app.py

import gradio as gr
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image
import numpy as np
import torch

# Load the primary OCR model (DeepDiveDev/transformodocs-ocr)
processor1 = TrOCRProcessor.from_pretrained("DeepDiveDev/transformodocs-ocr")
model1 = VisionEncoderDecoderModel.from_pretrained("DeepDiveDev/transformodocs-ocr")

# Load the fallback model (microsoft/trocr-base-handwritten) for handwritten text
processor2 = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
model2 = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten")

# Function to extract text from handwritten images
def extract_text(image):
    try:
        # Ensure input is a PIL Image
        if isinstance(image, np.ndarray):
            if len(image.shape) == 2:  # Grayscale (H, W) -> Convert to RGB
                image = np.stack([image] * 3, axis=-1)
            image = Image.fromarray(image)
        elif isinstance(image, str):  # If file path is given, open the image
            image = Image.open(image).convert("RGB")

        # Maintain aspect ratio while resizing (better for OCR)
        image.thumbnail((800, 800))  

        # Process image with the first model
        pixel_values = processor1(images=image, return_tensors="pt").pixel_values.to(torch.float32)
        generated_ids = model1.generate(pixel_values)
        extracted_text = processor1.batch_decode(generated_ids, skip_special_tokens=True)[0]

        # If output is short or incorrect, use the fallback model
        if len(extracted_text.strip()) < 2:  
            inputs = processor2(images=image, return_tensors="pt").pixel_values.to(torch.float32)
            generated_ids = model2.generate(inputs)
            extracted_text = processor2.batch_decode(generated_ids, skip_special_tokens=True)[0]

        return extracted_text if extracted_text else "No text detected."

    except Exception as e:
        return f"Error: {str(e)}"

# Gradio UI for OCR Extraction
iface = gr.Interface(
    fn=extract_text,
    inputs=gr.Image(type="pil"),  # Ensures input is a PIL image
    outputs="text",
    title="Handwritten OCR Extraction",
    description="Upload a handwritten image to extract text using AI OCR.",
)

iface.launch()