Update app.py

app.py

@@ -1,26 +1,25 @@
 ___all___ = ['learn', 'classify_image', 'categories', 'image', 'label', 'examples', 'intf']
 
-from tensorflow.keras.models import model_from_json, load_model
+from tensorflow.keras.models import load_model
+from PIL import Image
+import numpy as np
 import gradio as gr
-import json
 
 # Define the ETHNICITIES dictionary
 ETHNICITIES = {0: "White", 1: "Black", 2: "Asian", 3: "Indian", 4: "Hispanic"}
 
 # Load the trained model
-try:
-    # Load the entire model from the .h5 file
-    Model_L = load_model('model_L.h5')
-except Exception as e:
-    print(f"Error loading model: {e}")
+Model_L = load_model('model_L.h5')
 
 # Define the categories based on your model's output
 categories = list(ETHNICITIES.values())
 
 # Define the function to classify images
 def classify_image(img):
-    import numpy as np
-    img = np.array(img).reshape((1, *img.size, 3))  # Reshape the image to match model input shape
+    img = img.resize((48, 48))  # Resize the image to match model input shape
+    img = img.convert('L')  # Convert image to grayscale
+    img = np.array(img) / 255.0  # Normalize the image
+    img = img.reshape(-1, 48, 48, 1)  # Reshape the image to match model input shape
     pred = Model_L.predict(img)
     probs = pred[0]
     return dict(zip(categories, map(float, probs)))