Update app.py

app.py

@@ -2,16 +2,12 @@ import cv2
 import numpy as np
 import gradio as gr
 
-def compare_images(original_image_path, adjusted_image_path):
-    # Read images using OpenCV
-    original_image = cv2.imread(original_image_path)
-    adjusted_image = cv2.imread(adjusted_image_path)
-
-    # Check if images loaded correctly
+def compare_images(original_image, adjusted_image):
+    # Check if images are loaded correctly
     if original_image is None or adjusted_image is None:
-        return "Error: One or both images could not be loaded."
+        return "Error: One or both images could not be loaded.", None
 
-    # Resize the adjusted image to match the dimensions of the original for accurate comparison
+    # Resize adjusted image to match original image dimensions
     original_image_resized = cv2.resize(original_image, (adjusted_image.shape[1], adjusted_image.shape[0]))
 
     # Step 1: Compare Dimensions
@@ -23,23 +19,19 @@ def compare_images(original_image_path, adjusted_image_path):
     # Convert images to grayscale
     original_gray = cv2.cvtColor(original_image_resized, cv2.COLOR_BGR2GRAY)
     adjusted_gray = cv2.cvtColor(adjusted_image, cv2.COLOR_BGR2GRAY)
-    
+
     # Calculate average brightness
     original_brightness = np.mean(original_gray)
     adjusted_brightness = np.mean(adjusted_gray)
     brightness_diff = abs(original_brightness - adjusted_brightness)
-    brightness_match = brightness_diff < 10  # Allowing small tolerance
+    brightness_match = brightness_diff < 10  # Allow small tolerance
 
     # Step 3: Highlight Differences
     # Subtract images to find differences
     difference = cv2.absdiff(original_gray, adjusted_gray)
     _, threshold_diff = cv2.threshold(difference, 30, 255, cv2.THRESH_BINARY)
-    
-    # Save the difference image
-    difference_image_path = "difference_image.jpg"
-    cv2.imwrite(difference_image_path, threshold_diff)
 
-    # Prepare results summary
+    # Prepare result summary
     comparison_result = {
         "Dimension Match": "Yes" if dimension_match else "No",
         "Original Dimensions": f"{original_width}x{original_height}",
@@ -48,40 +40,24 @@ def compare_images(original_image_path, adjusted_image_path):
         "Original Brightness": f"{original_brightness:.2f}",
         "Adjusted Brightness": f"{adjusted_brightness:.2f}",
         "Brightness Difference": f"{brightness_diff:.2f}",
-        "Difference Image": difference_image_path
     }
 
     return comparison_result, threshold_diff
 
 # Gradio interface
-def gradio_compare_images(original_image, adjusted_image):
-    # Save images temporarily
-    original_image_path = "original_image.jpg"
-    adjusted_image_path = "adjusted_image.jpg"
-    
-    cv2.imwrite(original_image_path, original_image)
-    cv2.imwrite(adjusted_image_path, adjusted_image)
-    
-    # Compare images
-    result, diff_image = compare_images(original_image_path, adjusted_image_path)
-    
-    return result, diff_image
-
-# Create Gradio interface
 iface = gr.Interface(
-    fn=gradio_compare_images,
+    fn=compare_images,
     inputs=[
-        gr.inputs.Image(type="numpy", label="Original Image"),
-        gr.inputs.Image(type="numpy", label="Adjusted Image")
+        gr.Image(type="numpy", label="Original Image"),
+        gr.Image(type="numpy", label="Adjusted Image")
     ],
     outputs=[
-        gr.outputs.JSON(label="Comparison Result"),
-        gr.outputs.Image(type="numpy", label="Difference Image")
+        gr.JSON(label="Comparison Result"),
+        gr.Image(type="numpy", label="Difference Image")
     ],
     title="Image Comparison Tool",
     description="Upload two images to compare dimensions, brightness, and highlight differences."
 )
 
-# Launch the Gradio app
 iface.launch()