app.py

import gradio as gr
from PIL import Image
processor = AutoProcessor.from_pretrained(daliavanilla/BLIP-Radiology-model)
model = BlipForConditionalGeneration.from_pretrained(daliavanilla/BLIP-Radiology-model)

# Define the prediction function
def generate_caption(image):
    # Process the image
    image = Image.fromarray(image)
    #inputs = tokenizer(image, return_tensors="pt")
    inputs = processor(images=image, return_tensors="pt")#.to(device)
    pixel_values = inputs.pixel_values

    # Generate caption
    generated_ids = model.generate(pixel_values=pixel_values, max_length=50)
    generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

    return generated_caption

# Define the Gradio interface
interface = gr.Interface(
    fn=generate_caption,
    inputs=gr.Image(),
    outputs=gr.Textbox(),
    live=True
)

# Launch the Gradio interface
interface.launch()