ok

app.py

@@ -2,24 +2,26 @@ import gradio as gr
 import tensorflow as tf
 import numpy as np
 from tensorflow.keras.preprocessing import image
+from PIL import Image
 
 # Load the trained model
-model = tf.keras.models.load_model("face_mask_model.h5")  # Replace with your model file
+model = tf.keras.models.load_model("face_mask_model.h5")  # Ensure this file exists
 
 # Class labels
 labels = ["With Mask", "Without Mask"]
 
 # Function to predict mask
 def predict(img):
-    img = img.resize((224, 224))  # Resize image to model input size
+    img = img.convert("RGB")  # Ensure RGB format
+    img = img.resize((224, 224))  # Resize for model input
     img_array = image.img_to_array(img) / 255.0  # Normalize
     img_array = np.expand_dims(img_array, axis=0)
-    
+
     prediction = model.predict(img_array)
     return labels[np.argmax(prediction)]  # Return class label
 
 # Create Gradio interface
-iface = gr.Interface(fn=predict, inputs="image", outputs="text")
+iface = gr.Interface(fn=predict, inputs=gr.Image(type="pil"), outputs="text")
 
 # Launch the Gradio interface
 iface.launch()