Delete paragraph_checker.py

paragraph_checker.py

@@ -1,62 +0,0 @@
-import language_tool_python
-from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
-import torch
-
-class ParagraphCorrector:
-    def __init__(self):
-        """Initialize correction models with conservative settings"""
-        # Grammar tool with increased timeout
-        self.grammar_tool = language_tool_python.LanguageTool(
-            'en-US',
-            config={'maxTextLength': 100000}
-        )
-
-        # Conservative grammar correction model
-        self.grammar_model = pipeline(
-            "text2text-generation",
-            model="vennify/t5-base-grammar-correction",
-            device=0 if torch.cuda.is_available() else -1
-        )
-
-    def correct_sentence(self, sentence: str) -> str:
-        """Correct a single sentence conservatively"""
-        # Basic grammar/spelling correction
-        matches = self.grammar_tool.check(sentence)
-        corrected = language_tool_python.utils.correct(sentence, matches)
-
-        # Light neural correction
-        result = self.grammar_model(
-            corrected,
-            max_length=256,
-            num_beams=3,
-            temperature=0.3,  # Low temperature for minimal changes
-            early_stopping=True
-        )
-        return result[0]['generated_text']
-
-    def conservative_correction(self, text: str) -> str:
-        """Process text while preserving original structure"""
-        if not text.strip():
-            return text
-
-        # Split into sentences while preserving delimiters
-        sentences = []
-        current = ""
-        for char in text:
-            current += char
-            if char in {'.', '!', '?'}:
-                sentences.append(current)
-                current = ""
-        if current:
-            sentences.append(current)
-
-        # Correct each sentence individually
-        corrected_sentences = []
-        for sentence in sentences:
-            if sentence.strip():
-                corrected = self.correct_sentence(sentence)
-                corrected_sentences.append(corrected)
-            else:
-                corrected_sentences.append(sentence)
-
-        return ''.join(corrected_sentences)