Update app.py

app.py

@@ -6,6 +6,10 @@ import subprocess
 import nltk
 from nltk.corpus import wordnet
 from spellchecker import SpellChecker
+from flask import Flask, jsonify, request
+
+# Initialize Flask app
+app = Flask(__name__)
 
 # Initialize the English text classification pipeline for AI detection
 pipeline_en = pipeline(task="text-classification", model="Hello-SimpleAI/chatgpt-detector-roberta")
@@ -29,234 +33,78 @@ def predict_en(text):
     res = pipeline_en(text)[0]
     return res['label'], res['score']
 
-# Function to get synonyms using NLTK WordNet
-def get_synonyms_nltk(word, pos):
-    synsets = wordnet.synsets(word, pos=pos)
-    if synsets:
-        lemmas = synsets[0].lemmas()
-        return [lemma.name() for lemma in lemmas]
-    return []
-
-# Function to remove redundant and meaningless words
-def remove_redundant_words(text):
-    doc = nlp(text)
-    meaningless_words = {"actually", "basically", "literally", "really", "very", "just"}
-    filtered_text = [token.text for token in doc if token.text.lower() not in meaningless_words]
-    return ' '.join(filtered_text)
-
-# Function to capitalize the first letter of sentences and proper nouns
-def capitalize_sentences_and_nouns(text):
-    doc = nlp(text)
-    corrected_text = []
-
-    for sent in doc.sents:
-        sentence = []
-        for token in sent:
-            if token.i == sent.start:  # First word of the sentence
-                sentence.append(token.text.capitalize())
-            elif token.pos_ == "PROPN":  # Proper noun
-                sentence.append(token.text.capitalize())
-            else:
-                sentence.append(token.text)
-        corrected_text.append(' '.join(sentence))
-
-    return '\n'.join(corrected_text)  # Preserve paragraphs by joining sentences with newline
-
-# Function to force capitalization of the first letter of every sentence
-def force_first_letter_capital(text):
-    sentences = text.split(". ")  # Split by period to get each sentence
-    capitalized_sentences = [sentence[0].capitalize() + sentence[1:] if sentence else "" for sentence in sentences]
-    return ". ".join(capitalized_sentences)
-
-# Function to correct tense errors in a sentence
-def correct_tense_errors(text):
-    doc = nlp(text)
-    corrected_text = []
-    for token in doc:
-        if token.pos_ == "VERB" and token.dep_ in {"aux", "auxpass"}:
-            lemma = wordnet.morphy(token.text, wordnet.VERB) or token.text
-            corrected_text.append(lemma)
-        else:
-            corrected_text.append(token.text)
-    return ' '.join(corrected_text)
-
-# Function to correct singular/plural errors
-def correct_singular_plural_errors(text):
-    doc = nlp(text)
-    corrected_text = []
-    
-    for token in doc:
-        if token.pos_ == "NOUN":
-            if token.tag_ == "NN":  # Singular noun
-                if any(child.text.lower() in ['many', 'several', 'few'] for child in token.head.children):
-                    corrected_text.append(token.lemma_ + 's')
-                else:
-                    corrected_text.append(token.text)
-            elif token.tag_ == "NNS":  # Plural noun
-                if any(child.text.lower() in ['a', 'one'] for child in token.head.children):
-                    corrected_text.append(token.lemma_)
-                else:
-                    corrected_text.append(token.text)
-        else:
-            corrected_text.append(token.text)
-    
-    return ' '.join(corrected_text)
-
-# Function to check and correct article errors
-def correct_article_errors(text):
-    doc = nlp(text)
-    corrected_text = []
-    for token in doc:
-        if token.text in ['a', 'an']:
-            next_token = token.nbor(1)
-            if token.text == "a" and next_token.text[0].lower() in "aeiou":
-                corrected_text.append("an")
-            elif token.text == "an" and next_token.text[0].lower() not in "aeiou":
-                corrected_text.append("a")
-            else:
-                corrected_text.append(token.text)
-        else:
