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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(label="Image to search", type = 'pil', optional=False) | |
output = gr.outputs.Textbox(type="auto",label="Captions") | |
#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,article=article,title=title,theme="huggingface",layout='vertical') | |
interface.launch(share=True)''' | |
cat_image = "./example_1.jpg" | |
dog_image = "./example_2.jpg" | |
interface = gr.Interface( | |
fn=predict, | |
inputs = input, | |
theme="grass", | |
outputs=output, | |
title=title, | |
description=article, | |
) | |
interface.launch(share = True) |