Create requirements.txt

requirements.txt

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+from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
+from PIL import Image
+
+# Load the pre-trained model and processor
+model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+processor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+
+# Load an image
+image_path = "path/to/your/image.jpg"  # Update with your image path
+image = Image.open(image_path)
+
+# Process the image
+pixel_values = processor(images=image, return_tensors="pt").pixel_values
+
+# Generate text
+output_ids = model.generate(pixel_values)
+text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+# Print the extracted text
+print(text)