Update app.py

app.py

@@ -1,55 +1,162 @@
+import os
 import gradio as gr
-from transformers import pipeline
 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 other components (AI detection, NLP, etc.) as before...
+# Initialize the English text classification pipeline for AI detection
+pipeline_en = pipeline(task="text-classification", model="Hello-SimpleAI/chatgpt-detector-roberta")
 
-# Function to paraphrase and correct grammar using Ginger
-def correct_with_ginger(text):
-    ginger_result = get_ginger_result(text)
-    if "error" in ginger_result:
-        return ginger_result["error"]
+# 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 = []
     
-    original_text = text
-    fixed_text = original_text
-    color_gap, fixed_gap = 0, 0
-    if not ginger_result["LightGingerTheTextResult"]:
-        return "No grammatical issues found!"
+    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
     
-    for result in ginger_result["LightGingerTheTextResult"]:
-        if result["Suggestions"]:
-            from_index = result["From"] + color_gap
-            to_index = result["To"] + 1 + color_gap
-            suggest = result["Suggestions"][0]["Text"]
-
-            original_text = original_text[:from_index] + original_text[from_index:to_index] + original_text[to_index:]
-            fixed_text = fixed_text[:from_index-fixed_gap] + suggest + fixed_text[to_index-fixed_gap:]
-            color_gap += len(suggest) - (to_index - from_index)
-            fixed_gap += to_index - from_index - len(suggest)
-
-    return fixed_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')
-
-        button1.click(fn=predict_en, inputs=t1, outputs=[label1, score1])
-
-    with gr.Tab("Paraphrasing & Grammar Correction"):
-        t2 = gr.Textbox(lines=5, label='Enter text for paraphrasing and grammar correction')
-        button2 = gr.Button("🔄 Paraphrase and Correct")
-        ginger_button = gr.Button("🔧 Correct with Ginger")
-        result2 = gr.Textbox(lines=5, label='Corrected Text')
-
-        button2.click(fn=paraphrase_and_correct, inputs=t2, outputs=result2)
-        ginger_button.click(fn=correct_with_ginger, inputs=t2, outputs=result2)
-
-demo.launch(share=True)
+    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()