| import streamlit as st | |
| import torch | |
| from transformers import YolosImageProcessor, YolosForObjectDetection, pipeline | |
| from PIL import Image | |
| import requests | |
| st.title("Welcome to 🇷🇺 Translator App!🪆") | |
| input = st.text_area("Your input here! 🇬🇧") | |
| pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-ru-en") | |
| st.write(pipe(input)[0]['translation_text']) | |
| # url = st.text_area("Put your URL here") | |
| # if url: | |
| # image = Image.open(requests.get(url, stream=True).raw) | |
| # st.image(image) | |
| # model = YolosForObjectDetection.from_pretrained('hustvl/yolos-tiny') | |
| # image_processor = YolosImageProcessor.from_pretrained("hustvl/yolos-tiny") | |
| # inputs = image_processor(images=image, return_tensors="pt") | |
| # outputs = model(**inputs) | |
| # # model predicts bounding boxes and corresponding COCO classes | |
| # logits = outputs.logits | |
| # bboxes = outputs.pred_boxes | |
| # st.image(bboxes) | |
| # # print results | |
| # target_sizes = torch.tensor([image.size[::-1]]) | |
| # results = image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[0] | |
| # for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): | |
| # box = [round(i, 2) for i in box.tolist()] | |
| # st.write( | |
| # f"Detected {model.config.id2label[label.item()]} with confidence " | |
| # f"{round(score.item(), 3)} at location {box}" | |
| # ) |