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import gradio as gr
from model_architecture import ImageCaptionGenerationWithAttention
from transformers import BartForConditionalGeneration, BartTokenizer, ViTModel, ViTImageProcessor
import torch
from PIL import Image
from dotenv import load_dotenv
import os
import traceback
load_dotenv()
HF_TOKEN = os.getenv('hf_token')
class GenerateCaptions:
def __init__(self):
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
vit_model = ViTModel.from_pretrained(
"google/vit-base-patch16-224", token=HF_TOKEN).to(self.device)
bart_model = BartForConditionalGeneration.from_pretrained(
"facebook/bart-base").to(self.device)
self.processor = ViTImageProcessor.from_pretrained(
"google/vit-base-patch16-224")
self.tokenizer = BartTokenizer.from_pretrained("facebook/bart-base")
self.model = ImageCaptionGenerationWithAttention(
vit_model, bart_model, self.tokenizer)
self.model.load_state_dict(torch.load(
'image_captioning_model_state_dict.pt', map_location=self.device))
self.model.eval()
def generate_caption(self, frame, max_length=50, num_beams=5):
try:
image_pixel_values = self.processor(
frame, return_tensors="pt").pixel_values
generated_caption_ids = self.model.generate(
image_pixel_values, max_length, num_beams)
return self.tokenizer.decode(generated_caption_ids[0], skip_special_tokens=True)
except Exception as e:
print(e)
print(traceback.format_exc())
gc = GenerateCaptions()
demo = gr.Interface(
fn=gc.generate_caption,
inputs=gr.Image(type='pil'),
outputs="text",
title="Image Caption Generation",
examples=['Image.jpg', 'Image 2.jpg'],
submit_btn='Generate Caption',
flagging_mode='never'
)
demo.launch()
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