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| 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 keras.models import load_model | |
| app = Flask(__name__) | |
| model = load_model("emotion_detector.h5") | |
| def index(): | |
| return render_template("index.html") | |
| def paraphrase(): | |
| input_text = request.form["input_text"] | |
| options = request.form.getlist("options") | |
| # 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) | |
| # Summarize text | |
| if "summarize_text" in options: | |
| input_text = summarize_text(input_text) | |
| # Multilingual support | |
| target_language = request.form.get("target_language") | |
| if target_language: | |
| input_text = translate(input_text, target_language) | |
| # Custom synonyms | |
| custom_synonyms = request.form.getlist("custom_synonyms") | |
| for word, synonym in custom_synonyms: | |
| input_text = replace_word(input_text, word, synonym) | |
| # Output customization | |
| input_text = customise_output(input_text, options) | |
| # 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) | |
| # Emotion detector | |
| emotion = detect_emotion(input_text) | |
| input_text = adjust_tone(input_text, emotion) | |
| return render_template("index.html", paraphrased_text=input_text, named_entities=named_entities, part_of_speech=part_of_speech, sentiment=sentiment) | |
| 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") |