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| from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer | |
| import torch | |
| from PIL import Image | |
| import streamlit as st | |
| st.title('Image Caption Generator for Listed - 1') | |
| uploaded_file = st.file_uploader('Choose an image', type=['jpg', 'jpeg', 'png']) | |
| num_return_sequences = st.number_input('Number of captions to generate', min_value=1, max_value=10, value=1) | |
| model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning") | |
| feature_extractor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning") | |
| tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning") | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model.to(device) | |
| max_length = 16 | |
| num_beams = 4 | |
| gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "num_return_sequences": num_return_sequences} | |
| def predict_step(image_paths): | |
| images = [] | |
| for image_path in image_paths: | |
| i_image = Image.open(image_path) | |
| if i_image.mode != "RGB": | |
| i_image = i_image.convert(mode="RGB") | |
| images.append(i_image) | |
| pixel_values = feature_extractor(images=images, return_tensors="pt").pixel_values | |
| pixel_values = pixel_values.to(device) | |
| 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 | |
| if uploaded_file is None: | |
| st.write("Please upload an image.") | |
| else: | |
| captions = predict_step([uploaded_file]) | |
| st.write(captions) |