Add application with streamlit

app.py

@@ -0,0 +1,85 @@
+"""This is a demo for running the barcode QR code reader usng streamlit library"""
+
+from dataclasses import dataclass, field
+from typing import Any, Optional
+import asyncio
+
+import streamlit as st
+from PIL import Image
+import numpy as np
+import pandas as pd
+
+from src.deep_barcode_reader.barcode import Wrapper
+
+
+@dataclass
+class DemoBarcodeReader:
+    """This is a demo class for barcode/qr code reader using different methods"""
+
+    image: Optional[Any] = field(init=False, default=None)
+    model_option: str = field(init=False, default="opencv")
+    model_size: str = field(init=False, default="n")
+
+    def upload_image(self) -> None:
+        """Upload an image from the streamlit page"""
+        uploaded_file = st.file_uploader(
+            "Choose an image...", type=["jpg", "png", "jpeg"]
+        )
+        if uploaded_file is not None:
+            self.image = Image.open(uploaded_file)
+        else:
+            self.image = Image.open("tests/test_data/sample.jpg")
+
+        st.image(
+            self.image, caption="Original/Uploaded Image", use_container_width=True
+        )
+
+    def select_model(self) -> None:
+        """Select a model for barcode/qr code reader"""
+        self.model_option = st.selectbox(
+            "Choose a reader/decoder model", ["zbar", "opencv", "qrreader"]
+        )
+        if self.model_option == "qrreader":
+            ml_size = st.selectbox(
+                "Choose a model size for QRReader method",
+                ["nano", "small", "medium", "large"],
+            )
+            self.model_size = (
+                "n"
+                if ml_size == "nano"
+                else "s" if ml_size == "small" else "m" if ml_size == "medium" else "l"
+            )
+
+    def process_image(self) -> None:
+        """Process the image for barcode/qr code reader"""
+        if st.button("Read/Decode Barcode/QR Code"):
+            reader = Wrapper(model_size=self.model_size, method=self.model_option)
+            detections, result_img = asyncio.run(
+                reader.method_selection(image=np.array(self.image), result_path="")
+            )
+            st.markdown("<h3>Detected Results</h3>", unsafe_allow_html=True)
+            st.image(result_img, caption="Decoded Result", use_container_width=True)
+            results = pd.DataFrame(
+                {
+                    "Barcode/QR Types": detections.decoded_types,
+                    "Data": detections.decoded_data,
+                    "Boundary Box": [str(bbx) for bbx in detections.bbox_data],
+                }
+            )
+            st.markdown('<div class="center-container">', unsafe_allow_html=True)
+            st.markdown(
+                "<h3>Detailed Information of Detections</h3>", unsafe_allow_html=True
+            )
+            st.table(results)
+            st.markdown("</div>", unsafe_allow_html=True)
+
+    def design_page(self) -> None:
+        """Design the streamlit page for barcode/qr code reader"""
+        st.title("Image Barcode/QR Code Reader and Detector")
+        self.upload_image()
+        self.select_model()
+        self.process_image()
+
+
+demo = DemoBarcodeReader()
+demo.design_page()