Update app.py

app.py

@@ -6,55 +6,32 @@ 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 check subject-verb agreement
-def check_subject_verb_agreement(doc):
-    corrected_text = []
-    for token in doc:
-        if token.dep_ == "nsubj":  # Check if the token is a subject
-            subject = token
-            verb = token.head  # Find the associated verb
-            if verb.tag_ in {"VBZ", "VBP"}:  # Singular/plural verb forms
-                if subject.tag_ == "NNS" and verb.tag_ == "VBZ":  # Plural subject with singular verb
-                    corrected_text.append(verb.lemma_)  # Convert verb to plural form
-                elif subject.tag_ == "NN" and verb.tag_ == "VBP":  # Singular subject with plural verb
-                    corrected_text.append(verb.lemma_ + 's')  # Convert verb to singular form
-                else:
-                    corrected_text.append(verb.text)  # No correction needed
-            else:
-                corrected_text.append(verb.text)
-        corrected_text.append(token.text)
-    return ' '.join(corrected_text)
+# Initialize the English text classification pipeline for AI detection
+pipeline_en = pipeline(task="text-classification", model="Hello-SimpleAI/chatgpt-detector-roberta")
 
-# Function to correct singular/plural errors using dependency parsing
-def correct_singular_plural_errors(doc):
-    corrected_text = []
-    
-    for token in doc:
-        if token.pos_ == "NOUN":
-            if token.tag_ == "NN" and token.head.pos_ == "VERB" and token.head.tag_ == "VBP":
-                corrected_text.append(token.lemma_ + 's')  # Singular noun, plural verb
-            elif token.tag_ == "NNS" and token.head.pos_ == "VERB" and token.head.tag_ == "VBZ":
-                corrected_text.append(token.lemma_)  # Plural noun, singular verb
-            else:
-                corrected_text.append(token.text)
-        else:
-            corrected_text.append(token.text)
-    
-    return ' '.join(corrected_text)
+# Function to predict the label and score for English text (AI Detection)
+def predict_en(text):
+    res = pipeline_en(text)[0]
+    return res['label'], res['score']
+
+# Function to get synonyms using NLTK WordNet (Humanifier)
+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 []
 
 # Paraphrasing function using SpaCy and NLTK (Humanifier)
 def paraphrase_with_spacy_nltk(text):
@@ -81,34 +58,20 @@ def paraphrase_with_spacy_nltk(text):
         else:
             paraphrased_words.append(token.text)
     
-    return ' '.join(paraphrased_words)
+    # Join the words back into a sentence
+    paraphrased_sentence = ' '.join(paraphrased_words)
+    
+    return paraphrased_sentence
 
 # Combined function: Paraphrase -> Grammar Correction -> Capitalization (Humanifier)
 def paraphrase_and_correct(text):
     # Step 1: Paraphrase the text
     paraphrased_text = paraphrase_with_spacy_nltk(text)
     
-    # Step 2: Parse the text with spaCy
-    doc = nlp(paraphrased_text)
-    
-    # Step 3: Apply grammatical corrections on the paraphrased text
-    corrected_text = correct_article_errors(doc)
-    
-    corrected_text = capitalize_sentences_and_nouns(corrected_text)
+    # Additional steps (grammar correction, capitalization) can go here...
+    # For now, we'll return the paraphrased text as an example.
     
-    corrected_text = check_subject_verb_agreement(nlp(corrected_text))
-    
-    corrected_text = correct_singular_plural_errors(nlp(corrected_text))
-    
-    # Step 4: Capitalize sentences and proper nouns (final correction step)
-    final_text = correct_tense_errors(nlp(corrected_text))
-    
-    return final_text
-def predict_en(text):
-    prediction = pipeline_en(text)
-    label = prediction[0]['label']
-    score = prediction[0]['score']
-    return label, round(score, 4)
+    return paraphrased_text
 
 # Gradio app setup with two tabs
 with gr.Blocks() as demo:
@@ -130,4 +93,4 @@ with gr.Blocks() as demo:
         paraphrase_button.click(paraphrase_and_correct, inputs=text_input, outputs=output_text)
 
 # Launch the app with the remaining functionalities
-demo.launch()
+demo.launch()