Update app.py

app.py

@@ -319,6 +319,7 @@ import gradio as gr
 import numpy as np
 import cv2 as cv
 import requests
+from PIL import Image
 import os
 import tensorflow as tf
 
@@ -356,19 +357,27 @@ def classify(platform, UserInput, Image, Textbox2, Textbox3):
             if platform == "wh":
                 get_image = requests.get(Image, headers=headers)
                 if get_image.status_code == 200:
-                    print(get_image.content)
-                    imageData = cv.imdecode(np.asarray(bytearray(get_image.content), dtype="uint8"), cv.IMREAD_COLOR)
+                    # print(get_image.content)
+                    # imageData = cv.imdecode(np.asarray(bytearray(get_image.content), dtype="uint8"), cv.IMREAD_COLOR)
+                    image_bytes = get_image.content
+                    image = Image.open(io.BytesIO(image_bytes))
+                    image_data = cv.cvtColor(np.array(image), cv.COLOR_RGB2BGR)
             elif platform == "web":
                 print("WEB")
                 # Handle web case if needed
             else:
                 pass
 
-            image_data = cv.resize(imageData, (224, 224))
+            image_data = cv.resize(image_data, (224, 224))
             normalized_image_array = (image_data.astype(np.float32) / 127.0) - 1
+            data = np.zeros((1, 224, 224, 3))
             data[0] = normalized_image_array
-
             prediction = model.predict(data)
+            # image_data = cv.resize(imageData, (224, 224))
+            # normalized_image_array = (image_data.astype(np.float32) / 127.0) - 1
+            # data[0] = normalized_image_array
+
+            # prediction = model.predict(data)
 
             max_label_index = None
             max_prediction_value = -1