Update app.py

app.py

@@ -1,31 +1,126 @@
+import os
 import gradio as gr
-from gramformer import Gramformer  # Assuming gramformer.py is in the same directory as this app
+from transformers import pipeline
+import spacy
+import subprocess
+import nltk
+from nltk.corpus import wordnet
+import torch
+from gramformer import Gramformer
+
+# Initialize the English text classification pipeline for AI detection
+pipeline_en = pipeline(task="text-classification", model="Hello-SimpleAI/chatgpt-detector-roberta")
 
 # Initialize Gramformer
-gf = Gramformer(models=1, use_gpu=False)  # Set use_gpu=True if using a GPU-enabled space
-
-def correct_sentence(input_sentence):
-    # Use the correct method from Gramformer
-    corrected_sentences = gf.correct(input_sentence, max_candidates=1)
-    return "\n".join(corrected_sentences)
-
-# Create Gradio interface
-def gradio_interface():
-    input_text = gr.inputs.Textbox(lines=2, placeholder="Enter a sentence here...")
-    output_text = gr.outputs.Textbox()
-
-    # Gradio Interface: Takes a sentence, corrects it, and outputs the correction
-    iface = gr.Interface(
-        fn=correct_sentence,
-        inputs=input_text,
-        outputs=output_text,
-        title="Grammar Correction",
-        description="Corrects grammatical errors in the input sentence using the Gramformer model."
-    )
-    
-    return iface
-
-# Run the Gradio app
-if __name__ == "__main__":
-    iface = gradio_interface()
-    iface.launch()
+gf = Gramformer(models=2, use_gpu=False)  # 2 = corrector
+
+# 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']
+
+# Ensure necessary NLTK data is downloaded for Humanifier
+nltk.download('wordnet')
+nltk.download('omw-1.4')
+
+# Ensure the SpaCy model is installed for Humanifier
+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)
+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 capitalize the first letter of sentences and proper nouns (Humanifier)
+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 ' '.join(corrected_text)
+
+# Paraphrasing function using SpaCy and NLTK (Humanifier)
+def paraphrase_with_spacy_nltk(text):
+    doc = nlp(text)
+    paraphrased_words = []
+    
+    for token in doc:
+        # Map SpaCy POS tags to WordNet POS tags
+        pos = None
+        if token.pos_ in {"NOUN"}:
+            pos = wordnet.NOUN
+        elif token.pos_ in {"VERB"}:
+            pos = wordnet.VERB
+        elif token.pos_ in {"ADJ"}:
+            pos = wordnet.ADJ
+        elif token.pos_ in {"ADV"}:
+            pos = wordnet.ADV
+        
+        synonyms = get_synonyms_nltk(token.text.lower(), pos) if pos else []
+        
+        # Replace with a synonym only if it makes sense
+        if synonyms and token.pos_ in {"NOUN", "VERB", "ADJ", "ADV"} and synonyms[0] != token.text.lower():
+            paraphrased_words.append(synonyms[0])
+        else:
+            paraphrased_words.append(token.text)
+    
+    # Join the words back into a sentence
+    paraphrased_sentence = ' '.join(paraphrased_words)
+    
+    # Capitalize sentences and proper nouns
+    corrected_text = capitalize_sentences_and_nouns(paraphrased_sentence)
+    
+    return corrected_text
+
+# Combined function: Paraphrase -> Capitalization -> Grammar Correction (Humanifier)
+def paraphrase_correct_and_grammar(text):
+    # Step 1: Paraphrase the text
+    paraphrased_text = paraphrase_with_spacy_nltk(text)
+    
+    # Step 2: Capitalize sentences and proper nouns
+    capitalized_text = capitalize_sentences_and_nouns(paraphrased_text)
+    
+    # Step 3: Grammar correction using Gramformer
+    corrected_sentences = gf.correct(capitalized_text)
+    final_text = corrected_sentences[0] if corrected_sentences else capitalized_text
+    
+    return final_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(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 & Grammar Check")
+        output_text = gr.Textbox(label="Processed Text")
+
+        # Connect the paraphrasing and grammar correction function to the button
+        paraphrase_button.click(paraphrase_correct_and_grammar, inputs=text_input, outputs=output_text)
+
+# Launch the app with both functionalities
+demo.launch()