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()