Update app.py

app.py

@@ -1,61 +1,23 @@
-import re
-from textblob import TextBlob
-import nltk
-import emopy
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-import torch
-import spacy
-from flask import Flask, request, render_template
-
-nltk.download('averaged_perceptron_tagger')
-nltk.download('punkt')
-nltk.download('maxent_ne_chunker')
-nltk.download('words')
-
-nlp = spacy.load("en_core_web_sm")
-
-app = Flask(__name__)
-
-@app.route('/', methods=['GET', 'POST'])
-def index():
-    if request.method == 'POST':
-        text = request.form['text']
-        paraphrase_option = request.form['paraphrase_option']
-        remove_special_chars = request.form.get('remove_special_chars')
-        summarize = request.form.get('summarize')
-        correct_grammar = request.form.get('correct_grammar')
-
-        if correct_grammar:
-            text = str(TextBlob(text).correct())
-
-        if remove_special_chars:
-            text = re.sub(r'[^\w\s]', '', text)
-
-        if summarize:
-            doc = nlp(text)
-            sentences = [sent.text for sent in doc.sents]
-            text = " ".join(sentences[:3]) + "..."
-
-        if paraphrase_option == 'repeat':
-            text = re.sub(r'\b(\w+)\b', r'\1', text)
-        elif paraphrase_option == 'emotion_detector':
-            emotion = emopy.EmotionDetector()
-            emotions = emotion.detect_emotion(text)
-            emotion_labels = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']
-            index = np.argmax(emotions)
-            emotion = emotion_labels[index]
-            if emotion == 'happy':
-                text = text.upper()
-            elif emotion == 'sad':
-                text = text.lower()
-            else:
-                text = text.capitalize()
-
-        return render_template('index.html', text=text)
-    return render_template('index.html')
-
-if __name__ == '__main__':
-    app.run(host="0.0.0.0",port=7860,debug=True)
+import gradio as gr
+from parrot import Parrot
+
+# Create a Parrot instance
+parrot = Parrot(model_tag="turing", use_gpu=False)
+
+def paraphrase_text(text):
+    # Call the Parrot instance to paraphrase the input text
+    result = parrot.predict([text])
+    return result[0]['translation_text']
+
+# Create a Gradio interface
+interface = gr.Interface(
+    fn=paraphrase_text,
+    inputs=gr.inputs.Textbox(label="Enter text to paraphrase:"),
+    outputs=gr.outputs.Textbox(label="Paraphrased text:"),
+    title="Parrot - Text Paraphrasing",
+    description="Enter a text to paraphrase using Parrot.",
+    theme="default"
+)
+
+# Launch the interface
+interface.launch()