Update app.py

app.py

@@ -2,22 +2,21 @@ import pandas as pd
 import numpy as np
 import streamlit as st
 import easyocr
-import PIL
 from PIL import Image, ImageDraw
 from streamlit_drawable_canvas import st_canvas
 
 def rectangle(image, result):
-    """Draw rectangles on image based on predicted coordinates."""
+    """Draw rectangles on the image based on predicted coordinates."""
     draw = ImageDraw.Draw(image)
     for res in result:
-        top_left = tuple(res[0][0])  # top left coordinates as tuple
-        bottom_right = tuple(res[0][2])  # bottom right coordinates as tuple
+        top_left = tuple(res[0][0])  # top left coordinates as a tuple
+        bottom_right = tuple(res[0][2])  # bottom right coordinates as a tuple
         draw.rectangle((top_left, bottom_right), outline="blue", width=2)
-    # Display image on streamlit
+    # Display the image on Streamlit
     st.image(image)
 
 # Main title
-st.title("Get text from image with EasyOCR")
+st.title("Get text from an image with EasyOCR")
 
 # Subtitle
 st.markdown("## EasyOCR with Streamlit")
@@ -32,7 +31,7 @@ with col1:
 with col2:
     # Drawable canvas
     canvas_result = st_canvas(
-        fill_color="rgba(255, 165, 0, 0.3)", 
+        fill_color="rgba(255, 165, 0, 0.3)",
         stroke_width=3,
         stroke_color="#ffffff",
         background_color="#000000",
@@ -44,36 +43,34 @@ with col2:
         key="canvas",
     )
 
+image = None  # Initialize image variable
+
 # Process uploaded image or drawing
 if file is not None:
     image = Image.open(file)  # Read image with PIL library
-if canvas_result.image_data is not None:
-    temp_image = Image.fromarray(canvas_result.image_data.astype('uint8'), 'RGBA').convert('RGB')
-    temp_image.save("temp_canvas_image.jpg")
-    st.image(temp_image)  # Display to confirm
-    image = temp_image
-else:
-    st.write("Please upload an image or use the canvas to draw.")
-    image = None
+elif canvas_result.image_data is not None:
+    image = Image.fromarray(np.array(canvas_result.image_data, dtype=np.uint8)).convert('RGB')
 
 if image is not None:
-    st.image(image)  # Display
+    st.image(image)  # Display the image
 
-    # Only detect the English and Turkish part of the image as text
-    reader = easyocr.Reader(['en','ja'], gpu=False) 
-    result = reader.readtext(np.array(image))  # Turn image to numpy array
+    # Initialize EasyOCR reader; you can add or remove languages based on your preference
+    reader = easyocr.Reader(['en', 'ja'], gpu=False)
+    result = reader.readtext(np.array(image))  # Convert image to numpy array and process with EasyOCR
 
-    # Print all predicted text:
+    # Print all predicted text
     for idx, res in enumerate(result):
         pred_text = res[1]
         st.write(pred_text)
     
-    # Collect the results in the dictionary:
+    # Collect the results in a dictionary
     textdic_easyocr = {res[1]: {'pred_confidence': res[2]} for res in result}
 
-    # Create a data frame which shows the predicted text and prediction confidence
+    # Create a DataFrame to show the predicted text and prediction confidence
     df = pd.DataFrame.from_dict(textdic_easyocr).T
     st.table(df)
 
-    # Get boxes on the image 
+    # Draw rectangles around the detected text in the image
     rectangle(image, result)
+else:
+    st.write("Please upload an image or use the canvas to draw.")