Create app.py

app.py

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+from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
+from PIL import Image
+import torch
+import gradio as gr
+
+# Load your model and tokenizer
+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")
+
+# Function to generate captions from images
+def generate_caption(image):
+    # Preprocess the image
+    pixel_values = processor(images=image, return_tensors="pt").pixel_values
+
+    # Generate captions
+    output_ids = model.generate(pixel_values, max_length=16, num_beams=4, return_dict_in_generate=True).sequences
+    caption = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+    return caption
+
+# Create a Gradio Interface
+interface = gr.Interface(
+    fn=generate_caption,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Textbox(),
+    title="Image Caption Generator",
+    description="Upload an image and click 'Generate' to get a caption."
+)
+
+# Launch the app in Hugging Face Spaces
+interface.launch()