Spaces:
Runtime error
Runtime error
File size: 1,966 Bytes
f527373 3c059bd f527373 8a55d9d e2a98c2 f527373 1c31448 f527373 6cb2eec d3d8066 3ed339a a79b50e d3d8066 8a55d9d b5bd9ee d3d8066 5da6813 d3d8066 c313dd0 5da6813 d3d8066 5da6813 f527373 c0996a6 05eb182 c313dd0 05eb182 2869667 f094fcd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 |
import ast
import os
import requests
from PIL import Image
import streamlit as st
import io
api = "https://23d4-188-130-155-153.ngrok-free.app"
st.set_page_config(layout="wide")
st.title("Stamp2vec 📄")
input_image = st.file_uploader("insert image")
if(input_image):
image = Image.open(input_image)
st.header("Original")
st.image(input_image, width = 700)
detection_model = st.selectbox("Select detection model", ("YOLO-stamp", ))
embedding_model = st.selectbox("Select embedding model", ("vits8", ))
if st.button("Get prediction"):
with st.spinner("Loading..."):
response = requests.post(os.path.join(api, f"bounding-boxes-{detection_model}/"),
files = {"file": input_image.getvalue(), "model_id": detection_model})
prediction = ast.literal_eval(response.text)
response = requests.post(os.path.join(api, f"image-w-boxes-{detection_model}/"),
files = {"file": input_image.getvalue(), "model_id": detection_model})
image_with_boxes = response.content
arr = []
for b in prediction["bboxes"]:
stamp = image.crop((b["xmin"], b["ymin"], b["xmin"] + b["width"], b["ymin"] + b["height"]))
output = io.BytesIO()
stamp.save(output, format="BMP")
response = ast.literal_eval(requests.post(os.path.join(api, f"embeddings-from-cropped-{embedding_model}/"),
files = {"file": output.getvalue(), "model_id": embedding_model}).text)
arr.extend(response["embedding"])
col1, col2, col3 = st.columns(3)
col1.subheader("Prediction")
col1.write(prediction)
col2.subheader("Image")
col2.image(image_with_boxes, use_column_width=True)
col3.subheader("Embeddings")
col3.write(arr) |