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import os
import torch
import re
import gradio as gr
from pathlib import Path
from transformers import AutoTokenizer, AutoFeatureExtractor, VisionEncoderDecoderModel
# Pattern to ignore all the text after 2 or more full stops
regex_pattern = "[.]{2,}"
def post_process(text):
try:
text = text.strip()
text = re.split(regex_pattern, text)[0]
except Exception as e:
print(e)
pass
return text
def set_example_image(example: list) -> dict:
return gr.Image.update(value=example[0])
def predict(image, max_length=64, num_beams=4):
pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values
pixel_values = pixel_values.to(device)
with torch.no_grad():
output_ids = model.generate(
pixel_values,
max_length=max_length,
num_beams=num_beams,
return_dict_in_generate=True,
).sequences
preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
pred = post_process(preds[0])
return pred
model_name_or_path = "deepklarity/poster2plot"
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Load model.
model = VisionEncoderDecoderModel.from_pretrained(model_name_or_path)
model.to(device)
print("Loaded model")
feature_extractor = AutoFeatureExtractor.from_pretrained(model.encoder.name_or_path)
print("Loaded feature_extractor")
tokenizer = AutoTokenizer.from_pretrained(model.decoder.name_or_path, use_fast=True)
if model.decoder.name_or_path == "gpt2":
tokenizer.pad_token = tokenizer.eos_token
print("Loaded tokenizer")
title = "Poster2Plot: Upload a Movie/T.V show poster to generate a plot"
description = ""
input = gr.inputs.Image(type="pil")
example_images = sorted(
[f.as_posix() for f in Path("examples").glob("*.jpg")]
)
print(f"Loaded {len(example_images)} example images")
demo = gr.Blocks()
filenames = next(os.walk('examples'), (None, None, []))[2]
examples = [[f"examples/{filename}"] for filename in filenames]
print(examples)
with demo:
with gr.Column():
with gr.Row():
with gr.Column():
input_image = gr.Image()
with gr.Row():
clear_button = gr.Button(value="Clear", variant='secondary')
submit_button = gr.Button(value="Submit", variant='primary')
with gr.Column():
plot = gr.Textbox()
with gr.Row():
example_images = gr.Dataset(components=[input_image], samples=examples)
submit_button.click(fn=predict, inputs=[input_image], outputs=[plot])
example_images.click(fn=set_example_image, inputs=[example_images], outputs=example_images.components)
demo.launch()
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