Update app.py

app.py

@@ -12,7 +12,7 @@ import streamlit as st
 import torch
 from ultralyticsplus import YOLO, render_result
 
-from convert import convert_to_braille_unicode, parse_xywh_and_class
+from convert import convert_to_braille_unicode, parse_xywh_and_class, braille_to_text
 
 
 def load_model(model_path):
@@ -54,59 +54,63 @@ source_img = None
 source_img = st.sidebar.file_uploader(
     "Choose an image...", type=("jpg", "jpeg", "png", "bmp", "webp")
 )
-col1, col2 = st.columns(2)
-
-# left column of the page body
-with col1:
-    if source_img is None:
-        default_image_path = "./images/alpha-numeric.jpeg"
-        image = load_image(default_image_path)
-        st.image(
-            default_image_path, caption="Example Input Image", use_column_width=True
-        )
-    else:
-        image = load_image(source_img)
-        st.image(source_img, caption="Uploaded Image", use_column_width=True)
-
-# right column of the page body
-with col2:
-    with st.spinner("Wait for it..."):
-        start_time = time.time()
-    try:
-        with torch.no_grad():
-            res = model.predict(
-                image, save=True, save_txt=True, exist_ok=True, conf=conf
-            )
-            boxes = res[0].boxes  # first image
-            res_plotted = res[0].plot()[:, :, ::-1]
 
-            list_boxes = parse_xywh_and_class(boxes)
+# Single column layout - only show uploaded image
+if source_img is None:
+    default_image_path = "./images/alpha-numeric.jpeg"
+    image = load_image(default_image_path)
+    st.image(
+        default_image_path, caption="Example Input Image", use_column_width=True
+    )
+else:
+    image = load_image(source_img)
+    st.image(source_img, caption="Uploaded Image", use_column_width=True)
 
-            st.image(res_plotted, caption="Detected Image", use_column_width=True)
-            IMAGE_DOWNLOAD_PATH = f"runs/detect/predict/image0.jpg"
+# Process the image
+with st.spinner("Wait for it..."):
+    start_time = time.time()
 
-    except Exception as ex:
-        st.write("Please upload image with types of JPG, JPEG, PNG ...")
+try:
+    with torch.no_grad():
+        res = model.predict(
+            image, save=True, save_txt=True, exist_ok=True, conf=conf
+        )
+        boxes = res[0].boxes  # first image
+        list_boxes = parse_xywh_and_class(boxes)
 
+except Exception as ex:
+    st.write("Please upload image with types of JPG, JPEG, PNG ...")
 
+# Convert braille to text and display results
+detected_text_lines = []
 try:
     st.success(f"Done! Inference time: {time.time() - start_time:.2f} seconds")
-    st.subheader("Detected Braille Patterns")
+    st.subheader("Detected Text")
+    
     for box_line in list_boxes:
         str_left_to_right = ""
         box_classes = box_line[:, -1]
         for each_class in box_classes:
-            str_left_to_right += convert_to_braille_unicode(
-                model.names[int(each_class)]
-            )
-        st.write(str_left_to_right)
+            braille_unicode = convert_to_braille_unicode(model.names[int(each_class)])
+            str_left_to_right += braille_unicode
+        
+        # Convert braille unicode to actual text
+        text_line = braille_to_text(str_left_to_right)
+        detected_text_lines.append(text_line)
+        st.write(text_line)
+    
+    # Combine all detected text
+    full_detected_text = "\n".join(detected_text_lines)
+    
+    # Add copy to clipboard functionality
+    if st.button("Copy Text to Clipboard"):
+        # Use streamlit's built-in clipboard functionality
+        st.code(full_detected_text, language=None)
+        st.success("Text displayed above. Use Ctrl+A to select all, then Ctrl+C to copy!")
+    
+    # Alternative: Create a text area that users can easily copy from
+    st.subheader("Copy Text Below:")
+    st.text_area("Detected Text (Select All & Copy)", value=full_detected_text, height=150)
+    
 except Exception as ex:
-    st.write("Please try again with images with types of JPG, JPEG, PNG ...")
-
-with open(IMAGE_DOWNLOAD_PATH, "rb") as fl:
-    st.download_button(
-        "Download object-detected image",
-        data=fl,
-        file_name="image0.jpg",
-        mime="image/jpg",
-    )
+    st.write("Please try again with images with types of JPG, JPEG, PNG ...")