-            corrected_text.append(token.text)
-    return ' '.join(corrected_text)
-
-# Function to get the correct synonym while maintaining verb form
-def replace_with_synonym(token):
-    pos = None
-    if token.pos_ == "VERB":
-        pos = wordnet.VERB
-    elif token.pos_ == "NOUN":
-        pos = wordnet.NOUN
-    elif token.pos_ == "ADJ":
-        pos = wordnet.ADJ
-    elif token.pos_ == "ADV":
-        pos = wordnet.ADV
-    
-    synonyms = get_synonyms_nltk(token.lemma_, pos)
-    
-    if synonyms:
-        synonym = synonyms[0]
-        if token.tag_ == "VBG":  # Present participle (e.g., running)
-            synonym = synonym + 'ing'
-        elif token.tag_ == "VBD" or token.tag_ == "VBN":  # Past tense or past participle
-            synonym = synonym + 'ed'
-        elif token.tag_ == "VBZ":  # Third-person singular present
-            synonym = synonym + 's'
-        return synonym
-    return token.text
-
-# Function to check for and avoid double negatives
-def correct_double_negatives(text):
-    doc = nlp(text)
-    corrected_text = []
-    for token in doc:
-        if token.text.lower() == "not" and any(child.text.lower() == "never" for child in token.head.children):
-            corrected_text.append("always")
-        else:
-            corrected_text.append(token.text)
-    return ' '.join(corrected_text)
-
-# Function to ensure subject-verb agreement
-def ensure_subject_verb_agreement(text):
-    doc = nlp(text)
-    corrected_text = []
-    for token in doc:
-        if token.dep_ == "nsubj" and token.head.pos_ == "VERB":
-            if token.tag_ == "NN" and token.head.tag_ != "VBZ":  # Singular noun, should use singular verb
-                corrected_text.append(token.head.lemma_ + "s")
-            elif token.tag_ == "NNS" and token.head.tag_ == "VBZ":  # Plural noun, should not use singular verb
-                corrected_text.append(token.head.lemma_)
-        corrected_text.append(token.text)
-    return ' '.join(corrected_text)
-
-# Function to correct spelling errors
-def correct_spelling(text):
-    words = text.split()
-    corrected_words = []
-    for word in words:
-        corrected_word = spell.correction(word)
-        corrected_words.append(corrected_word)
-    return ' '.join(corrected_words)
-
-# Function to rephrase text and replace words with their synonyms while maintaining form
-def rephrase_with_synonyms(text):
-    doc = nlp(text)
-    rephrased_text = []
-
-    for token in doc:
-        pos_tag = None
-        if token.pos_ == "NOUN":
-            pos_tag = wordnet.NOUN
-        elif token.pos_ == "VERB":
-            pos_tag = wordnet.VERB
-        elif token.pos_ == "ADJ":
-            pos_tag = wordnet.ADJ
-        elif token.pos_ == "ADV":
-            pos_tag = wordnet.ADV
-        
-        if pos_tag:
-            synonyms = get_synonyms_nltk(token.text, pos_tag)
-            if synonyms:
-                synonym = synonyms[0]  # Just using the first synonym for simplicity
-                if token.pos_ == "VERB":
-                    if token.tag_ == "VBG":  # Present participle (e.g., running)
-                        synonym = synonym + 'ing'
-                    elif token.tag_ == "VBD" or token.tag_ == "VBN":  # Past tense or past participle
-                        synonym = synonym + 'ed'
-                    elif token.tag_ == "VBZ":  # Third-person singular present
-                        synonym = synonym + 's'
-                elif token.pos_ == "NOUN" and token.tag_ == "NNS":  # Plural nouns
-                    synonym += 's' if not synonym.endswith('s') else ""
-                rephrased_text.append(synonym)
-            else:
-                rephrased_text.append(token.text)
-        else:
-            rephrased_text.append(token.text)
-
-    return ' '.join(rephrased_text)
+# Other processing functions (remove redundant words, capitalization, etc.) as previously defined
+# For brevity, I'm skipping them here since they're unchanged. Make sure to include all the defined functions from the original code.
 
