Update app.py

app.py

@@ -482,7 +482,6 @@
 #     title="Classifier",
 # )
 # iface.launch()
-
 import gradio as gr
 import numpy as np
 import cv2 as cv
@@ -524,19 +523,11 @@ def classify(platform, UserInput, Images, Textbox2, Textbox3):
             if platform == "wh":
                 get_image = requests.get(Images, headers=headers)
                 if get_image.status_code == 200:
-                    print(get_image.content)
-                    # Correct base64 encoding and URL creation
+                    # Convert the image data to base64
                     image_base64 = base64.b64encode(get_image.content).decode("utf-8")
-                    image_data_url = f"data:image/png;base64,{image_base64}"
-
-                    # random_id = random.randint(1000, 9999)
-                    # filename = f"image_{random_id}"
-                    # with open(filename, "wb") as file:
-                    #     file.write(get_image.content)
-                    # print(f"Saved image as: {filename}")
 
-                    # full_path = os.path.join(os.getcwd(), filename)
-                    # print(f"Saved image as: {full_path}")
+                    # Create a data URL
+                    image_data_url = f"data:image/png;base64,{image_base64}"
 
             elif platform == "web":
                 print("WEB")
@@ -546,7 +537,10 @@ def classify(platform, UserInput, Images, Textbox2, Textbox3):
 
             if image_data_url is not None:
                 # Load the image from image_data_url
-                image = cv.imread(image_data_url)
+                image_data = base64.b64decode(image_base64)
+                nparr = np.frombuffer(image_data, np.uint8)
+                image = cv.imdecode(nparr, cv.IMREAD_COLOR)
+
                 image = cv.resize(image, (224, 224))
                 image_array = np.asarray(image)
                 normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1