Update app.py

app.py

@@ -5,30 +5,19 @@ import gradio as gr
 import io
 
 # Load the model
-model_path = 'dentist/final_teath_classifier.h5'
+model_path = 'final_teath_classifier.h5'
 model = tf.keras.models.load_model(model_path)
-
-# Define preprocessing function
-def preprocess_image(image):
-    # Resize the image to match input size
-    image = image.resize((256, 256))
-    # Convert image to array and preprocess input
-    img_array = np.array(image) / 255.0
-    # Add batch dimension
-    img_array = np.expand_dims(img_array, axis=0)
-    return img_array
-
 # Define prediction function
 def predict_image(image):
     # Save the image to a file-like object
     image_bytes = io.BytesIO()
     image.save(image_bytes, format="JPEG")  # Change "JPG" to "JPEG"
     image_bytes.seek(0)  # Reset file pointer to start
-
     # Load the image from the file-like object
     image = tf.keras.preprocessing.image.load_img(image_bytes, target_size=(256, 256), color_mode="rgb")  # Specify color_mode="rgb"
-
-    img_array = preprocess_image(image)
+     #image = image.resize((256, 256))
+    img_array = np.array(image) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
     outputs = model.predict(img_array)
     predictions = tf.nn.softmax(outputs.logits, axis=-1)
     predicted_class_index = np.argmax(predictions)