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(type="pil")
#example_images = sorted([f.as_posix() for f in Path("examples").glob("*.jpg")])
#print(f"Loaded {len(example_images)} example images")
article = "This huggingface presents a demo for Image captioning in Hindi built with VIT Encoder and GPT2 Decoder"
'''interface = gr.Interface(
    fn=predict,
    inputs=input,
    outputs="textbox",
    title=title,
    description=description,
    #examples=example_images,
    live=True,
    theme="darkpeach"
  
)'''

#inp=gr.inputs.Textbox(lines=1, placeholder=None, default="", label="search you query here")
output = gr.outputs.Textbox(type="auto",label="Captions")

interface = gr.Interface(fn=predict, inputs=input, outputs=output,examples=exp,article=article,title=title,theme="huggingface",layout='vertical')
interface.launch(share=True)