("emotion_detector.h5")
 
-# Define the root route
-@app.route('/')
+@app.route("/")
 def index():
-    return render_template('index.html')
+    return render_template("index.html")
 
-# Define the route for paraphrasing text
-@app.route('/paraphrase', methods=['POST'])
+@app.route("/paraphrase", methods=["POST"])
 def paraphrase():
-    # Get the input text from the form
-    input_text = request.form['input_text']
+    input_text = request.form["input_text"]
+    options = request.form.getlist("options")
 
-    # Correct grammar using TextBlob
-    corrected_text = TextBlob(input_text).correct()
+    # Remove special characters
+    if "remove_special_characters" in options:
+        input_text = remove_special_characters(input_text)
+    
+    # Correct grammar
+    if "correct_grammar" in options:
+        input_text = correct_grammar(input_text)
 
-    # Remove special characters using regular expressions
-    cleaned_text = re.sub('[^A-Za-z0-9]+', ' ', corrected_text)
+    # Summarize text
+    if "summarize_text" in options:
+        input_text = summarize_text(input_text)
 
-    # Summarize the text using TextBlob
-    summarized_text = TextBlob(cleaned_text).summarize()
+    # Multilingual support
+    target_language = request.form.get("target_language")
+    if target_language:
+        input_text = translate(input_text, target_language)
 
-    # Perform Part-of-Speech (POS) tagging using NLTK
-    pos_tagged_text = nltk.pos_tag(summarized_text.words)
+    # Custom synonyms
+    custom_synonyms = request.form.getlist("custom_synonyms")
+    for word, synonym in custom_synonyms:
+        input_text = replace_word(input_text, word, synonym)
 
-    # Perform Named Entity Recognition (NER) using NLTK
-    ner_tagged_text = nltk.ne_chunk(pos_tagged_text)
+    # Output customization
+    input_text = customise_output(input_text, options)
 
-    # Perform sentiment analysis using TextBlob
-    sentiment = TextBlob(summarized_text).sentiment
+    # Integration with other NLP tools
+    named_entities = get_named_entities(input_text)
+    part_of_speech = get_part_of_speech(input_text)
+    sentiment = get_sentiment(input_text)
 
-    # 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"
+    # Emotion detector
+    emotion = detect_emotion(input_text)
+    input_text = adjust_tone(input_text, emotion)
 
-    # 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)
+    return render_template("index.html", paraphrased_text=input_text, named_entities=named_entities, part_of_speech=part_of_speech, sentiment=sentiment)
 
-# Run the Flask application
-if __name__ == '__main__':
-    app.run(debug=True,host="0.0.0.0",port=7860)
+def remove_special_characters(input_text):
+    # Code to remove special characters
+    return input_text
+
+def summarize_text(input_text):
+    # Code to summarize the text
+    return input_text
+
+def detect_language(input_text):
+    detector = Detector(input_text)
+    language = detector.language.code
+    return language
+
+def translate(input_text, target_language):
+    blob = textblob.TextBlob(input_text)
+    translated_text = blob.translate(to=target_language)
+    return translated_text
+
+def get_synonyms(word):
+    synonyms = []
+    for syn in wordnet.synsets(word):
+        for lemma in syn.lemmas():
+            synonyms.append(lemma.name())
+    return synonyms
+
+def replace_word(input_text, word, synonym):
+    words = word_tokenize(input_text)
+    words = [synonym if w == word else w for w in words]
+    input_text = " ".join(words)
+    return input_text
+
+def customise_output(input_text, options):
+    # Code to customise output based on options
+    return input_text
+
+def get_named_entities(input_text):
+    named_entities = ne_chunk(pos_tag(word_tokenize(input_text)))
+    return named_entities
+
+def get_part_of_speech(input_text):
+    pos = pos_tag(word_tokenize(input_text))
+    return pos
+
+def get_sentiment(input_text):
+    blob = textblob.TextBlob(input_text)
+    sentiment = blob.sentiment.polarity
+    return sentiment
+
+def correct_grammar(input_text):
+    blob = textblob.TextBlob(input_text)
+    corrected_text = str(blob.correct())
+    return corrected_text
+
+def detect_emotion(input_text):
+    words = word_tokenize(input_text)
+    words = [w.lower() for w in words]
+    words = [w for w in words if w.isalpha()]
+    input_text = " ".join(words)
+    input_text = np.array([input_text])
+    sentiment = model.predict(input_text, batch_size=1, verbose=0)[0]
+    return sentiment
+
+def adjust_tone(input_text, emotion):
+    # Code to adjust tone based on emotion
+    return input_text
+
+if __name__ == "__main__":
+    app.run(debug=True,port=7860,host="0.0.0.0")