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| import torch | |
| import gradio as gr | |
| from PIL import Image | |
| from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer | |
| def main(): | |
| model = VisionEncoderDecoderModel.from_pretrained("OmkarShidore/scene-caption") | |
| feature_extractor = ViTImageProcessor.from_pretrained("OmkarShidore/scene-caption") | |
| tokenizer = AutoTokenizer.from_pretrained("OmkarShidore/scene-caption") | |
| max_length = 16 | |
| num_beams = 4 | |
| gen_kwargs = {"max_length": max_length, "num_beams": num_beams} | |
| def predict(image): | |
| #image = Image.open(image_path) | |
| image = image.convert(mode="RGB") | |
| pixel_values = feature_extractor(images=[image], return_tensors="pt").pixel_values | |
| pixel_values = pixel_values.to(device="cpu") | |
| output_ids = model.generate(pixel_values, **gen_kwargs) | |
| preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True) | |
| preds = [pred.strip() for pred in preds] | |
| return preds[0] | |
| #built interface with gradio to test the function | |
| imagein = gr.components.Image(label='Scene Image', type='pil') | |
| output = gr.components.Textbox() | |
| gui = gr.Interface(fn=predict, inputs=imagein, outputs=[output]) | |
| gr.Interface(fn=predict, | |
| inputs=imagein, | |
| outputs=output, | |
| title='Image To Text- Scene Description', | |
| description="<html> <body> <h3>Hugging Face: <a href='https://huggingface.co/OmkarShidore/scene-caption'>OmkarShidore/scene-caption</a></h3><h3>Git: <a href='https://github.com/OmkarShidore/ImageToText-SceneDescription'>OmkarShidore/ImageToText-SceneDescription</a></h3> </body></html>", | |
| examples=["./data/car.jpg", "./data/gsd.jpg", "./data/highway.jpg"], | |
| theme=gr.themes.Default() | |
| ).launch() | |
| if __name__ == '__main__': | |
| main() |