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| import gradio as gr | |
| from transformers import pipeline | |
| import spacy | |
| from textblob import TextBlob | |
| from gradio_client import Client | |
| import re | |
| # Initialize models | |
| nlp = spacy.load("en_core_web_sm") | |
| spell_checker = pipeline("text2text-generation", model="oliverguhr/spelling-correction-english-base") | |
| def preprocess_capitalization(text: str) -> str: | |
| """Preprocess input text to handle capitalization rules.""" | |
| words = text.split(" ") | |
| processed_words = [] | |
| for word in words: | |
| # Check if the word is an acronym (all uppercase letters) | |
| if re.match(r"^[A-Z]+$", word): | |
| processed_words.append(word) # Leave acronyms unchanged | |
| # Check if the word has mixed capitalization (e.g., "HEllo") | |
| elif re.search(r"[A-Z]", word) and re.search(r"[a-z]", word): | |
| processed_words.append(word[0].upper() + word[1:].lower()) # Correct capitalization | |
| else: | |
| processed_words.append(word) # Leave other words unchanged | |
| return " ".join(processed_words) | |
| def preprocess_text(text: str): | |
| """Process text and return corrections with position information.""" | |
| result = { | |
| "spell_suggestions": [], | |
| "entities": [], | |
| "tags": [] | |
| } | |
| # Apply capitalization preprocessing | |
| capitalized_text = preprocess_capitalization(text) | |
| if capitalized_text != text: | |
| result["spell_suggestions"].append({ | |
| "original": text, | |
| "corrected": capitalized_text | |
| }) | |
| text = capitalized_text # Update text for further processing | |
| # Find and record positions of corrections | |
| doc = nlp(text) | |
| # TextBlob spell check with position tracking | |
| blob = TextBlob(text) | |
| corrected = str(blob.correct()) | |
| if corrected != text: | |
| result["spell_suggestions"].append({ | |
| "original": text, | |
| "corrected": corrected | |
| }) | |
| # Transformer spell check | |
| spell_checked = spell_checker(text, max_length=512)[0]['generated_text'] | |
| if spell_checked != text and spell_checked != corrected: | |
| result["spell_suggestions"].append({ | |
| "original": text, | |
| "corrected": spell_checked | |
| }) | |
| # Add entities and tags | |
| result["entities"] = [{"text": ent.text, "label": ent.label_} for ent in doc.ents] | |
| result["tags"] = [token.text for token in doc if token.text.startswith(('#', '@'))] | |
| return text, result | |
| def preprocess_and_forward(text: str): | |
| """Process text and forward to translation service.""" | |
| original_text, preprocessing_result = preprocess_text(text) | |
| # Forward original text to translation service | |
| client = Client("Frenchizer/space_17") | |
| try: | |
| translation = client.predict(original_text) | |
| return translation, preprocessing_result | |
| except Exception as e: | |
| return f"Error: {str(e)}", preprocessing_result | |
| # Gradio interface | |
| with gr.Blocks() as demo: | |
| input_text = gr.Textbox(label="Input Text") | |
| output_text = gr.Textbox(label="Output Text") | |
| preprocess_button = gr.Button("Process") | |
| preprocess_button.click(fn=preprocess_and_forward, inputs=[input_text], outputs=[output_text]) | |
| if __name__ == "__main__": | |
| demo.launch() |