Update app.py

app.py

@@ -1,11 +1,40 @@
-# Added more redundant/filler words
+import os
+import gradio as gr
+from transformers import pipeline
+import spacy
+import nltk
+from nltk.corpus import wordnet
+from spellchecker import SpellChecker
+import re
+import inflect
+
+# Initialize components
+try:
+    nlp = spacy.load("en_core_web_sm")
+except OSError:
+    print("Downloading spaCy model...")
+    spacy.cli.download("en_core_web_sm")
+    nlp = spacy.load("en_core_web_sm")
+
+# Initialize the spell checker
+spell = SpellChecker()
+
+# Initialize the inflect engine for pluralization
+inflect_engine = inflect.engine()
+
+# Ensure necessary NLTK data is downloaded
+nltk.download('wordnet', quiet=True)
+nltk.download('omw-1.4', quiet=True)
+
+# Function to remove redundant/filler words
 def remove_redundant_words(text):
     doc = nlp(text)
-    meaningless_words = {"actually", "basically", "literally", "really", "very", "just", "quite", "rather", "simply", "that", "kind of", "sort of", "you know", "honestly", "seriously"}
+    meaningless_words = {"actually", "basically", "literally", "really", "very", "just", "quite", "rather", "simply", 
+                         "that", "kind of", "sort of", "you know", "honestly", "seriously"}
     filtered_text = [token.text for token in doc if token.text.lower() not in meaningless_words]
     return ' '.join(filtered_text)
 
-# Capitalize sentences and proper nouns
+# Function to capitalize sentences and proper nouns
 def capitalize_sentences_and_nouns(text):
     doc = nlp(text)
     corrected_text = []
@@ -19,7 +48,7 @@ def capitalize_sentences_and_nouns(text):
         corrected_text.append(' '.join(sentence))
     return ' '.join(corrected_text)
 
-# Function to dynamically correct tenses and verb forms
+# Function to correct verb tenses
 def correct_tense_errors(text):
     doc = nlp(text)
     corrected_text = []
@@ -31,7 +60,7 @@ def correct_tense_errors(text):
             corrected_text.append(token.text)
     return ' '.join(corrected_text)
 
-# Enhanced function to handle subject-verb agreement
+# Function to ensure subject-verb agreement
 def ensure_subject_verb_agreement(text):
     doc = nlp(text)
     corrected_text = []
@@ -47,19 +76,93 @@ def ensure_subject_verb_agreement(text):
             corrected_text.append(token.text)
     return ' '.join(corrected_text)
 
-# Ensure proper apostrophe usage and possessives
+# Function to correct apostrophe usage
 def correct_apostrophes(text):
     text = re.sub(r"\b(\w+)s\b(?<!\'s)", r"\1's", text)  # Simple apostrophe correction
     text = re.sub(r"\b(\w+)s'\b", r"\1s'", text)         # Handles plural possessives
     return text
 
-# Enhanced punctuation
+# Function to enhance punctuation usage
 def enhance_punctuation(text):
     text = re.sub(r'\s+([?.!,";:])', r'\1', text)         # Remove extra space before punctuation
     text = re.sub(r'([?.!,";:])(\S)', r'\1 \2', text)     # Add space after punctuation if needed
+    text = re.sub(r'\s*"\s*', '" ', text).strip()         # Clean up spaces around quotes
+    text = re.sub(r'([.!?])\s*([a-z])', lambda m: m.group(1) + ' ' + m.group(2).upper(), text)
+    text = re.sub(r'([a-z])\s+([A-Z])', r'\1. \2', text)  # Ensure sentences start with capitalized words
     return text
 
