app.py

import gradio as gr
import pandas as pd
from datasets import load_dataset 
import transformers
from transformers import AutoProcessor
from transformers import AutoModelForCausalLM
import torch
from torch.utils.data import Dataset, DataLoader
from torchvision.transforms import Resize
import os
from PIL import Image
saved_folder_path = "sudeep-007/saved_model"
processor = AutoProcessor.from_pretrained(saved_folder_path)
model = AutoModelForCausalLM.from_pretrained(saved_folder_path)
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
interface = gr.Interface(
    fn=generate_caption,
    inputs=gr.Image(),
    outputs=gr.Textbox(),
    live=True
)
interface.queue()
interface.launch(share=True)