app.py

import torch
from transformers import AutoProcessor, AutoModelForVision2Seq
from PIL import Image
import requests
import matplotlib.pyplot as plt

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

# Load processor and model
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
model = AutoModelForVision2Seq.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")

def perform_ocr(image_path: str):
    # Load image
    image = Image.open(image_path).convert("RGB")
    
    # Preprocess image
    inputs = processor(images=image, return_tensors="pt").to(device)
    
    # Generate text
    with torch.no_grad():
        generated_ids = model.generate(**inputs)
    
    # Decode generated text
    extracted_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
    return extracted_text

# Example usage
if __name__ == "__main__":
    IMAGE_PATH = "Images\Hindi-to-English-sentences-translation.jpg"  # Replace with the path to your image
    
    # Perform OCR
    extracted_text = perform_ocr(IMAGE_PATH)
    
    # Display results
    print("Extracted Text:", extracted_text)
    
    # Show image
    img = Image.open(IMAGE_PATH)
    plt.imshow(img)
    plt.axis("off")
    plt.show()