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import torch
import re
import gradio as gr
from transformers import AutoTokenizer, ViTFeatureExtractor, VisionEncoderDecoderModel
from transformers import AutoProcessor, AutoTokenizer, BlipForConditionalGeneration
from huggingface_hub import hf_hub_download
device='cpu'
encoder_checkpoint = "nlpconnect/vit-gpt2-image-captioning"
decoder_checkpoint = "nlpconnect/vit-gpt2-image-captioning"
model_checkpoint = "nlpconnect/vit-gpt2-image-captioning"
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):
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))
#caption_text2 = generate_captions(image)
return caption_text
blip_processor_large = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
blip_model_large = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")
blip_model_large.to(device)
def generate_caption(processor, model, image, tokenizer=None, use_float_16=False):
inputs = processor(images=image, return_tensors="pt").to(device)
if use_float_16:
inputs = inputs.to(torch.float16)
generated_ids = model.generate(pixel_values=inputs.pixel_values, max_length=50)
if tokenizer is not None:
generated_caption = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
else:
generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
return generated_caption
def generate_captions(image):
caption_blip_large = generate_caption(blip_processor_large, blip_model_large, image)
return caption_blip_large
input = gr.inputs.Image(label="Upload your Image", type = 'pil', optional=True)
#Two output boxes
output_1 = gr.outputs.Textbox(type="text",label="Caption - 1")
examples = [f"example{i}.png" for i in range(1,4)]
description= "Image caption Generator"
title = "Deep Learning and AI Intern Assignment for Listed Inc"
article = "Created By : Sravanth Kurmala"
interface = gr.Interface(
fn=predict,
inputs = input,
theme="grass",
outputs = output_1,
examples = examples,
title=title,
description=description,
article = article,
)
interface.launch(debug=True) |