app.py

import spaces
import transformers
import re
import torch
import gradio as gr
import os
import ctranslate2
from concurrent.futures import ThreadPoolExecutor

# Define the device
device = "cuda" if torch.cuda.is_available() else "cpu"

# Load CTranslate2 model and tokenizer
model_path = "ocronos_CT2"
generator = ctranslate2.Generator(model_path, device=device)
tokenizer = transformers.AutoTokenizer.from_pretrained("PleIAs/OCRonos-Vintage")

# CSS for formatting (unchanged)
css = """
<style>
... (your existing CSS)
</style>
"""

# Helper functions
def generate_html_diff(old_text, new_text):
    # (unchanged)
    ...

def preprocess_text(text):
    # (unchanged)
    ...

def split_text(text, max_tokens=400):
    encoded = tokenizer.encode(text)
    splits = []
    for i in range(0, len(encoded), max_tokens):
        split = encoded[i:i+max_tokens]
        splits.append(tokenizer.decode(split))
    return splits

# Function to generate text using CTranslate2
def ocr_correction(prompt, max_new_tokens=600):
    splits = split_text(prompt, max_tokens=400)
    corrected_splits = []

    for split in splits:
        full_prompt = f"### Text ###\n{split}\n\n\n### Correction ###\n"
        encoded = tokenizer.encode(full_prompt)
        prompt_tokens = tokenizer.convert_ids_to_tokens(encoded)

        result = generator.generate_batch(
            [prompt_tokens],
            max_length=max_new_tokens,
            sampling_temperature=0.7,
            sampling_topk=20,
            include_prompt_in_result=False
        )[0]

        corrected_text = tokenizer.decode(result.sequences_ids[0])
        corrected_splits.append(corrected_text)

    return " ".join(corrected_splits)

# OCR Correction Class
class OCRCorrector:
    def __init__(self, system_prompt="Le dialogue suivant est une conversation"):
        self.system_prompt = system_prompt

    def correct(self, user_message):
        generated_text = ocr_correction(user_message)
        html_diff = generate_html_diff(user_message, generated_text)
        return generated_text, html_diff

# Combined Processing Class
class TextProcessor:
    def __init__(self):
        self.ocr_corrector = OCRCorrector()

    @spaces.GPU(duration=120)
    def process(self, user_message):
        # OCR Correction
        corrected_text, html_diff = self.ocr_corrector.correct(user_message)
        
        # Combine results
        ocr_result = f'<h2 style="text-align:center">OCR Correction</h2>\n<div class="generation">{html_diff}</div>'
        
        final_output = f"{css}{ocr_result}"
        return final_output

# Create the TextProcessor instance
text_processor = TextProcessor()

# Define the Gradio interface
with gr.Blocks(theme='JohnSmith9982/small_and_pretty') as demo:
    gr.HTML("""<h1 style="text-align:center">Vintage OCR corrector</h1>""")
    text_input = gr.Textbox(label="Your (bad?) text", type="text", lines=5)
    process_button = gr.Button("Process Text")
    text_output = gr.HTML(label="Processed text")
    process_button.click(text_processor.process, inputs=text_input, outputs=[text_output])

if __name__ == "__main__":
    demo.queue().launch()