Update app.py

app.py

@@ -1,74 +1,61 @@
+from PIL import Image, ImageDraw
+import numpy as np
 import streamlit as st
 from streamlit_drawable_canvas import st_canvas
-import numpy as np
-from PIL import Image
 import easyocr
 import pandas as pd
 
-def process_image_for_ocr(image):
-    """Converts the image for EasyOCR and draws rectangles around detected text."""
-    # Convert PIL image to numpy array
-    image_np = np.array(image)
-    
-    # Initialize EasyOCR reader
-    reader = easyocr.Reader(['en'], gpu=False)  # Add more languages or adjust as needed
-    result = reader.readtext(image_np)
-    
-    # Draw rectangles on a copy of the image
-    draw_image = image.copy()
+def rectangle(image, result):
+    """Draw rectangles on the image based on predicted coordinates and display the image."""
+    draw_image = image.copy()  # Work on a copy of the image
     draw = ImageDraw.Draw(draw_image)
     for res in result:
         top_left = tuple(res[0][0])
         bottom_right = tuple(res[0][2])
-        draw.rectangle([top_left, bottom_right], outline="red", width=2)
-    
-    return draw_image, result
-
-def display_text_results(result):
-    """Displays text results in Streamlit."""
-    textdic_easyocr = {idx: {'Text': res[1], 'Confidence': res[2]} for idx, res in enumerate(result)}
-    df = pd.DataFrame.from_dict(textdic_easyocr, orient='index')
-    st.table(df)
+        draw.rectangle((top_left, bottom_right), outline="blue", width=2)
+    st.image(draw_image, caption="Processed Image with Detected Text Highlighted")
 
-st.title("OCR with EasyOCR and Streamlit")
-st.markdown("Upload an Image or Draw Below")
+# Main title and markdowns
+st.title("Get text from an image with EasyOCR")
+st.markdown("## EasyOCR with Streamlit")
+st.markdown("## Upload an Image or Draw")
 
 # Column layout for uploader and canvas
 col1, col2 = st.columns(2)
 
 with col1:
-    uploaded_file = st.file_uploader("Upload Here", type=['png', 'jpg', 'jpeg'])
+    file = st.file_uploader("Upload Here", type=['png', 'jpg', 'jpeg'])
 
 with col2:
-    # Use the background_image parameter if you want to draw on an uploaded image.
     canvas_result = st_canvas(
-        fill_color="rgba(255, 165, 0, 0.3)",  # Use a transparent fill color
+        fill_color="rgba(255, 165, 0, 0.3)",
         stroke_width=3,
-        stroke_color="#FFFFFF",
+        stroke_color="#ffffff",
         background_color="#000000",
+        background_image=None if file else st.session_state.get("background", None),
+        update_streamlit=True,
         width=400,
         height=400,
         drawing_mode="freedraw",
         key="canvas",
     )
 
-image = None
-
-if uploaded_file is not None:
-    image = Image.open(uploaded_file).convert('RGB')
-
-elif canvas_result.image_data is not None:
-    # Convert the canvas data to an Image
-    canvas_image = Image.fromarray((canvas_result.image_data).astype('uint8'), mode='RGBA')
-    image = canvas_image.convert('RGB')
-
-if image:
+if image is not None:
     st.image(image, caption="Uploaded/Drawn Image")
 
-    processed_image, ocr_result = process_image_for_ocr(image)
+    # Optional: Indicate that processing is happening
+    with st.spinner('Processing...'):
+        reader = easyocr.Reader(['en', 'ja'], gpu=False)  # Consider moving this outside the loop if performance is a concern
+        result = reader.readtext(np.array(image))
+
+    for idx, res in enumerate(result):
+        pred_text = res[1]
+        st.write(pred_text)
     
-    st.image(processed_image, caption="Processed Image with Detected Text")
-    display_text_results(ocr_result)
+    textdic_easyocr = {res[1]: {'pred_confidence': res[2]} for res in result}
+    df = pd.DataFrame.from_dict(textdic_easyocr, orient='index', columns=['pred_confidence'])
+    st.table(df)
 
+    rectangle(image, result)
 else:
     st.write("Please upload an image or use the canvas to draw.")