app.py

import streamlit as st
from PIL import Image
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
import torch

# Load the OCR model and processor (switching to a larger model)
model_name = "microsoft/trocr-large-stage1"  # You can try this larger model for better accuracy
processor = TrOCRProcessor.from_pretrained(model_name)
model = VisionEncoderDecoderModel.from_pretrained(model_name)

# Streamlit app title
st.title("OCR with TrOCR (Improved Accuracy)")

# Upload image section
uploaded_image = st.file_uploader("Upload an image for OCR", type=["jpg", "jpeg", "png"])

if uploaded_image is not None:
    # Open and display the uploaded image
    image = Image.open(uploaded_image).convert("RGB")  # Ensure image is in RGB format
    st.image(image, caption="Uploaded Image", use_column_width=True)

    # Resize the image to improve OCR accuracy
    resized_image = image.resize((224, 224))  # Resize to a standard resolution
    
    # Convert image to a suitable format and ensure it's a batch (list of images)
    try:
        # Convert image to the right format for the processor
        inputs = processor(images=[resized_image], return_tensors="pt")  # Put image in a list
        
        # Perform OCR
        with torch.no_grad():
            outputs = model.generate(**inputs)

        # Decode the generated text
        text = processor.decode(outputs[0], skip_special_tokens=True)
        
        # Display the OCR result
        st.write("Extracted Text:")
        st.text(text)
    except Exception as e:
        st.error(f"An error occurred: {str(e)}")