app.py

import gradio as gr
import streamlit as st
import torch 
import re 
from transformers import AutoTokenizer, ViTFeatureExtractor, VisionEncoderDecoderModel 

# def greet(name):
#     return "Hello " + name + "!!"

# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
# iface.launch()

device='cpu'
encoder_checkpoint = "ydshieh/vit-gpt2-coco-en"
decoder_checkpoint = "ydshieh/vit-gpt2-coco-en"
model_checkpoint = "ydshieh/vit-gpt2-coco-eng"
feature_extractor = ViTFeatureExtractor.from_pretrained(encoder_checkpoint)
tokenizer = AutoTokenizer.from_pretrained(decoder_checkpoint)
model = VisionEncoderDecoderModel.from_pretrained(model_checkpoint).to(device)


def predict(image,max_length=64, num_beams=4):
    input_image = Image.open(image)
    model.eval()
    pixel_values = feature_extractor(images=[input_image], return_tensors="pt").pixel_values
    with torch.no_grad():
        output_ids = model.generate(pixel_values, max_length=16, num_beams=4, return_dict_in_generate=True).sequences
    preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
    preds = [pred.strip() for pred in preds]  
    return preds[0]
    
  # image = image.convert('RGB')
  # image = feature_extractor(image, return_tensors="pt").pixel_values.to(device)
  # clean_text = lambda x: x.replace('<|endoftext|>','').split('\n')[0]
  # caption_ids = model.generate(image, max_length = max_length)[0]
  # caption_text = clean_text(tokenizer.decode(caption_ids))
  # return caption_text 

# st.title("Image to Text using Lora")

inputs = gr.inputs.Image(label="Upload any Image", type = 'pil', optional=True)
output = gr.outputs.Textbox(type="text",label="Captions")
description = "NTT Data Bilbao team"
title = "Image to Text using Lora"

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