import torch import re import gradio as gr from transformers import AutoTokenizer, ViTFeatureExtractor, VisionEncoderDecoderModel device='cpu' encoder_checkpoint = "nlpconnect/vit-gpt2-image-captioning" decoder_checkpoint = "nlpconnect/vit-gpt2-image-captioning" model_checkpoint = "nlpconnect/vit-gpt2-image-captioning" feature_extractor = ViTFeatureExtractor.from_pretrained(encoder_checkpoint) tokenizer = AutoTokenizer.from_pretrained(decoder_checkpoint) model = VisionEncoderDecoderModel.from_pretrained(model_checkpoint).to(device) def predict(image,max_length=64, num_beams=4): image = image.convert('RGB') image = feature_extractor(image, return_tensors="pt").pixel_values.to(device) clean_text = lambda x: x.replace('<|endoftext|>','').split('\n')[0] caption_ids = model.generate(image, max_length = max_length)[0] caption_text = clean_text(tokenizer.decode(caption_ids)) return caption_text input = gr.inputs.Image(label="Upload any Image", type = 'pil', optional=True) output = gr.outputs.Textbox(type="auto",label="Captions") examples = [f"example{i}.jpg" for i in range(1,7)] title = "Image Captioning " description = "Made by : shreyasdixit.tech" interface = gr.Interface( fn=predict, description=description, inputs = input, theme="grass", outputs=output, examples = examples, title=title, ) interface.launch(debug=True)