ui

app.py

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+import ast
+import os
+import requests
+from requests_toolbelt.multipart.encoder import MultipartEncoder
+from PIL import Image
+import streamlit as st
+import io
+
+api = "http://127.0.0.1:8000/"
+
+st.title("Stamp2vec")
+
+input_image = st.file_uploader("insert image")
+image = Image.open(input_image)
+if(input_image):
+    st.header("Original")
+    st.image(input_image, use_column_width=True)
+    if st.button("Get prediction"):
+        col1, col2 = st.columns(2)
+        col1.subheader("Prediction")
+        response = requests.post(os.path.join(api, "bounding-boxes/"), files = {"file": input_image.getvalue()})
+        prediction = ast.literal_eval(response.text)
+        col1.write(prediction)
+
+        col2.subheader("Image")
+        response = requests.post(os.path.join(api, "image-w-boxes/"), files = {"file": input_image.getvalue()})
+        col2.image(response.content, use_column_width=True)
+
+        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, "embeddings-from-cropped/"), files = {"file": output.getvalue()}).text)
+            arr.append(response["emb"])
+
+        st.write(arr)

requirements.txt

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+requests
+requests_toolbelt
+pillow