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# import gradio as gr
# import streamlit as st
# import torch
# import re
# from transformers import AutoTokenizer, ViTFeatureExtractor, VisionEncoderDecoderModel
# 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)
import torch
import re
import gradio as gr
from transformers import AutoTokenizer, ViTFeatureExtractor, VisionEncoderDecoderModel
device='cpu'
encoder_checkpoint = "nlpconnect/vit-gpt2-image-captioning"
decoder_checkpoint = "nlpconnect/vit-gpt2-image-captioning"
model_checkpoint = "nlpconnect/vit-gpt2-image-captioning"
print("------------------------- 1 -------------------------\n")
feature_extractor = ViTFeatureExtractor.from_pretrained(encoder_checkpoint)
print("------------------------- 2 -------------------------\n")
tokenizer = AutoTokenizer.from_pretrained(decoder_checkpoint
print("------------------------- 3 -------------------------\n")
model = VisionEncoderDecoderModel.from_pretrained(model_checkpoint).to(device)
print("------------------------- 4 -------------------------\n")
def predict(image,max_length=64, num_beams=4):
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
print("------------------------- 5 -------------------------\n")
input = gr.inputs.Image(label="Upload any Image", type = 'pil', optional=True)
output = gr.outputs.Textbox(type="auto",label="Captions")
examples = [f"example{i}.jpg" for i in range(1,7)]
print("------------------------- 6 -------------------------\n")
title = "Image Captioning "
description = "NTT Data"
interface = gr.Interface(
fn=predict,
description=description,
inputs = input,
theme="grass",
outputs=output,
examples = examples,
title=title,
)
interface.launch(debug=True)
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