code error added

app.py

@@ -12,7 +12,7 @@ iface = gr.Interface(fn=process_images,
                      inputs=[gr.Image(type='filepath', label='Main Image where object identify', width="60%", height="60%"),
                              gr.Image(type='filepath', label='Object Image which placed on Main Image (PNG file only RGBA Channel)', image_mode="RGBA", width="60%", height="60%"),
                              gr.Dropdown(options_list, label='Replace Object Name (Default = chair)')],
-                     outputs=gr.Image(type='numpy', label='Final Result', width="70%", height="70%"),
+                     outputs=gr.Image(type='numpy', label='Final Result', width="60%", height="60%"),
                      title="AI Based Image Processing",
                      description="Object to Object Replacement (Note: due to limitation of free usage on this server task will take approx 3-5 minutes for process, But the actual speed of this process on pc or dedicated server is < 10 seconds)")
 

only_gradio_server.py

@@ -13,6 +13,7 @@ from utils.tools import get_config
 import torch.nn.functional as F
 from iopaint.single_processing import batch_inpaint_cv2
 from pathlib import Path
+import gradio as gr
 
 # set current working directory cache instead of default
 os.environ["TORCH_HOME"] = "./pretrained-model"
@@ -49,43 +50,24 @@ def resize_image(input_image_path, width=640, height=640):
         return im
 
     except Exception as e:
-        print(f"Error resizing image: {e}")
-        return None  # Or handle differently as needed
+        raise gr.Error("Error in resizing image!")
 
 
-def load_weights(path, device):
-    model_weights = torch.load(path)
-    return {
-        k: v.to(device)
-        for k, v in model_weights.items()
-    }
-
-
-# Function to convert image to base64
-def convert_image_to_base64(image):
-    # Convert image to bytes
-    _, buffer = cv2.imencode('.png', image)
-    # Convert bytes to base64
-    image_base64 = base64.b64encode(buffer).decode('utf-8')
-    return image_base64
-
-
-def convert_to_base64(image):
-    # Read the image file as binary data
-    image_data = image.read()
-    # Encode the binary data as base64
-    base64_encoded = base64.b64encode(image_data).decode('utf-8')
-    return base64_encoded
+def process_images(input_image, append_image, default_class="chair"):
+    if not input_image:
+        raise gr.Error("Please upload a main image.")
 
-def convert_to_base64_file(image):
-    # Convert the image to binary data
-    image_data = cv2.imencode('.png', image)[1].tobytes()
-    # Encode the binary data as base64
-    base64_encoded = base64.b64encode(image_data).decode('utf-8')
-    return base64_encoded
+    if not append_image:
+        raise gr.Error("Please upload an object image.")
 
+    # Check if the append_image is a PNG file with RGBA mode
+    try:
+        with Image.open(append_image) as img:
+            if img.format != 'PNG' or img.mode != 'RGBA':
+                raise gr.Error("Please upload a valid PNG file with RGBA mode for the object image.")
+    except Exception as e:
+        raise gr.Error("Failed to validate object image: Upload new image")
 
-def process_images(input_image, append_image, default_class="chair"):
     # Static paths
     config_path = Path('configs/config.yaml')
     model_path = Path('pretrained-model/torch_model.p')
@@ -94,7 +76,7 @@ def process_images(input_image, append_image, default_class="chair"):
     img = resize_image(input_image)
 
     if img is None:
-        return {'error': 'Failed to decode resized image'}, 419
+        raise gr.Error("Failed to decode resized image!")
 
     H, W, _ = img.shape
     x_point = 0
@@ -139,13 +121,13 @@ def process_images(input_image, append_image, default_class="chair"):
                 resized_mask = cv2.resize(dilated_mask, (img.shape[1], img.shape[0]))
 
                 # call repainting and merge function
-                output_base64 = repaitingAndMerge(append_image,str(model_path), str(config_path),width, height, x_point, y_point, img, resized_mask)
-                # Return the output base64 image in the API response
-                return output_base64
+                output_numpy = repaitingAndMerge(append_image,str(model_path), str(config_path),width, height, x_point, y_point, img, resized_mask)
+                # Return the output numpy image in the API response
+                return output_numpy
 
     # return class not found in prediction
     if not class_found:
-        return {'message': f'{default_class} object not found in the image'}, 200
+        raise gr.Error(f'{default_class} object not found in the image')
 
 def repaitingAndMerge(append_image_path, model_path, config_path, width, height, xposition, yposition, input_base, mask_base):
     config = get_config(config_path)
@@ -160,10 +142,8 @@ def repaitingAndMerge(append_image_path, model_path, config_path, width, height,
     final_image = Image.fromarray(inpaint_result_np)
 
     print("merge start")
-
     # Load the append image using cv2.imread
     append_image = cv2.imread(append_image_path, cv2.IMREAD_UNCHANGED)
-    cv2.imwrite('appneded-image.png',append_image)
     # Resize the append image while preserving transparency
     resized_image = cv2.resize(append_image, (width, height), interpolation=cv2.INTER_AREA)
     # Convert the resized image to RGBA format (assuming it's in BGRA format)