poster2plot / app.py
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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 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")
interface = gr.Interface(
fn=predict,
inputs=input,
outputs="textbox",
title=title,
description=description,
examples=example_images,
examples_per_page=20,
live=True,
article='<p>Made by: <a href="https://twitter.com/kartik_godawat" target="_blank" rel="noopener noreferrer">dk-crazydiv</a> and <a href="https://twitter.com/dsr_ai" target="_blank" rel="noopener noreferrer">dsr</a></p>'
)
interface.launch()