Update app.py

app.py

@@ -2,7 +2,6 @@ import gradio as gr
 import cv2
 import easyocr
 from PIL import Image
-import numpy as np
 
 # Functions for OCR steps
 def get_grayscale(image):
@@ -18,25 +17,22 @@ def ocr_with_easy(img):
     return bounds
 
 def process_image(img, steps):
-    gray_image = None
     for step in steps:
         if step == "Grayscale Conversion":
             img = get_grayscale(img)
-            gray_image = img
         elif step == "Thresholding":
             img = thresholding(img)
     cv2.imwrite('processed_image.png', img)
-    return 'processed_image.png', gray_image
+    return 'processed_image.png'
 
 def generate_ocr(img, steps):
     text_output = ''
-    gray_image = None
     if img is not None and (img).any():
-        processed_image_path, gray_image = process_image(img, steps)
+        processed_image_path = process_image(img, steps)
         text_output = ocr_with_easy(processed_image_path)
     else:
         raise gr.Error("Please upload an image and select the processing steps!")
-    return text_output, gray_image
+    return text_output
 
 # Interactive tutorial steps
 tutorial_steps = [
@@ -118,13 +114,12 @@ Please upload an image and select the correct order of steps to perform OCR.
 image = gr.Image()
 steps = gr.CheckboxGroup(choices=tutorial_steps, label="Select and order the steps for OCR")
 output = gr.Textbox(label="OCR Output")
-gray_image_output = gr.Image(label="Grayscale Image", type="numpy")
 explanation = gr.Markdown(explanation_text)
 
 ocr_app = gr.Interface(
     fn=generate_ocr,
     inputs=[image, steps],
-    outputs=[output, gray_image_output],
+    outputs=output,
     title="Optical Character Recognition",
     description=explanation_text,
     css=".gradio-container {background-color: lightgray} #radio_div {background-color: #FFD8B4; font-size: 40px;}"