app.py

import torch
from transformers import TrOCRProcessor, VisionEncoderDecoderModel, Trainer, TrainingArguments
from datasets import load_dataset

# Load your dataset
dataset = load_dataset('your_dataset_name')  # Replace with your dataset name

# Initialize the processor and model
processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten")

# Preprocess the data
def preprocess_data(example):
    # Process images and texts
    pixel_values = processor(images=example['image'], return_tensors="pt").pixel_values
    labels = processor(text=example['text'], return_tensors="pt").input_ids
    return {'pixel_values': pixel_values, 'labels': labels}

# Map preprocessing to the train dataset
train_dataset = dataset['train'].map(preprocess_data)

# Training arguments
training_args = TrainingArguments(
    output_dir='./results',
    per_device_train_batch_size=8,
    num_train_epochs=3,
    logging_steps=100,
    save_steps=500,
    evaluation_strategy='steps',
)

# Trainer setup
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
)

# Train the model
trainer.train()

# Save the model and processor after training
model.save_pretrained('./your_model_name')
processor.save_pretrained('./your_model_name')