app.py

import numpy as np
import random
from PIL import Image
from ram.models import tag2text
from ram import inference_tag2text as inference
from ram import get_transform
import gradio as gr
import torch
import torchvision.transforms as transforms
from PIL import Image

device = torch.device('cpu')
model = tag2text(pretrained='tag2text.pth', image_size=384, vit='swin_b')
model.threshold = 0.68
model.eval()

def tag_image(input_image):
    transform = get_transform(image_size=384) 

    if isinstance(input_image, Image.Image):
        img = input_image
    else:
        # Convert Gradio Image datatype (NumPy array) to PIL Image
        img = Image.fromarray(input_image)

    # Process the image
    print(f"Start processing, image size {img.size}")
    image = transform(img).unsqueeze(0)
    # Generate Tags and Captions
    res = inference(image, model)
    tags = res[0].strip(' ').replace('  ', ' ')
    caption = res[2]
    print(tags, caption)
    return tags, caption

# Interface for the demo
inputs = gr.inputs.Image()
outputs = [gr.outputs.Textbox(label='Tags'), gr.outputs.Textbox(label='Caption')]

# Launch the Gradio app
gr.Interface(
    fn=tag_image,
    inputs=inputs,
    outputs=outputs,
    title="Tags and Captioning using Tag2Text",
    description="Upload an image and see the tags and captions in the output boxes",
    live=True,  
    
).launch(share=True, enable_queue=True, debug=True)