Update app.py

app.py

@@ -1,162 +1,25 @@
-import os
-import gradio as gr
-import spacy
-import subprocess
-import nltk
-from nltk.corpus import wordnet
-from spellchecker import SpellChecker
-from ginger import get_ginger_result  # Importing the grammar correction function
-
-# Initialize the English text classification pipeline for AI detection
-pipeline_en = pipeline(task="text-classification", model="Hello-SimpleAI/chatgpt-detector-roberta")
-
-# Initialize the spell checker
-spell = SpellChecker()
-
-# Ensure necessary NLTK data is downloaded
-nltk.download('wordnet')
-nltk.download('omw-1.4')
-
-# 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 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
-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 ' '.join(corrected_text)
-
-# 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.text, pos)
-    if synonyms:
-        return synonyms[0]
-    return token.text
-
-# Function to use Ginger API for grammar correction (NEW)
-def correct_grammar_with_ginger(text):
-    result = get_ginger_result(text)
-    corrected_text = text
-    for suggestion in result["LightGingerTheTextResult"]:
-        if suggestion["Suggestions"]:
-            from_index = suggestion["From"]
-            to_index = suggestion["To"] + 1
-            suggested_text = suggestion["Suggestions"][0]["Text"]
-            corrected_text = corrected_text[:from_index] + suggested_text + corrected_text[to_index:]
-    return corrected_text
-
-# Gradio interface
-def process_text(text):
-    text = correct_article_errors(text)
-    text = correct_singular_plural_errors(text)
-    text = correct_tense_errors(text)
-    text = capitalize_sentences_and_nouns(text)
-    text = remove_redundant_words(text)
-    text = correct_grammar_with_ginger(text)  # Add grammar correction using Ginger here
-    return text
-
-iface = gr.Interface(fn=process_text, inputs="text", outputs="text")
-iface.launch()
+from ginger import correct_sentence
+
+def main():
+    """
+    Main application function.
+    Prompts the user for input and corrects grammar using the Ginger API.
+    """
+    while True:
+        # Get input from the user
+        text = input("Enter a sentence (or 'q' to quit): ")
+
+        # Exit the loop if the user wants to quit
+        if text.lower() == 'q':
+            print("Exiting the application.")
+            break
+
+        # Correct the sentence using the Ginger API
+        corrected_text = correct_sentence(text)
+
+        # Display the original and corrected sentences
+        print(f"Original Sentence: {text}")
+        print(f"Corrected Sentence: {corrected_text}\n")
+
+if __name__ == "__main__":
+    main()