app.py

import torch
import re
import gradio as gr
from pathlib import Path
from transformers import AutoTokenizer, AutoFeatureExtractor, VisionEncoderDecoderModel

def predict(image, max_length=64, num_beams=4):
    image = image.convert('RGB')
    pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values
    pixel_values = pixel_values.to(device)
    with torch.no_grad():
        text = tokenizer.decode(model.generate(pixel_values.cpu())[0])
        text = text.replace('<|endoftext|>', '').split('\n')
    return text[0]
    
model_path = "team-indain-image-caption/hindi-image-captioning"
device = "cpu"
# Load model.
model = VisionEncoderDecoderModel.from_pretrained(model_path)
model.to(device)
print("Loaded model")
feature_extractor = AutoFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k")
print("Loaded feature_extractor")
tokenizer = AutoTokenizer.from_pretrained(model_path)
print("Loaded tokenizer")
title = "Hindi Image Captioning"
description = ""

input = gr.inputs.Image(label="Image to search", type = 'pil', optional=False)
output = gr.outputs.Textbox(type="auto",label="Captions")

article = "This HuggingFace Space presents a demo for Image captioning in Hindi built with VIT Encoder and GPT2 Decoder"

example = ["./examples/example_{i}.jpg" for i in range(1,6)]

interface = gr.Interface(
        fn=predict,
        inputs = input,
        theme="grass",
        outputs=output,
        title=title,
        description=article,
    )
interface.launch(share = True)