app.py

import gradio as gr
import numpy as np
from PIL import Image
from transformers import AutoProcessor, BlipForConditionalGeneration

processor = # write your code here
model = # write your code here

def caption_image(input_image: np.ndarray):
    # Convert numpy array to PIL Image and convert to RGB
    raw_image = Image.fromarray(input_image).convert('RGB')
    
    # Process the image
    

    # Generate a caption for the image


    # Decode the generated tokens to text and store it into `caption`
    

    return caption

iface = gr.Interface(
    fn=caption_image, 
    inputs=gr.Image(), 
    outputs="text",
    title="Image Captioning",
    description="This is a simple web app for generating captions for images using a trained model."
)

iface = gr.Interface(
    fn=caption_image, 
    inputs=gr.Image(), 
    outputs="text",
    title="Image Captioning",
    description="This is a simple web app for generating captions for images using a trained model."
)