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}"
#         )