Create app.py

app.py

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+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()