typo_check.py

#!/usr/bin/env python
# Gradio app for Dhivehi typo correction

import difflib
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
import gradio as gr
import spaces

# Available models
MODEL_OPTIONS_TYPO = {
    "A3 Model": "alakxender/t5-dhivehi-typo-corrector-asr",
    "XS Model": "alakxender/dhivehi-quick-spell-check-t5"
}

# Function to load model and tokenizer
def load_model(model_choice):
    print("Loading model and tokenizer...")
    try:
        selected_model = MODEL_OPTIONS_TYPO[model_choice]

        tokenizer = AutoTokenizer.from_pretrained(selected_model)
        if tokenizer.pad_token is None:
            tokenizer.pad_token = tokenizer.eos_token
        
        model = AutoModelForSeq2SeqLM.from_pretrained(selected_model)
        
        # Move model to GPU if available
        device = "cuda" if torch.cuda.is_available() else "cpu"
        model = model.to(device)
        
        print(f"Model loaded successfully on {device}")
        return model, tokenizer, device
    except Exception as e:
        print(f"Error loading model: {e}")
        return None, None, None

# Function to correct typos (reverted to single output)
def correct_typo(text, model, tokenizer, device):
    if not text.strip():
        #return "Please enter some text."
        raise gr.Error("Please enter some text💥!", duration=5)


    if len(text.strip()) > 1024:
        #return "Shorter the better."
        raise gr.Error("Shorter the better💥!", duration=5)

    
    try:
        # Prepare input with prefix
        input_text = "fix: " + text
        
        # Tokenize input
        inputs = tokenizer(input_text, return_tensors="pt", max_length=128, truncation=True)
        inputs = inputs.to(device)
        
        # Generate output
        with torch.no_grad():
            outputs = model.generate(
                input_ids=inputs["input_ids"],
                attention_mask=inputs.get("attention_mask", None),
                max_length=128,
                num_beams=4,
                early_stopping=True
            )
        
        # Decode the output
        corrected_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
        
        return corrected_text
    except Exception as e:
        return f"Error: {str(e)}"

# Initialize model and tokenizer
model, tokenizer, device = load_model("A3 Model")

if model is None:
    print("Failed to load model. Please check your model and tokenizer paths.")

# Function to highlight differences between original and corrected text
def highlight_differences(original, corrected):
    
    d = difflib.Differ()
    orig_words = original.split()
    corr_words = corrected.split()
    
    diff = list(d.compare(orig_words, corr_words))
    
    html_parts = []
    i = 0
    while i < len(diff):
        if diff[i].startswith('  '):  # Unchanged
            html_parts.append(f'<span>{diff[i][2:]}</span>')
        elif diff[i].startswith('- '):  # Removed
            if i + 1 < len(diff) and diff[i + 1].startswith('+ '):
                # Changed word - show correction
                old_word = diff[i][2:]
                new_word = diff[i + 1][2:]
                html_parts.append(f'<span style="background-color: #fff3cd">{old_word}</span>→<span style="background-color: #d4edda">{new_word}</span>')
                i += 1
            else:
                # Removed word
                html_parts.append(f'<span style="background-color: #f8d7da">{diff[i][2:]}</span>')
        elif diff[i].startswith('+ '):  # Added
            html_parts.append(f'<span style="background-color: #d4edda">{diff[i][2:]}</span>')
        i += 1
    
    return f'<div class="dhivehi-diff">{" ".join(html_parts)}</div>'

# Function to process the input for Gradio
@spaces.GPU()
def process_input(text,model_choice):
    if model is None:
        load_model(model_choice)
    
    corrected = correct_typo(text, model, tokenizer, device)
    highlighted = highlight_differences(text, corrected)
    return corrected, highlighted

# Define CSS for Dhivehi font styling
css = """
.textbox1 textarea {
    font-size: 18px !important;
    font-family: 'MV_Faseyha', 'Faruma', 'A_Faruma' !important;
    line-height: 1.8 !important;
    direction: rtl !important;
}

.dhivehi-text {
    font-size: 18px !important;
    font-family: 'MV_Faseyha', 'Faruma', 'A_Faruma' !important;
    line-height: 1.8 !important;
    direction: rtl !important;
    text-align: right !important;
    padding: 10px !important;
    background: transparent !important;  /* Make background transparent */
    border-radius: 4px !important;
    color: #ffffff !important;  /* White text for dark background */
}

/* Style for the highlighted differences */
.dhivehi-diff {
    font-size: 18px !important;
    font-family: 'MV_Faseyha', 'Faruma', 'A_Faruma' !important;
    line-height: 1.8 !important;
    direction: rtl !important;
    text-align: right !important;
    padding: 15px !important;
    background: transparent !important;  /* Make background transparent */
    border: 1px solid rgba(255, 255, 255, 0.1) !important;  /* Subtle border */
    border-radius: 4px !important;
    margin-top: 10px !important;
    color: #ffffff !important;  /* White text for dark background */
}

/* Ensure the highlighted spans have good contrast */
.dhivehi-diff span {
    padding: 2px 5px !important;
    border-radius: 3px !important;
    margin: 0 2px !important;
}

/* Original text (yellow background) */
.dhivehi-diff span[style*="background-color: #fff3cd"] {
    background-color: rgba(255, 243, 205, 0.2) !important;
    color: #ffd700 !important;  /* Golden yellow for visibility */
    border: 1px solid rgba(255, 243, 205, 0.3) !important;
}

/* Corrected text (green background) */
.dhivehi-diff span[style*="background-color: #d4edda"] {
    background-color: rgba(212, 237, 218, 0.2) !important;
    color: #98ff98 !important;  /* Light green for visibility */
    border: 1px solid rgba(212, 237, 218, 0.3) !important;
}

/* Removed text (red background) */
.dhivehi-diff span[style*="background-color: #f8d7da"] {
    background-color: rgba(248, 215, 218, 0.2) !important;
    color: #ff6b6b !important;  /* Light red for visibility */
    border: 1px solid rgba(248, 215, 218, 0.3) !important;
}

/* Arrow color */
.dhivehi-diff span:contains('→') {
    color: #ffffff !important;
}
"""