Create requirements.txt

requirements.txt

@@ -1,21 +1,5 @@
-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)
+streamlit
+transformers
+torch
+torchvision
+Pillow