Update app.py

app.py

@@ -1,92 +1,68 @@
 import os
-import subprocess
-import sys
 import gradio as gr
-from transformers import pipeline
 import spacy
 import nltk
 from nltk.corpus import wordnet
+from nltk.stem import WordNetLemmatizer
+from collections import defaultdict
 
-# Function to install GECToR
-def install_gector():
-    if not os.path.exists('gector'):
-        print("Cloning GECToR repository...")
-        subprocess.run(["git", "clone", "https://github.com/grammarly/gector.git"], check=True)
-        
-        # Install dependencies from GECToR requirements
-        subprocess.run([sys.executable, "-m", "pip", "install", "-r", "gector/requirements.txt"], check=True)
-
-        # Manually add GECToR to the Python path
-        sys.path.append(os.path.abspath('gector'))
-
-# Install and import GECToR
-install_gector()
-
-# Ensure the gector module path is added correctly
-sys.path.insert(0, os.path.abspath('./gector'))
-
-# Import GECToR after installation
-from gector.gec_model import GecBERTModel
-
-# Initialize GECToR model for grammar correction
-gector_model = GecBERTModel(vocab_path='gector/data/output_vocabulary',
-                            model_paths=['https://grammarly-nlp-data.s3.amazonaws.com/gector/roberta_1_gector.th'],
-                            is_ensemble=False)
-
-# Initialize the English text classification pipeline for AI detection
-pipeline_en = pipeline(task="text-classification", model="Hello-SimpleAI/chatgpt-detector-roberta")
-
-# 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
+# Ensure necessary NLTK data is downloaded
 nltk.download('wordnet')
-nltk.download('omw-1.4')
+nltk.download('averaged_perceptron_tagger')
+nltk.download('punkt')
 
-# Ensure the SpaCy model is installed for Humanifier
+# Ensure the SpaCy model is installed for POS tagging
 try:
     nlp = spacy.load("en_core_web_sm")
 except OSError:
-    subprocess.run([sys.executable, "-m", "spacy", "download", "en_core_web_sm"])
+    subprocess.run(["python", "-m", "spacy", "download", "en_core_web_sm"])
     nlp = spacy.load("en_core_web_sm")
 
-# Function to correct grammar using GECToR
-def correct_grammar_with_gector(text):
-    corrected_sentences = []
-    sentences = [text]
-    for sentence in sentences:
-        preds = gector_model.handle_batch([sentence])
-        corrected_sentences.append(preds[0])
-    return ' '.join(corrected_sentences)
-
-# Gradio app setup with three 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')
+# Initialize lemmatizer
+lemmatizer = WordNetLemmatizer()
+
+# Helper function to map nltk POS tags to wordnet POS tags
+def get_wordnet_pos(treebank_tag):
+    if treebank_tag.startswith('J'):
+        return wordnet.ADJ
+    elif treebank_tag.startswith('V'):
+        return wordnet.VERB
+    elif treebank_tag.startswith('N'):
+        return wordnet.NOUN
+    elif treebank_tag.startswith('R'):
+        return wordnet.ADV
+    else:
+        return None
+
+# Function to correct tense, singular/plural, and verb forms
+def grammar_correction(text):
+    words = nltk.word_tokenize(text)
+    tagged = nltk.pos_tag(words)
     
-    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(correct_grammar_with_gector, inputs=text_input, outputs=output_text)
+    corrected_text = []
+    for word, tag in tagged:
+        wordnet_pos = get_wordnet_pos(tag) or wordnet.NOUN
+        lemma = lemmatizer.lemmatize(word, pos=wordnet_pos)
+        
+        # Apply basic rules for common errors
+        if tag.startswith('VB') and word.lower() != lemma:  # Verb tense correction
+            corrected_text.append(lemma)
+        elif tag.startswith('NNS') and word.lower() == lemma:  # Singular/plural correction
+            corrected_text.append(word + 's')
+        else:
+            corrected_text.append(word)
     
+    return ' '.join(corrected_text)
+
+# Gradio app setup
+with gr.Blocks() as demo:
     with gr.Tab("Grammar Correction"):
         grammar_input = gr.Textbox(lines=5, label="Input Text")
         grammar_button = gr.Button("Correct Grammar")
         grammar_output = gr.Textbox(label="Corrected Text")
 
-        # Connect the GECToR grammar correction function to the button
-        grammar_button.click(correct_grammar_with_gector, inputs=grammar_input, outputs=grammar_output)
+        # Connect the grammar correction function to the button
+        grammar_button.click(grammar_correction, inputs=grammar_input, outputs=grammar_output)
 
-# Launch the app with all functionalities
-demo.launch()
+# Launch the app
+demo.launch()