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import os
import re
import torch
import gradio as gr
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")
examples = [[f"examples/{filename}"] for filename in next(os.walk('examples'), (None, None, []))[2]]
print(f"Loaded {len(examples)} example images")
with gr.Blocks() as poster2plot:
with gr.Column():
with gr.Row():
gr.Markdown("# Poster2Plot: Upload a Movie/T.V show poster to generate a plot")
with gr.Row():
with gr.Column():
with gr.Row():
input_image = gr.Image(label='Input Image', type='numpy')
with gr.Row():
submit_button = gr.Button(value="Submit", variant='primary')
with gr.Column():
plot = gr.Textbox(label="Plot")
with gr.Row():
example_images = gr.Dataset(components=[input_image], samples=examples)
with gr.Row():
gr.Markdown("Made by: [dk-crazydiv](https://twitter.com/kartik_godawat) and [dsr](https://twitter.com/dsr_ai)")
submit_button.click(fn=predict, inputs=[input_image], outputs=[plot])
example_images.click(fn=set_example_image, inputs=[example_images], outputs=example_images.components)
poster2plot.launch()
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