Update app.py

app.py

@@ -1,38 +1,42 @@
 import os
+import subprocess
 import gradio as gr
 from transformers import pipeline
 import spacy
-import subprocess
 import nltk
 from nltk.corpus import wordnet
 
-# Initialize the English text classification pipeline for AI detection
-pipeline_en = pipeline(task="text-classification", model="Hello-SimpleAI/chatgpt-detector-roberta")
-
-# Ensure necessary NLTK data is downloaded for Humanifier
+# Ensure necessary NLTK data is downloaded
 nltk.download('wordnet')
 nltk.download('omw-1.4')
 
-# Ensure the SpaCy model is installed for Humanifier
+# Ensure the SpaCy model is installed
 try:
     nlp = spacy.load("en_core_web_sm")
 except OSError:
     subprocess.run(["python", "-m", "spacy", "download", "en_core_web_sm"])
     nlp = spacy.load("en_core_web_sm")
 
-# Function to get synonyms using NLTK WordNet (Humanifier)
+# Initialize the English text classification pipeline for AI detection
+pipeline_en = pipeline(task="text-classification", model="Hello-SimpleAI/chatgpt-detector-roberta")
+
+def predict_en(text):
+    """ Function to predict the label and score for English text (AI Detection) """
+    res = pipeline_en(text)[0]
+    return res['label'], res['score']
+
 def get_synonyms_nltk(word, pos):
+    """ Function to get synonyms using NLTK WordNet """
     synsets = wordnet.synsets(word, pos=pos)
     if synsets:
         lemmas = synsets[0].lemmas()
         return [lemma.name() for lemma in lemmas]
     return []
 
-# Function to capitalize the first letter of sentences and proper nouns (Humanifier)
 def capitalize_sentences_and_nouns(text):
+    """ Function to capitalize the first letter of sentences and proper nouns """
     doc = nlp(text)
     corrected_text = []
-
     for sent in doc.sents:
         sentence = []
         for token in sent:
@@ -43,50 +47,42 @@ def capitalize_sentences_and_nouns(text):
             else:
                 sentence.append(token.text)
         corrected_text.append(' '.join(sentence))
-
     return ' '.join(corrected_text)
 
-# Function to correct tense errors in a sentence (Tense Correction)
 def correct_tense_errors(text):
+    """ Function to correct tense errors in a sentence """
     doc = nlp(text)
     corrected_text = []
     for token in doc:
-        # Check for tense correction based on modal verbs
         if token.pos_ == "VERB" and token.dep_ in {"aux", "auxpass"}:
-            # Replace with appropriate verb form
             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 (Singular/Plural Correction)
 def correct_singular_plural_errors(text):
+    """ Function to correct singular/plural errors """
     doc = nlp(text)
     corrected_text = []
-    
     for token in doc:
         if token.pos_ == "NOUN":
-            # Check if the noun is singular or plural
             if token.tag_ == "NN":  # Singular noun
-                # Look for determiners like "many", "several", "few" to correct to plural
                 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
-                # Look for determiners like "a", "one" to correct to singular
                 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):
+    """ Function to check and correct article errors """
     doc = nlp(text)
     corrected_text = []
     for token in doc:
@@ -102,23 +98,10 @@ def correct_article_errors(text):
             corrected_text.append(token.text)
     return ' '.join(corrected_text)
 
-# Function to paraphrase and correct grammar
 def paraphrase_and_correct(text):
-    # Enhance rephrasing by using more advanced SpaCy functionalities
-    paraphrased_text = capitalize_sentences_and_nouns(text)  # Start by capitalizing sentences properly
-
-    # Use SpaCy to rephrase by substituting synonyms, restructuring sentences, etc.
-    doc = nlp(paraphrased_text)
-    rephrased = []
-    for token in doc:
-        synonyms = get_synonyms_nltk(token.text, pos=wordnet.VERB if token.pos_ == "VERB" else wordnet.NOUN)
-        if synonyms:
-            rephrased.append(synonyms[0])
-        else:
-            rephrased.append(token.text)
-
-    paraphrased_text = ' '.join(rephrased)
-
+    """ Function to paraphrase and correct grammar """
+    paraphrased_text = capitalize_sentences_and_nouns(text)  # Capitalize first to ensure proper noun capitalization
+    
     # Apply grammatical corrections
     paraphrased_text = correct_article_errors(paraphrased_text)
     paraphrased_text = correct_singular_plural_errors(paraphrased_text)
@@ -126,24 +109,22 @@ def paraphrase_and_correct(text):
 
     return paraphrased_text
 
-# Gradio app setup with two tabs
+# Setup Gradio interface
 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')
+        label1 = gr.Textbox(lines=1, label='Predicted Label')
+        score1 = gr.Textbox(lines=1, label='Score')
+
+        button1.click(predict_en, inputs=[t1], outputs=[label1, score1])
 
-        # Connect the prediction function to the button
-        button1.click(predict_en, inputs=[t1], outputs=[label1, score1], api_name='predict_en')
-    
     with gr.Tab("Humanifier"):
         text_input = gr.Textbox(lines=5, label="Input Text")
         paraphrase_button = gr.Button("Paraphrase & Correct")
         output_text = gr.Textbox(label="Paraphrased Text")
 
-        # Connect the paraphrasing function to the button
-        paraphrase_button.click(paraphrase_and_correct, inputs=text_input, outputs=output_text)
+        paraphrase_button.click(paraphrase_and_correct, inputs=[text_input], outputs=[output_text])
 
-# Launch the app with the remaining functionalities
+# Launch the app
 demo.launch()