 # Function to paraphrase and correct grammar with enhanced accuracy
 def paraphrase_and_correct(text):
-    # Remove meaningless or redundant words first
     cleaned_text = remove_redundant_words(text)
-    
-    # Capitalize sentences and nouns
     paraphrased_text = capitalize_sentences_and_nouns(cleaned_text)
-    
-    # Ensure first letter of each sentence is capitalized
     paraphrased_text = force_first_letter_capital(paraphrased_text)
-    
-    # Apply grammatical corrections
     paraphrased_text = correct_article_errors(paraphrased_text)
     paraphrased_text = correct_singular_plural_errors(paraphrased_text)
     paraphrased_text = correct_tense_errors(paraphrased_text)
     paraphrased_text = correct_double_negatives(paraphrased_text)
     paraphrased_text = ensure_subject_verb_agreement(paraphrased_text)
-    
-    # Rephrase with synonyms while maintaining grammatical forms
     paraphrased_text = rephrase_with_synonyms(paraphrased_text)
-    
-    # Correct spelling errors
     paraphrased_text = correct_spelling(paraphrased_text)
     
     return paraphrased_text
 
-# Gradio app setup with two tabs
-with gr.Blocks() as demo:
-    with gr.Tab("AI Detection"):
-        t1 = gr.Textbox(lines=5, label='Text')
-        button1 = gr.Button("🤖 Predict!")
-        label1 = gr.Textbox(lines=1, label='Predicted Label 🎃')
-        score1 = gr.Textbox(lines=1, label='Prob')
-
-        # Connect the prediction function to the button
-        button1.click(fn=predict_en, inputs=t1, outputs=[label1, score1])
-
-    with gr.Tab("Paraphrasing & Grammar Correction"):
-        t2 = gr.Textbox(lines=5, label='Enter text for paraphrasing and grammar correction')
-        button2 = gr.Button("🔄 Paraphrase and Correct")
-        result2 = gr.Textbox(lines=10, label='Corrected Text', placeholder="The corrected text will appear here...")
-
-        # Connect the paraphrasing and correction function to the button
-        button2.click(fn=paraphrase_and_correct, inputs=t2, outputs=result2)
+# API Endpoint for AI Detection
+@app.route('/api/ai-detection', methods=['POST'])
+def ai_detection():
+    data = request.get_json()
+    text = data.get('text', '')
+    
+    if text:
+        label, score = predict_en(text)
+        return jsonify({"label": label, "score": score})
+    else:
+        return jsonify({"error": "No text provided"}), 400
+
+# API Endpoint for Paraphrasing and Grammar Correction
+@app.route('/api/paraphrase-correct', methods=['POST'])
+def paraphrase_and_correct_api():
+    data = request.get_json()
+    text = data.get('text', '')
+    
+    if text:
+        corrected_text = paraphrase_and_correct(text)
+        return jsonify({"corrected_text": corrected_text})
+    else:
+        return jsonify({"error": "No text provided"}), 400
 
-demo.launch(share=True)  # Share=True to create a public link
+# Gradio app setup with two tabs
+def launch_gradio():
+    with gr.Blocks() as demo:
+        with gr.Tab("AI Detection"):
+            t1 = gr.Textbox(lines=5, label='Text')
+            button1 = gr.Button("🤖 Predict!")
+            label1 = gr.Textbox(lines=1, label='Predicted Label 🎃')
+            score1 = gr.Textbox(lines=1, label='Prob')
+
+            # Connect the prediction function to the button
+            button1.click(fn=predict_en, inputs=t1, outputs=[label1, score1])
+
+        with gr.Tab("Paraphrasing & Grammar Correction"):
+            t2 = gr.Textbox(lines=5, label='Enter text for paraphrasing and grammar correction')
+            button2 = gr.Button("🔄 Paraphrase and Correct")
+            result2 = gr.Textbox(lines=10, label='Corrected Text', placeholder="The corrected text will appear here...")
+
+            # Connect the paraphrasing and correction function to the button
+            button2.click(fn=paraphrase_and_correct, inputs=t2, outputs=result2)
+
+    demo.launch(share=True)  # Share=True to create a public link
+
+# Launch Gradio interface in a separate thread
+if __name__ == '__main__':
+    # Run Flask app in one thread and Gradio in another
+    from threading import Thread
+
+    # Gradio interface
+    gradio_thread = Thread(target=launch_gradio)
+    gradio_thread.start()
+
+    # Flask API
+    app.run(debug=True, port=5000)