-# Paraphrasing using synonyms and correcting semantic errors
+# Function to correct semantic errors and replace with more appropriate words
+def correct_semantic_errors(text):
+    semantic_corrections = {
+        "animate_being": "animal",
+        "little": "smallest",
+        "big": "largest",
+        "mammalian": "mammals",
+        "universe": "world",
+        "manner": "ways",
+        "continue": "preserve",
+        "dirt": "soil",
+        "wellness": "health",
+        "modulate": "regulate",
+        "clime": "climate",
+        "function": "role",
+        "keeping": "maintaining",
+        "lend": "contribute",
+        "better": "improve",
+        "cardinal": "key",
+        "expeditiously": "efficiently",
+        "marauder": "predator",
+        "quarry": "prey",
+        "forestalling": "preventing",
+        "bend": "turn",
+        "works": "plant",
+        "croping": "grazing",
+        "flora": "vegetation",
+        "dynamical": "dynamic",
+        "alteration": "change",
+        "add-on": "addition",
+        "indispensable": "essential",
+        "nutrient": "food",
+        "harvest": "crops",
+        "pollenateing": "pollinating",
+        "divers": "diverse",
+        "beginning": "source",
+        "homo": "humans",
+        "fall_in": "collapse",
+        "takeing": "leading",
+        "coinage": "species",
+        "trust": "rely",
+        "angleworm": "earthworm",
+        "interrupt": "break",
+        "affair": "matter",
+        "air_out": "aerate",
+        "alimentary": "nutrient",
+        "distributeed": "spread",
+        "country": "areas",
+        "reconstruct": "restore",
+        "debauched": "degraded",
+        "giant": "whales",
+        "organic_structure": "bodies",
+        "decease": "die",
+        "carcase": "carcasses",
+        "pin_downing": "trapping",
+        "cut_downs": "reduces",
+        "ambiance": "atmosphere",
+        "extenuateing": "mitigating",
+        "decision": "conclusion",
+        "doing": "making",
+        "prolongs": "sustains",
+        "home_ground": "habitats",
+        "continueing": "preserving",
+        "populateing": "living",
+        "beingness": "beings"
+    }
+    
+    words = text.split()
+    corrected_words = [semantic_corrections.get(word.lower(), word) for word in words]
+    return ' '.join(corrected_words)
+
+# Function to rephrase using synonyms and adjust verb forms
 def rephrase_with_synonyms(text):
     doc = nlp(text)
     rephrased_text = []
@@ -88,26 +191,39 @@ def rephrase_with_synonyms(text):
 
     return ' '.join(rephrased_text)
 
-# Comprehensive text correction
+# Function to apply enhanced spell check
+def enhanced_spell_check(text):
+    words = text.split()
+    corrected_words = []
+    for word in words:
+        if '_' in word:
+            sub_words = word.split('_')
+            corrected_sub_words = [spell.correction(w) or w for w in sub_words]
+            corrected_words.append('_'.join(corrected_sub_words))
+        else:
+            corrected_word = spell.correction(word) or word
+            corrected_words.append(corrected_word)
+    return ' '.join(corrected_words)
+
+# Comprehensive function to correct the entire text
 def paraphrase_and_correct(text):
     text = enhanced_spell_check(text)
     text = remove_redundant_words(text)
     text = capitalize_sentences_and_nouns(text)
     text = correct_tense_errors(text)
-    text = correct_singular_plural_errors(text)
-    text = correct_article_errors(text)
+    text = ensure_subject_verb_agreement(text)
     text = enhance_punctuation(text)
     text = correct_apostrophes(text)
+    text = correct_semantic_errors(text)
     text = rephrase_with_synonyms(text)
-    text = correct_double_negatives(text)
-    text = ensure_subject_verb_agreement(text)
     return text
 
-# Integrate with Gradio UI
+# Gradio interface function
 def gradio_interface(text):
     corrected_text = paraphrase_and_correct(text)
     return corrected_text
 
+# Setting up Gradio interface
 iface = gr.Interface(
     fn=gradio_interface,
     inputs=gr.Textbox(lines=5, placeholder="Enter text here..."),
@@ -115,5 +231,6 @@ iface = gr.Interface(
     title="Grammar & Semantic Error Correction",
 )
 
+# Run the Gradio interface
 if __name__ == "__main__":
     iface.launch()