app.py

import gradio as gr
import os
from transformers import AutoProcessor, AutoModelForImageTextToText

# Load model and processor
processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = AutoModelForImageTextToText.from_pretrained("Salesforce/blip-image-captioning-base")

def caption(image):
    # 'image' is now a PIL Image object because of type="pil"
    inputs = processor(image, return_tensors="pt")
    out = model.generate(**inputs)
    return processor.decode(out[0], skip_special_tokens=True)

demo = gr.Interface(
    fn=caption,
    inputs=gr.Image(label="Upload Image", type="pil"), 
    outputs="text"
)

demo.launch()